econometric modelling and forecasting company`s fcf components

Transcription

ISM UNIVERSITY OF MANAGEMENT AND ECONOMICS
MASTER OF SCIENCE IN FINANCIAL ECONOMICS PROGRAMME
Laura Virbukaitė
MASTER'S THESIS
ECONOMETRIC MODELLING AND FORECASTING COMPANY'S FCF COMPONENTS
Supervisor
Dr. A. Klimavičienė 2010
____________
Reviewer
2010 _
VILNIUS, 2010
__________
Modelling and Forecasting Company's FCF
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Abstract
Virbukaitė, L. Econometric Modelling and Forecasting Company's FCF Components [Manuscript]:
Master Thesis: economics. Vilnius, ISM University of Management and Economics, 2010.
The aim of the study is to verify a hypothesis, whether a company‟s financial statement items can
be modelled using econometric techniques incorporating accounting and macroeconomic variables.
For the modelling and forecasting are selected items, necessary to calculate a company‟s free cash
flow (FCF) of four Lithuanian companies: telecommunication provider TEO LT, cheese
manufacturer Rokiškio sūris, producer of household refrigerators Snaigė and distributor and supplier
of electric energy VST. From their financial statements are taken such items as operating profit,
current assets and current liabilities, long - term assets and long - term liabilities, and modeled as
endogenous variables. Two types of exogenous variables are used: accounting variables (revenues
and various types of expenditures) and macroeconomic variables (interest rates, disposable income
or net earnings, growth of gross domestic product, country‟s export, foreign direct investment and
inflation). Initial econometric analysis of the variables includes verification of seasonality and
stationarity according to the time series graphs and unit - root tests as well as correlation and
causality analysis using cross - correlation matrices and Granger causality tests. For the modelling
are selected two types of econometric methods: structural simultaneous - equations models (SEM),
estimating them using two - stage least squares technique, and vector autoregression (VAR) models.
After estimation of the models forecasts with them are carried out. The estimated SEM models turns
out to be not satisfactory according to goodness - of - fit statistics and thus are stated as not suitable
for modelling of the selected data. Forecasts with them also strongly deviate from actual data. VAR
models appear to be superior to SEM in both explanatory and forecasting capabilities. The main
reason for the improvement is assumed to be lagged values of dependent variables, which are
included into the models. The main limitation of the study is shortage of data, thus it is
recommended to perform similar research with larger samples and including more company specific
variables. Using longer time series more lags can be included into VAR models and they can be
extended incorporating error correction components and making vector error correction models
(VECM). Adjusted the gaps it is possible to use the forecasts, made with the models, practically for
budgeting and company valuation by discounted cash flow (DCF) method.
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Santrauka
Virbukaitė, L. Įmonės laisvo pinigų srauto sudedamųjų dalių ekonometrinis modeliavimas ir
prognozavimas [Rankraštis]: magistro baigiamasis darbas: ekonomika. Vilnius, ISM Vadybos ir
ekonomikos universitetas, 2010.
Darbo tikslas yra patikrinti hipotezę, ar įmonės finansinės atskaitomybės straipsniai gali būti
modeliuojami naudojant ekonometrinius metodus įtraukiant apskaitos ir makroekonominius
kintamuosius. Modeliavimui ir prognozavimui yra pasirinkti įmonės laisvam pinigų srautui (angl.
free cash flow, FCF) apskaičiuoti reikalingi straipsniai ir keturios Lietuvos įmonės:
telekomunikacijų paslaugų teikėja „TEO LT“, sūrių gamybos įmonė „Rokiškio sūris“, buitinių
šaldytuvų gamintoja „Snaigė“ bei elektros energijos skirstytoja ir tiekėja VST. Iš šių bendrovių
finansinių atskaitomybių yra paimti tokie straipsniai, kaip veiklos pelnas, trumpalaikis turtas ir
trumpalaikiai įsipareigojimai, ilgalaikis turtas ir ilgalaikiai įsipareigojimai. Šie rodikliai yra
modeliuojami kaip endogeniniai kintamieji. Modeliuojant naudojami egzogeniniai kintamieji yra
dviejų tipų: apskaitos kintamieji (pardavimai ir įvairios sąnaudos) bei makroekonominiai kintamieji
(palūkanų normos, disponuojamos pajamos, neto darbo užmokestis, bendrojo vidaus produkto
augimas, šalies eksportas, tiesioginės užsienio investicijos ir infliacija). Pradinė ekonometrinė
kintamųjų analizė apima sezoniškumo ir stacionarumo tikrinimą pagal laiko eilučių grafikus ir
vienetinės šaknies testus bei koreliacijų ir priežastingumo analizę, naudojant kryžmines koreliacijas
ir Granger priežastingumo testus. Modeliavimui yra pasirinkti du ekonometriniai metodai:
struktūrinių vienalaikių lygčių modeliai (angl. structural simultaneous – equation models, SEM),
vertinant juos dviejų žingsnių mažiausių kvadratų metodu, ir vek torinės autoregresijos (angl. vector
autoregression, VAR) modeliai. Įvertinus modelius su jais yra atliekamos prognozės. Įvertinti SEM
modeliai nėra geri pagal modelių suderinamumo kriterijus, todėl teigiama, jog jie nėra tinkami
modeliuoti pasirinktus duomenis. Šių modelių prognozės taip pat smarkiai nukrypsta nuo tikrųjų
reikšmių. VAR modeliai pranoksta SEM modelius ir paaiškinamumo ir prognozavimo prasme.
Daroma prielaida, jog pagrindinė priežastis, kodėl VAR modeliai yra geresni, yra vėluojančių
priklausomųjų kintamųjų įtraukimas į modelius. Pagrindinis darbo trūkumas yra duomenų stygius,
todėl yra rekomenduojama atlikti panašų tyrimą naudojant didesnes imtis ir įtraukiant daugiau tik
konkrečioms įmonėms būdingų kintamųjų. Naudojant ilgesnes laiko eilutes į VAR modelius galima
įtraukti daugiau vėlavimų ir praplėsti modelį naudojant paklaidų korekcijos komponentę ir sudarant
vektorinius paklaidų korekcijos modelius (angl. vector error correction model, VECM). Ištaisius
minėtus trūkumus, prognozes, gautas naudojant sudarytus modelius, būtų galima praktiškai naudoti
biudžetų sudarymui ir įmonių vertinimui diskontuotų pinigų srautų metodu.
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Table of Contents
List of Figures ............................................................................................................................................5
List of Tables .............................................................................................................................................6
List of Abbreviations .................................................................................................................................8
1. Introduction ...........................................................................................................................................9
2. Review of Literature on Application of Econometrics in Corporate Finance .....................................11
2.1. Econometric Modelling of Company‟s Financial Statements .................................................11
2.2. Econometric Modelling of Corporate Finance Data and its Factors .......................................16
3. Research Problem Definition ..............................................................................................................19
4. Methodological Approach on Econometric Corporate Finance Data Analysis and Modelling .........21
4.1. Empirical Research Aim and Research Design .......................................................................21
4.2. Variables and Data, Used to Model FCF Components ...........................................................22
4.3. Procedures of Econometric Analysis of Data..........................................................................24
4.4. Model of FCF Components Specification ...............................................................................26
4.5. Forecasting with SEM and VAR Models ................................................................................28
4.6. Choice of Software for Econometric Analysis of Data ...........................................................29
5. Empirical Research Report of FCF Components‟ Econometric Modelling ........................................30
5.1. Results of Seasonality and Stationarity Analysis ....................................................................30
5.2. Results of Correlation Analysis ...............................................................................................31
5.3. Results of Granger Causality Analysis ....................................................................................35
5.4. Results of SEM Estimation .....................................................................................................35
5.5. Results of VAR Estimation .....................................................................................................39
5.6. Forecasts of FCF Components ................................................................................................41
6. Discussion on Results of Econometric Modelling of FCF Components ............................................44
6.1. Overview of the Significant Findings of the Empirical Research and Verification of the
Thesis Hypotheses ...................................................................................................................44
6.2. Overview of the Findings in Context of the Analyzed Literature ...........................................45
6.3. Limitations of the Study ..........................................................................................................46
6.4. Implications for Further Research and Practical Application .................................................47
7. Conclusions .........................................................................................................................................49
References List.........................................................................................................................................51
Appendices...............................................................................................................................................53
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List of Figures
Figure 1 Time series graph of quarterly inflation, % ......................................................................... 30
Figure 2 Time series graph of quarterly GDP growth, % .................................................................. 30
Figure 3 Time series graphs of annual GDP growth and inflation, % ............................................... 53
Figure 4 Time series graphs of net average monthly earnings and disposable income (yearly data),
LTL .................................................................................................................................................... 53
Figure 5 Time series graphs of EURIBOR and VILIBOR, average of the year, % .......................... 53
Figure 6 Time series graphs of FDI stock and export (yearly data), ths. LTL .................................. 53
Figure 7 Time series graphs of EURIBOR and VILIBOR, average of the quarter, % ...................... 53
Figure 8 Time series graphs of net average monthly earnings (quarterly data), LTL ....................... 53
Figure 9 Time series graphs of FDI stock and export (quarterly data), ths. LTL .............................. 54
Figure 10 Time series graphs of annual TEO L-T assets, revenue and total expenses, ths. LTL...... 54
Figure 11 Time series graphs of annual TEO L-T liabilities and current liabilities, ths. LTL .......... 54
Figure 12 Time series graphs of annual TEO L-T assets and current assets, ths. LTL ..................... 54
Figure 13 Time series graphs of annual RSU COGS and revenue, ths. LTL .................................... 55
Figure 14 Time series graphs of annual RSU L-T assets and current assets, ths. LTL ..................... 55
Figure 15 Time series graphs of annual RSU L-T liabilities, operating profit and current liabilities,
ths. LTL.............................................................................................................................................. 55
Figure 16 Time series graphs of quarterly TEO current and L-T assets, ths. LTL............................ 55
Figure 17 Time series graphs of quarterly TEO current and L-T liabilities, ths. LTL ...................... 55
Figure 18 Time series graphs of quarterly TEO operating profit, total expenses and revenue, ths.
LTL .................................................................................................................................................... 56
Figure 19 Time series graphs of quarterly RSU current assets and liabilities and L-T assets, ths.
LTL .................................................................................................................................................... 56
Figure 20 Time series graphs of quarterly RSU operating profit, L-T liabilities and operating
expenses, ths. LTL ............................................................................................................................. 56
Figure 21 Time series graphs of quarterly RSU revenue and COGS, ths. LTL ................................ 56
Figure 22 Time series graphs of quarterly SNG operating expenses and operating profit, ths. LTL 57
Figure 23 Time series graphs of quarterly SNG current and L-T assets, ths. LTL ........................... 57
Figure 24 Time series graphs of quarterly SNG current and L-T liabilities, ths. LTL ...................... 57
Figure 25 Time series graphs of quarterly SNG COGS and revenue, ths. LTL ................................ 57
Figure 26 Time series graphs of quarterly VST L-T assets and liabilities, ths. LTL ........................ 57
Figure 27 Time series graphs of quarterly VST current assets and revenue, ths. LTL ..................... 57
Figure 28 Time series graphs of quarterly VST current liabilities, COGS and operating profit, ths.
LTL .................................................................................................................................................... 58
Figure 29 TEO quarterly actual and with VAR fitted data ................................................................ 67
Figure 30 RSU quarterly actual and with VAR fitted data ................................................................ 68
Figure 31 SNG quarterly actual and with VAR fitted data ................................................................ 69
Figure 32 VST quarterly actual and with VAR fitted data ................................................................ 70
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List of Tables
Table 1 Classification of quantitative non-experimental research........................................................21
Table 2 Endogenous variables and their sources ..................................................................................22
Table 3 Exogenous variables and their sources ....................................................................................23
Table 4 Information about the companies, to which models are applied .............................................24
Table 5 Results of ADF test for annual macroeconomic variables ......................................................30
Table 6 Results of ADF test for quarterly macroeconomic variables ...................................................31
Table 7 Statistically significant correlations between annual TEO accounting variables ....................32
Table 8 Statistically significant correlations between annual RSU accounting and
macroeconomic variables......................................................................................................................32
Table 9 Statistically significant correlations between quarterly TEO accounting variables ................33
Table 10 Statistically significant correlations between quarterly RSU accounting and
Table 11 Statistically significant correlations between quarterly SNG accounting variables ..............34
Table 12 Statistically significant correlations between quarterly VST accounting and
Table 13 Summary of TEO annual SEM estimation results .................................................................36
Table 14 Summary of RSU annual SEM estimation results .................................................................36
Table 15 Summary of TEO quarterly SEM estimation results .............................................................37
Table 16 Summary of RSU quarterly SEM estimation results .............................................................38
Table 17 Summary of SNG quarterly SEM estimation results .............................................................38
Table 18 Summary of VST quarterly SEM estimation results .............................................................39
Table 19 Deviation of TEO annual SEM forecasted values from the actual ones ...............................42
Table 20 Deviation of RSU annual SEM forecasted values from the actual ones ...............................42
Table 21 Deviation of TEO quarterly VAR forecasted values from the actual ones ...........................42
Table 22 Results of ADF test for annual accounting variables ............................................................59
Table 23 Results of ADF test for quarterly accounting variables.........................................................60
Table 24 Cross-correlation matrix between annual TEO accounting and macroeconomic
variables ................................................................................................................................................61
Table 25 Cross-correlation matrix between annual RSU accounting and macroeconomic
variables ................................................................................................................................................61
Table 26 Cross-correlation matrix between quarterly TEO accounting and macroeconomic
variables ................................................................................................................................................62
Table 27 Cross-correlation matrix between quarterly RSU accounting and macroeconomic
variables ................................................................................................................................................62
Table 28 Cross-correlation matrix between quarterly SNG accounting and macroeconomic
variables ................................................................................................................................................63
Table 29 Cross-correlation matrix between quarterly VST accounting and macroeconomic
variables ................................................................................................................................................63
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Table 30 Two-tailed probability levels for the significance of correlation coefficients .......................64
Table 31 Results of Granger causality test of quarterly TEO accounting and macroeconomic
variables ................................................................................................................................................65
Table 32 Results of Granger causality test of quarterly RSU accounting and macroeconomic
variables ................................................................................................................................................65
Table 33 Results of Granger causality test of quarterly SNG accounting and macroeconomic
variables ................................................................................................................................................66
Table 34 Results of Granger causality test of quarterly VST accounting and macroeconomic
variables ................................................................................................................................................66
Table 35 Deviation of TEO quarterly SEM forecasted values from the actual ones............................71
Table 36 Deviation of RSU quarterly SEM forecasted values from the actual ones............................71
Table 37 Deviation of SNG quarterly SEM forecasted values from the actual ones ...........................71
Table 38 Deviation of VST quarterly SEM forecasted values from the actual ones ............................71
Table 39 Deviation of RSU quarterly VAR forecasted values from the actual ones ...........................72
Table 40 Deviation of SNG quarterly VAR forecasted values from the actual ones ...........................72
Table 41 Deviation of VST quarterly VAR forecasted values from the actual ones............................72
List of Abbreviations
2SLS
Two-stage least squares
AB
Joint Stock Company, (lt. akcinė bendrovė)
ADF
Augmented Dickey-Fuller test
AIC
Akaike criterion
CF, DCF, FCF
Cash flow, discounted cash flow, free cash flow
COGS
Costs of goods sold
DW
Durbin-Watson statistic
EBIT
Earnings before interest and taxes
FDI
Foreign direct investment
GDP
Gross domestic product
IRF
Impulse Response Function
IV
Instrumental variables
L-T
Long term
LTL, ths. LTL
Lithuanian litas, thousand Lithuanian litas
M&A
Mergers and acquisitions
OLS
Ordinary least squares
R&D
Research and development
RSU
Rokiškio sūris AB
SEM
Simultaneous-equations model
SA
Seasonal adjustment
SC
Schwarz criterion
SNG
Snaigė AB
TEO
TEO LT AB
VAR
Vector autoregression
VST
VST AB
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1. Introduction
Financial modelling of corporate activity is an essential part of financial analysis, budgeting and
company valuation. A model, consisting of selected items from financial statements is usually
constructed and used for forecasts without any specific techniques – simply predicting potential
growth of a company‟s income according to historical trends (adjusted by overall economic
situation or intentions of management) and calculating other items, such as profit or working
capital, using historical or desired ratios. Choosing assumptions for modelling and forecasting
includes a high amount of subjectivity, caused by human factor. It could be reduced using
econometric techniques.
It is common knowledge that corporate activity is highly influenced by macroeconomic
environment and econometric methods can be useful to incorporate both endogenous financial
statement (also called accounting) variables (for example, sales, expenditures, investments,
liabilities and so forth) and exogenous macroeconomic (for example, gross domestic product
(hereinafter – GDP) growth, inflation, interest rates and so forth) ones.
Probably the first research, concerned with econometric modelling of a company‟s financial
statements was done by Saltzman in 1967. According to him, “econometric models have been
constructed for a wide variety of macro - economic systems” (p. 332), but there were almost no
applications for corporate activity. Since the time the study was prepared, only a handful of
additional research were performed (Elliott, 1973; Beedles, 1977; De Medeiros, 2005; Doornik, De
Medeiros and De Oliveira, 2009). Conclusions of all of the reports stated that econometric methods
are applicable for corporate cases. For example Saltzman (1967) said “that the results of such
empirical studies would help to cast additional light on the validity of some of the current theories
of the firm” (p. 333), while Elliott (1973) added that such type of studies “can provide (…) a
potentially useful means for predicting [a company‟s] performance” (p. 1524). These studies were
done using US, German and Brazilian companies‟ data, thus it would be worth doing similar type of
research with companies from Lithuania.
As it was mentioned before, one of the purposes of financial modelling is company valuation (not
only for companies listed on exchanges, but also for those involved in mergers and acquisitions
(hereinafter - M&A) process), which can be performed using discounted cash flow (hereinafter DCF) method. Due to this reason items necessary for calculating free cash flow (hereinafter - FCF)
are modeled in the empirical part of the Thesis. These are: operating profit, current assets and
liabilities, long - term (hereinafter – L - T) assets and liabilities.
The Proble m to solve in this research is how to model and forecast items necessary to calculate a
company‟s FCF using econometric methods. From the problem definition the main aim to verify
major hypothesis, whether financial statement items can be modelled using econometric techniques
appears.
In order to get the most valid models for selected Lithuanian companies (in aspects of the best
fitting to data and forecasting accuracy) two types of econometric methods are used. The methods
are: structural simultaneous - equations models (hereinafter - SEM) estimating those using
instrumental variables 1 (hereinafter – IV) and vector autoregression (hereinafter – VAR) model.
After this, conclusions, which of the models are the most suitable (if suitable at all), are provided.
In the Thesis secondary quantitative data are used. It includes official statistics, such as
macroeconomic indicators, taken from internet pages of various state institutions, and data from
1
The most popular of IV are two -step least squares (hereinafter - 2SLS) and three-step least squares (hereinafter 3SLS) techniques.
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companies‟ financial statements, which are collected from the internet page of exchange operator
NASDAQ OMX Vilnius and internet pages of the selected companies. The companies that provided
financial statements for the longest period of time (approximately for 10 years) – TEO, Rokiškio
sūris, Snaigė and VST – are selected.
The quantitative data analysis is performed with statistical / econometric software package EViews
and some simple calculations are done with Microsoft Excel spreadsheet application.
The Thesis is structured in the following manner. In Part 2 review and analysis of literature on the
relevant topics are provided. After discussing previous approaches on the topic, in Part 3, problem
of the Thesis is defined. Part 4 presents the selected methodological framework, including
techniques chosen and models specified. In Part 5 analytical research results are summarized and
presented. Part 6 is devoted to synthesis of literature reviewed and findings of empirical research, as
well as distinguishing limitations of the study and providing recommendations for further research;
and Part 7 is committed to summarize the work done.
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2. Review of Literature on Application of Econometrics in Corporate Finance
An application of econometric methods in corporate finance sphere is not very popular due to lack
of publicly available data and usually short time series. But there are some works devoted exactly to
the econometric modelling of company‟s financial statements incorporating macroeconomic
variables (Saltzman, 1967; Elliot, 1973; Beedles, 1977; De Medeiros, 2005; Doornik et al., 2009).
These papers are presented in section 2.1., and further research of the Thesis is based on theoretical
framework, constructed combining these works.
Other literature from the relevant area is reviewed in section 2.2.
2.1. Econometric Modelling of Company’s Financial Statements
Saltzman (1967) was probably the first, who used econometric techniques to model company‟s
financial statements. He constructed a simultaneous equation model consisting “of ten relational
equations and five definitional equations” (p. 332). Endogenous variables, such as sales, production
prices, amount of output, inventories, various costs and expenses, investments were taken from the
company‟s financial statements and other reports. The main exogenous variables, included into the
model, were wage rates, raw materials prices and determinants of external demand. All the data
used was quarterly, for nine years “allowing 35 observations for estimating purposes” (p. 339). The
author chose to analyze a large US corporation‟s subsidiary, working in oligopolistic marke t of
manufacturing and selling home laundry appliances.
In order to get better interpretable results, theoretical specification of the model was divided into
three parts:
1) Sales, prices, inventory and output;
2) Investment and expenses;
3) Cost and profit.
Regarding the first group of endogenous variables, Saltzman (1967) described the company‟s sales
as a demand function, depending on a rather large amount of factors: sales in a previous period;
deflated sales and product engineering expenses; average price of products; interest rates; total
potential market and income effects. Also in the equation dummy or so called “shift” variables,
necessary to catch the effects of seasonality 2 , were included. The production price equation was
defined using economic theory, which states that company‟s demand should be equal to its supply
in order to maximize the profits. As data of other competitors were not available, in order to meet
previously mentioned condition, one of the explanatory variables was the company‟s average
production costs. Also supply and demand influence on price was tried to capture using “the
production capabilities and inventory position of the firm” (p. 335). Besides these variables lagged
price term was included. The company‟s output and inventory equations were defined in more
simple way. The latter one was defined as function of inventory in previous period, the output and
cost of sales, while the output was said to depend on output in previous period, production costs and
inventory in previous period.
The second group of endogenous variables, investment and expenses, according to Saltzman
(1967), were even more difficult to define. The reason for problems defining expenses were that
“management‟s decisions in the area of expenses is assumed to be the current and anticipated rates
of demand for the firm‟s products in relation to the production capabilities and the inventory
2
It was said that Saltzman ‟s (1967) analyzed time series “did not initially seem to have any strong seasonal variation”
(p. 334), thus seasonal adjustment was not performed, but dummy variables were included.
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position” (p. 336) and a lot of these data were unavailable. The situation was tackled including in
the expense equation difference between the company‟s costs and inventories in previous period, as
well as lagged and current sales and operating profit. Administrative expenses were assumed to be
influenced by these expenses in previous period and by sales.
Problems with modelling investment appeared in this case, as analyzed company was a subsidiary.
Due to this reason investment decisions depended not on its own, but on its parent company.
Modelling total investment gave no satisfactory results, thus investments were divided in separate
parts: capital expenditures, product engineering expenses and manufacturing engineering
expenditures. An equation of capital expenditures was defined as a function of lagged capital
expenditures, current sales, operating profit and dummy variable for periods, when there was no
such type of investments. Other kind of investments were product engineering expenses, which
were assumed to depend on such type of investments in previous period, current sales, operating
profit, “the firm‟s percentage of the total industry sales” (p. 337) and the same dummy variable as
in capital expenditures‟ case. The author assumed manufacturing engineering expenditures to be
influenced by the lagged variable itself, sales and operating profit.
The cost and profit subsystem consisted of the company‟s manufacturing cost equation, while total
and average costs, as well as profit, were defined as identities. Manufacturing cost was assumed to
be influenced by the same cost in previous period, the company‟s sales, average price of materials
and average wage rate. Moreover, “the firm‟s efforts on behalf of cost reduction” (p. 338) were
included into the equation in terms of production engineering, manufacturing, capital and
administrative expenses divided by the firm‟s average wage rate. Identity of total costs included
manufacturing cost, sales, product engineering, manufacturing engineering, administrative and
miscellaneous expenses. The company‟s profit was defined as difference between sales and total
costs and uncontrolled expenses.
An extremely large amount of exogenous and lagged endogenous variables and time series
consisting only of 35 observations caused model overidentification problem. The equations were
estimated using ordinary least squares (hereinafter - OLS) and 2SLS methods. The compared results
showed that “for this particular sample there was not a great deal of difference in the results of these
alternative estimating procedures” (p. 332). After the estimation some variables, which signs
differed from theoretical assumptions, were dropped from the equations and the model reestimated.
Although nearly all coefficients were statistically significant and R - squared measures were
relatively good (in all cases more than 0.7), multicollinearity could be the reason of these
satisfactory measures. This problem was tried to solve by replacing some variables by others, but
the results after this procedure were not commented.
Another research on the topic was performed by Elliott in 1973. The main hypothesis the author
tried to verify was “that many aspects of corporate financial performance are jointly determined and
thus can only be reliably explained in the context of multiple - equation simultaneous model” (p.
1499). Elliott (1973) distinguished such joint relationships: sales - cash flows (hereinafter - CF), CF
- “strategic sales - generating expenditures” and sales – “strategic expenditures”. He also stated that
these, as well as other relationships between accounting variables were influenced by various
multipliers and some other macroeconomic variables.
The author defined 11 relation equations and 10 definition identities. Using the equations such
endogenous variables were modeled: real sales, production cost per dollar of real sales, marketing,
research and development (hereinafter - R&D) and capital expenditures (separately), fixed financial
charges (mainly interest payments), depreciation, general, administrative and other expenses (as one
variable), after - tax profit, inventory investment and new debt. As explanatory variables other
accounting or lagged endogenous as well as macroeconomic variables were used. The latter were
various combinations of money supply, high - employment government expenditures and yield on
corporate securities, relative price movements, and shifts of industry demand. The other accounting
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variables were: firm dividend allocation, new capital, raised issuing common or preferred shares,
stock of marketing, R&D, plant and equipment assets, CF, ratio of costs of production unit to price,
long - term debt. Also into some equations time index was an input.
The author introduced the idea that viewing marketing, R&D and capital expenditures as investment
was possible to use a “flexible accelerator mechanism3 ” (p. 1504) for further analysis. Using this
technique stocks of previously mentioned expenditures were changed to other variables, defined
using respective expenditure in previous period and desired level of it. To define desired level
functions CF was used. There the author defined CF unconventionally. According to its approach,
CF can be calculated summing up “discretionary internal funds” (p. 1505), new debt and new funds,
raised issuing common or preferred shares.
Some variables, for example, money supply and high - employment government expenditures, were
transformed using Almon - weight 4 technique with polynomial constraints, while moving averages
of other variables were used.
Specified theoretical model was estimated using data of nine US corporations for 20 years (19481968). The companies were chosen from very different types of industries, such as drug producing,
air transport, distilled beverages, building materials and heating, in order “to broaden the
significance of observed trends and patterns and to provide a rigorous test of the general
applicability of the model developed” (p. 1500).
In order to escape the overidentification problem, which appeared due to “simultaneous system
having a large number of exogenous and lagged endogenous variables relative to the number of
observations” (p. 1511), instead of 2SLS method Structurally Ordered IV was used.
Multicollinearity was eliminated constructing and including into equations first and second stage
variables as well as excluding some variables, which signs differed from economic logic.
Previously mentioned time index was included into some equations in anticipation of reducing
autocorrelation problems. Moreover, dummy variables, meaning mergers of the firms, was included
in two cases.
A lot of exogenous variables were found insignificant explaining modeled accounting variables, but
the equations were not reestimated excluding these variables. Goodness-of- fit criteria had also
shown not very good results in every equation, but surprisingly the author concluded “the model fits
the data of the individual firms quite well” (p. 1517). This conclusion was explained by rather high
R - squared (higher than 0.5) measure.
The author tested the model using it for forecasts and comparing them with so called naïve5
predictions. In nearly all cases both SEM (despite its overidentification) and IV model performed
better than naïve scenarios.
Beedles’ (1977) research is quite similar to the ones performed by Saltzman (1967) and Elliot
(1973). But Beedles (1977) himself stated the main difference from his predecessors that his work
was devoted “for the study of firms with more than one 6 objective” (p. 1217). The author‟s selected
“goals” were sales, profit and stock price and they were modeled as endogenous variables. Besides
3
The most popular usage of flexible accelerator model is concerned with investment, when net investment is defined as
difference between the desired level of capital goods and the existing level of capital goods, which are left fro m
previous period, mu ltip lied by a coefficient, representing how quickly the desired level will be reached.
4
Special technique introduced by Almon (1965) to estimate lags using polynomials.
5
The first naïve approach predicted no change in value of respective variable, while the second assumed “the change to
be the same magnitude as the previous period change” (p. 1519).
6
The author stated that usually researchers in the field of financial management or corporate fina nce set a single goal –
to maximize the market price of its common stock.
14
these goals so called “policy variables” – investment, financing and dividend decisions – were
selected. Investment decisions were split into smaller ones: working capital (as current ratio) and
fixed asset (as level of fixed assets) investment decisions. Financing variable was measured as debt
to equity ratio. Three more exogenous macroeconomic variables chosen were the Index of Industrial
Production, interest rate (measured as yield on Baa corporate bonds) and t he level of the stock
market (measured as S&P 500 index). Specifying the model “endogenous goals [were] regressed on
lagged goals, current and lagged policies, and current values of macro-variables” (p. 1223).
Three specified equations were estimated with OLS and 2SLS methods (the latter was more
appropriate as the system was overidentified ) using time series of three US companies for 45 years
(the year 1929 - 1973). One more considerable difference comparing to the other research is that the
author used relative variables, explaining that “percentage changes are not scale sensitive” (p.
1226). Moreover, according to the author, the usage of variables in relative terms is more acceptable
for managers, who want to see percentage changes and set goals in percentage terms rather in
absolute ones.
To evaluate quality of the models, estimated by OLS and 2SLS techniques, five criteria were used:
Theil‟s U statistic measure, the covariance proportion of the mean square estimation error (MSE),
the model‟s ability to predict “turning points in actual data” (p. 1227) and not to predict when there
are no such ones, and “an ex - post outside of sample simulation7 has been conducted” (p. 1227) and
compared with naïve forecasts 8 . According to these criteria, OLS and 2SLS models reflected data
better than naïve model, but the latter model performed better in out-of-sample forecasts.
Comparing OLS and 2SLS “simulations”, 2SLS gave more accurate results, thus was more
appropriate in analyzed that specific corporate finance cases.
After almost 30 years from the last research, done in the analyzed field, De Medeiros (2005)
constructed and tested “an econometric model of a firm‟s financial statements” also using SEM in
order to connect market (micro and macroeconomic) variable s with accounting ones. De Medeiros
(2005), similarly as Saltzman (1967), has chosen one firm to apply the model, but differently from
Saltzman, the firm chosen was a Brazilian monopolist producer of petrol products. The author
raised the purposes to explain “the relationships between economic and accounting variables” (p. 2)
and “to test empirically the causal relationships between variables inside the financial statements”
(p. 2) and in order to reach the goal divided the model into three parts. In the first one petroleum
market, in the second – income statement, in the third – balance sheet was modeled. From the
financial statements such variables were taken: gross revenues, total costs, net earnings, current
assets and liabilities, long - term receivables and debt, fixed assets and equity. As market variables,
demand, supply, price of oil, GDP and exchange rate were used.
In the model the author constructed seven linear equations and nine identities. As was previously
mentioned, “the model was applied to (…) a near monopolist firm in the Brazilian domestic market
for petroleum products using its annual data from 1991 to 2001 ” (p. 6). For the estimation of the
equations 2SLS method was chosen.
Before the system estimation all series were tested with Augmented Dickey - Fuller (hereinafter ADF) test for unit root, but the results were not commented. No special goodness - of - fit tests was
performed. Such simple characteristics, as R - squared and t - statistics showed the exogenous
variables explaining endogenous rather well (nearly all coefficients were found significant, signs –
as expected according to economic and accounting logic, R-squared - “satisfactory”), thus the model
was used to forecast respective financial statement items for 2002 - 2004. But the forecasted
7
The author entitles forecasts as “simulat ions”.
8
The author used such naïve approaches:”no-change and no-change-in-the-change” (p. 1227).
15
numbers were not compared to the actual ones, thus it is difficult to decide, if the model‟s
forecasting performance was good.
New approach to econometric modelling of company‟s activity was presented by Doornik et al.
(2009). They offered to use VAR – VECM (vector error correction model) model instead of
common SEM. The main motivation of such choice was that VAR – VECM‟s “forecasts are
considered superior to simultaneous equation models” (p. 2). The researchers, one of which – De
Medeiros - wrote previously discussed paper, selected to model the same Brazilian company in
order to explain the interrelationship between accounting variables and their relationship with
economic variables. From quarterly financial statements (1990 - 2006) such variables were chosen:
current assets, fixed assets, current and long - term liabilities, equity, net revenue and net income.
The accounting variables were adjusted by inflation. As exogenous economic variables were
selected country‟s interest rate, country risk, exchange rate, price of petroleum, Brazilian Wholesale
Price Index (WPI) and US Producer Price Index (PPI).
Before starting modelling specific data analysis, necessary “to support an appropriate model
specification” (p. 5) was performed. Firstly ADF test, in order to identify, whether unit roots in the
variables exist, was carried out. The series, where unit root appeared, were differenced and then
first difference of them was included into the equations. After ADF test cross - correlation matrix
was calculated and Granger causality analysis performed. This analysis showed which variables
were related or influenced each other. Later on carried out cointegration analysis “offered”
constructing VECM instead of VAR model.
The best 9 estimated VECM model had four lags, thus there were difficulties interpreting the
coefficients. This problem was solved applying impulse response function (hereinafter - IRF) “to
verify how the dependent variables respond to a shock applied to one or more system equations” (p.
14) and variance decomposition, “which decomposes the forecasted variance error for each variable
in components which may be attributed to each of the endogenous variables” (pp. 14 - 15).
Two types of forecasts were performed with selected VECM model. The first type – ex - post
forecasts – was performed for the year 2002 - 2006 “with the objective of validating the predictive
capacity of the model, comparing annual predictions with real data” (p. 15). The second type – ex ante forecasts – was carried out for the year 2007 - 2010. Ex - post forecast showed relatively good
results for one year period (the largest deviation from real value was 1.37%), while for longer term
periods the precision decreased. Ex - ante forecasts required to do predictions for macroeconomic
variables and perform stochastic simulation. But accuracy of the forecasts could not be measured
due to absence of actual data for the forecasted period.
Saltzman (1967) undoubtedly did a great job pioneering econometric modelling of financial
statements and gave a lot of ideas for further researchers in this field. On the other hand, his work
showed very clearly, what should be done differently. For example, Saltzman‟s (1967) chosen
exogenous variables, such as demand or supply, were rather difficult to measure, thus limited the
model and made it imprecise. Lack of data and unsolved problems with multicollinearity,
overidentification as well as inconsistent estimators, got using OLS, also made results not very
reliable. Due to these reasons the model was not applicable for forecasts, although the author did
not try to use it for this purpose. Also it is unclear, whether exactly such way specified model would
be applicable and explanatory to other firms, but some ideas of selecting variables can be taken.
Elliott (1973), who also attempted to model corporate performance with econometric techniques,
took an advantage over Saltzman (1967) introducing different techniques of incorporating variables
(for example, Almon - weights, accelerator mechanism, moving averages) with rather reasonable
9
It was selected according to R2 measure, Akaike and Schwarz criteria and likelihood test.
16
economic logic. On the other hand, the economic and accounting logic of selecting variables and
specifying equations was not confirmed, when a large amount of estimated coefficients revealed to
be insignificant. Insignificance of coefficients does not allow using the models for other research
credibly, but not bad forecasting performance suggests taking into consideration some new
concepts.
Beedles (1977) gave some critique on Saltzman (1967) and Elliott (1973), but did not provide any
special improvement except of trial to use variables in relative instead of absolute terms. The author
clearly justified selection of endogenous variables (as firm‟s goals), but he admitted appearance of
difficulties with finding correct model specification and that “hundreds of other [macroeconomic]
variables could be used to explain the selected objectives” (p. 1223). Moreover, possible
multicollinearity was not tested and not adequate for overidentified SEM models OLS estimation
method was used.
The main limitation of last two in previous section presented research (De Medeiros, 2005; Doornik
et al., 2009) is that the models were applied to only one monopolistic company. Especially it is
important in the model presented by De Medeiros (2005), where such exogenous variables, as
supply and demand, where used. These variables would be hard to evaluate for non- monopolist
companies. Moreover, De Medeiros (2005) used too few measures to verify “goodness” of the
model: no goodness - of - fit tests was performed and although the model was used for forecasts,
forecasting performance was not evaluated.
Doornik et al. (2009) used the experience of their predecessors quite well and did not repeat most of
their mistakes, especially testing the quality of variables before including them into the models.
Moreover, the authors‟ presented new VAR - VECM approach allows not specifying every single
equation a priori. Final selection of variables can be done after constructing cross - correlation
matrix and performing Granger causality test. Due to this reason model specification becomes
easier. On the other hand these methods do not allow explaining every coefficient, but provide a
possibility to evaluate “the response of a variable with respect to a shock in another variable” (p. 5).
Simplicity of specification and rather good model‟s forecasting performance suggests using the
method for further research, including this Thesis.
2.2. Econometric Modelling of Corporate Finance Data and its Factors
The literature, reviewed in this section, involves papers, which analyses problems different from the
Thesis‟, but presents somehow useful ideas. Some of the papers analyzed another type of data, such
as cross - sectional (Mueller, 1967) and panel (Flannery and Hankins, 2007). Flannery and Hankins
(2007) as well as Christodoulou and McLeay (2009) presented more complex econometrics for
analysis of corporate finance data. They distinguished the main problems, concerned with the
specifics of accounting data, and shown how the problems should be tackled. Bezuidenhout,
Hamman and Mlambo (2008) investigated causality only between accounting variables, but
provided exact rules, what should be done before modelling. Another part of the papers is helpful
choosing macroeconomic variables (Friedrichs, Salman, and Shukur, 2009; Liu and Pang, 2005;
Mumford, 1996; Oxelheim, 2002).
Published simultaneously with Saltzman‟s (1967) Mueller’s (1967) study emphasized the
complexity of a firm‟s behavior and importance of “a complete understanding of decision process”
(p. 1) and interdependence between various decisions, such as various types of investment, “not
only in order to avoid undesirable side effects (…), but also to certain” (p. 1) the reach of
determined goals. For this aim, equations, explaining one type of investment using others as
17
exogenous variables, were constructed and estimated with OLS and k-class estimators 10 using cross
- sectional data of 67 firms. Unfortunately using such type of data results are obtained for overall
sample, but not for individual firms, thus practically managers do not get much benefit in order to
improve their decisions.
Bezuidenhout et al. (2008) devoted their work to investigating causality between cash flow and
earnings variables. They emphasized that it is very important to test stationarity, co-integration and
causality between variables “before any attempt (…) to regress one variable on another” (pp. 1 - 2).
The authors used four earnings measures in the research: earnings before interest and tax
(hereinafter - EBIT), profit before tax, profit after tax and net earnings. Also three cash flow
measures were used: cash generated from operations after adjustment for non-cash items, cash
generated from operations adjusted for investment income received and working capital, and cash
flow from operating activities after adjustments for interest and tax paid. The analysis was done
with data of 70 companies from 16 sectors, thus credibility of the results was high. All the
previously mentioned tests were performed in series. Firstly, stationarity was determined from the
graphs, autocorrelation functions and correlograms as well as unit root tests 11 (ADF) of the time
series. Secondly, the Johansen test for co- integration of the series, which were found to be nonstationary and integrated in the same order, was used. Thirdly, causality between variables, which
were stationary, with Granger test was tried to determine, “that is whether earnings drive cash
flows, or vice versa” (p. 35). Although the authors did not construct any models, they provided
really useful guidelines, how to prepare for modelling in order to get credible and useful models.
The paper of Flannery and Hankins (2007) was concentrated on evaluating various econometric
techniques, the most appropriate “for dynamic corporate finance panel data” (p. 2) despite the
problems of usually small number of observations and existence of large amount of companies
characteristics 12 , which can not be observed in the available data. The authors distinguished such
methods, as IV 13 , generalized method of moments (GMM), long difference (LD) technique and
corrected least squares dummy variable (LSDVC) method. The researchers paid attention that
usually short corporate finance panel data are biased, thus even these previously mentioned “well accepted econometric techniques” (p. 9) can give unreliable results, thus needs special approach. In
order to decide, which method is the best in specific case, Monte Carlo simulation was offered to
use and its usage described. Also the authors admitted that assumptions done simulating data “may
not mimic real world data” (p. 12), thus in order to compare the estimated results with real data
bootstrapping method was used. Although panel data analysis is not appropriate in the case of this
Thesis, the similar problems, as described there, appears in time series data, as well, thus even using
well known and found to be adequate techniques special attention is essential.
Christodoulou and McLeay (2009) brought back the fact, that “accounting variables are
contemporaneously codetermined through the resolution of multiple accounting identities 14 ” (p. 1),
thus modelling these variables as single equations with OLS method, would give unreliable results.
10
A type of instrumental variables, k - class estimator, is an estimation alternative for OLS and uses specified value k,
which is between zero and one (SAS/ ETS User's Guide , 1999).
11
Unit root tests are treated to be more powerful and more efficient tools comparing to graphs, autocorrelation functions
and correlograms, but latter also gives some valuable informat ion.
12
As it was stated in the research, “information on managerial risk aversion, revealed preferences, governance structure,
cash flow characteristics, and other relevant information may be difficult to measure” (p. 2).
13
But, accord ing to the authors, for such type of data it is difficu lt to find appropriate instruments .
14
In such case so called endogeneity problem, when variable correlates with regression‟s error tem, appears.
18
Instead of this the authors proposed using already well known SEM for accounting variables.
According to them, such type of models are suitable, when there are deterministic identities (and
include them “as parameter constraint to ensure that estimates are recovered with more precision to
their theoretical values” (p. 3)) coming from double - entry keeping of data in financial statements.
It was emphasized, that endogenous, lagged endogenous and exogenous variables can be involved
in such models in order to increase precision of them. Besides 2SLS method to estimate three
constructed equations 15 , 3SLS technique, combining 2SLS and seemingly unrelated regression, was
used. The 3SLS method was said to produce better asymptotically efficient results.
Selection of macroeconomic variables to include into models was shortly described in all the papers
discussed before, but it is worth mentioning, what other authors, which analyzed overall activity of
companies, distinguished. Friedrichs et al. (2009), who analyzed failures of Swedish companies
focused on such macroeconomic measure, as the degree of industrial activity, real wage, the pace of
establishing of new companies, the money supply growth rate, openness of the economy and
aggregate economic level (measured as gross national product),. Liu and Pang (2005) found “that
macroeconomic variables, i.e. credit, profits, inflation and company births, appear to be important
factors influencing business failures [in UK]” (p. 1) as well as “monetary policy shocks,
compounded by credit policies and a system of market conditions in different economic regimes”
(p. 18). Also there was a trial to determine a relationship between a company‟s financial results and
such phenomenon as strikes (M umford, 1996).
Oxelheim (2002) in his working paper emphasized the importance of “macroeconomic
fluctuations” on development of companies and gave some ideas on the choice of selecting
macroeconomic variables possible to include into models. The categories of indicators he pointed
out were exchange, interest and inflation rates 16 as well as political risk premiums. He noticed that
especially it is important to pay attention to the indicators in periods, when their volatility is high.
According to him, exchange rates became extremely volatile and thus influential after collapse of
Bretton Woods system in 1971 and since now (except cases when countries have pegged currency)
it is necessary to take them into consideration computing value of companies‟ foreign assets and
liabilities. The group of interest rate indicators usually was analyzed simultaneously with exchange
rate, as it is mainly “concerned with debt, and its main focus is on translation of foreign debt” (p. 6).
The impact of inflation was stated to be concerned with inflationary differences between domestic
country and the one, where a company invests. The last mentioned as important factor, political risk
premium, was not analyzed in more details and there is an uncertainty, how it should be measured.
It is obvious that exact factors should be selected individually. For example, analyzing the activity
of Swedish car producer Volvo the author included real effective exchange rate of Sweden, German
producer price level (as the main competitors of the company are located in Germany) and short term interest rates in Sweden, as well as interest rates of world basket. Also the author stated, “The
set of relevant variables may shift over time and the company should therefore follow up the
process of identification continuously” (p. 13).
15
16
Total accruals, sales and net capital expenditure were modeled.
These variables were selected and derived using “international equilibriu m relat ionships” (Oxelheim, 2 002, p. 10)
such as purchasing power parity and Fisher effect.
19
3. Research Problem Definition
After reviewing the relevant literature, it becomes clear that the research problem, how to model
and forecast items necessary to calculate a company‟s FCF using econometric methods, raised in
the Introduction Part, was not fully solved in existing research. As well as question, what
econometric methods and variables should be chosen to model and forecast FCF items was not
answered yet. Moreover, to the best of the author‟s knowledge, none of the researchers analyzed
European and especially Lithuanian companies, which like companies all over the world, could use
the econometric methods for their financial analysis.
In order to answer the question, verifying the following hypotheses should help:
1.
There are statistically significant correlations between accounting variables themselves and
macroeconomic variables;
2.
There are “causal relationships between variables “ from financial statements ( De Medeiros,
2005) and macroeconomic variables;
3.
FCF components can be explained by other accounting and / or macroeconomic variables
using econometric models;
4.
Econometric models are useful for short - term (one year ahead) forecasting of financial
statement items according to terms of forecast error.
Together with verification of the hypotheses such objectives are pursued:
1.
To choose several Lithuanian companies providing sufficient amount of data;
2.
To select variables (both accounting and macroeconomic), which influences the items
necessary to calculate FCF;
3.
To specify theoretically reasonable SEM and VAR models to explain FCF components;
4.
To perform econometric analysis (including seasonality, stationarity, correlation and
Granger causality analysis) of accounting and macroeconomic variables;
5.
To estimate SEM and VAR models with selected quarterly and annual accounting and
macroeconomic data;
6.
To test validity of the model in terms of goodness-of-fit criteria and forecasting
performance.
All except one (Doornik et al. (2009)) research analyzed, used SEM to model company‟s financial
statements. Using this method the major possible problem to encounter in the research can be
insufficient amount of data available, as only companies listed on exchanges provide their financial
statements publicly and for rather short period of time. Such problem occurred in nearly all
previously done research. Elliott (1973) admitted that his “model is overidentified simultaneous
system having a large number of exogenous and lagged endogenous variables relative to number of
observations” (p. 1511) and to tackle the problem instead of estimating the model using OLS
technique “the structurally ordered instrumental variables approach” (p. 1512), that is 2SLS, was
used. Doornik et al. (2009) presented alternative way for modelling corporate activity: VAR model,
followed by VECM, mainly reasoning that such way estimated models forecast better than SEM.
On the other hand, the better performance was not demonstrated, thus the question remains, maybe
in some specific cases SEM would perform better.
Taking into the consideration all the reviewed literature, there appear several aspects, which should
be mentioned. Firstly, it is very important to test the quality (multicollinearity, stationarity and so
forth) of variables before including them into the models. Secondly, it is widely agreed, that
company‟s activity is influenced by such phenomenon as political risk or production demand, but it
20
is essential to select macroeconomic factors, which are credibly measurable and their time series are
of an appropriate length. In other way taking doubtfully measured or artificially created variables
might lead to misspecified models or unexplainable results. The last analyzed papers applied the
models specified to only one monopolistic company, thus it is unclear if such type of models would
“work” for companies from other sectors or even countries. Due to this reason it is useful to apply
the same model to several companies as well as different types of models to the same company.
21
4. Methodological Approach on Econometric Corporate Finance Data
Analysis and Modelling
In this part of the Thesis research design, data their analysis and modelling methods are
described. The overview of methodology includes discussing the empirical research aims,
selection of research type, as well as procedures to be used in order to verify the main
hypothesis, raised in the Introduction part. These procedures mainly involve selection of data,
choosing the variables and performing the econometric analysis on them, as well as
specification and further usage of models.
4.1. Empirical Research Aim and Research Design
Empirical research aim and objectives are means to solve the research problem and answer the
research questions, while verifying the raised hypotheses. Thus creating satisfactory
econometric models for FCF components can be treated as the empirical research aim.
This aim gives an idea, how a research design, which “provides a framework for the collection
and analysis of data” (Bryman, 2008, p. 31) should be created. In order to pursue the aim only
numeric secondary data, which can not be manipulated, is used. Therefore quantitative nonexperimental research design is appropriate.
This type of design can be split into smaller categories. Quantitative non-experimental research
can be classified according two dimensions: purpose and time (Lapan, Quartaroli, 2009). As
presented in Table 1, there are nine possible types of research. In the Thesis historical data is
used, what means retrospective approach. Using the data and constructed models it is attempted
to explain some variables with other ones and provide forecasts, as well. This indicates both
explanatory and predictive point of view. Thus in this case Type 4, Predictive - Retrospective,
and Type 7, Explanatory - Retrospective, non-experimental research designs are suitable.
Table 1 Classification of quantitative non-experimental research
Descriptive
Predictive
Explanatory
Retrospective
Type 1
Type 4
Type 7
Cross - Sectional
Type 2
Type 5
Type 8
Prospective
Type 3
Type 6
Type 9
Source: Lapan, Quartaroli, 2009, p. 69
Within the framework steps for solving a research problem can be defined. The steps are derived
according to the Thesis objectives, defined in Research Problem Definition Part, and instructions of
Christensen and Johnson (2007) on non experimental quantitative research.
As the research problem and the hypotheses to be tested are already determined, the first thing to be
done is selection of accounting and macroeconomic variables. Secondly, data of several Lithuanian
companies and macroeconomic indicators are collected. Thirdly, model specification is performed.
Going further the data analysis that includes “statistical control techniques” (Christensen, Johnson,
2007) and estimation of the specified models is implemented. After this the best models are selected
and forecasts provided. The last, but not the least thing is interpretation of the results.
22
4.2. Variables and Data, Used to Model FCF Components
In the Thesis are modeled accounting variables, necessary to calculate a company‟s FCF. According
to conventional corporate finance literature, “free cash flow is the amount of cash that the firm can
pay out to investors after making all investments necessary for growth” (Allen, Brealey, Myers,
2006, p. 508) and can be calculated according to the general formula:
FCF = Operating CF – Investment CF
(1)
In order to use data for modelling, it must be easily collected from available sources, what in this
case are financial statements of companies‟. Due to this reason the components of the formula (1)
must be decomposed into smaller ones. The first component in the formula, Operating CF, can be
calculated from company‟s operating profit subtracting taxes 17 and adding depreciation and
amortization18 . The second component, Investment CF, involves investment to working capital and
investment to L-T assets. Such types of investments still are not proper items to be taken from
financial statements. In order to get investment to working capital, change of it (working capital this
year minus working capital last year) is taken. Using common definition of working capital, that is
difference between current assets and liabilities, the latter are taken from company‟s balance sheet
and modeled. Investment to L- T assets usually is calculated as change in L-T assets (the item this
year minus last year value).
Therefore with the purpose to calculate company‟s FCF, operating profit, current assets and
liabilities and L-T assets and liabilities are necessary and are taken as endogenous variables,
presented in Table 2.
Table 2 Endogenous variables and their sources
Abbreviation of variable
OP
CA
CL
LTA
LTL
Name of vari able
Operating profit
Current assets
Current liab ilit ies
Long - term assets
Long - term liab ilit ies
Source
Income statements
Balance sheets
Balance sheets
Balance sheets
Balance sheets
More complicated task is selecting exogenous 19 variables, which mostly influence endogenous
ones. They can be divided into two groups: accounting variables and macroeconomic variables. The
choice of the variables is based on analysis of all articles discussed in Review on Literature part.
From accounting variables in the majority of articles revenues and various types of expenditures
were taken as exogenous. Sales are considered to influence nearly all measures of corporate
activity. In this case the direct impact of sales should be felt on profit, as well as both types of
liabilities and assets. From various types of expenditures (for example, general and administrative,
operating, costs of goods sold (hereinafter – COGS) and so forth), depending on company‟s
17
Taxes are not modeled, as amount of them are can be calculated multip lying Operating profit and corporate tax rate,
which is usually fixed.
18
Depreciation and amort izat ion is not modeled, as some of the selected companies did not provide these measures for
the entire period. What is more, depreciat ion and amort ization can be easily calculated, when there is known value of L T assets and depreciation rates, used by specific co mpany.
19
Although in VAR models all variables are called endogenous, in this and other sections until VAR description, the
variables, wh ich are not modeled, but are used only as exp lanatory, are called exogenous.
23
activity, the major part of expenses usually constitutes COGS, thus they are taken as exogenous
accounting variable, too. In some cases, if COGS are not available, total expenditures are taken.
The logic on selecting the macroeconomic variables differs going through previous research. The
selection procedure in this Thesis is done after critical analysis of the literature and using such
criteria as availability of data and their relevance in the context of Lithuanian companies. All
exogenous variables are listed in Table 3.
Table 3 Exogenous variables and their sources
Abbreviation of variable
REV
EXP: COGS / T_ EXP
Name of vari able
Accounti ng variables
Source
Revenues
Expenses: Costs of goods sold / Total
expenses
Income statements
Income statements
Macroeconomic variables
Interest rates20
IR
DI / N_ EARN
GDP_ G
EX
FDI
INFL
Disposable income 21 / Average net
monthly earnings
Growth of gross domestic product
Expo rt (economic openness)
Foreign direct investment stock
Inflat ion
The Bank of Lithuania,
www.euribor.org
Depart ment of Statistics
Depart ment of Statistics, Eurostat
All the selected variables, according to the author‟s opinion might be useful due to the following
reasons:
The first macroeconomic variable, strongly influencing companies‟ financials – mainly liabilities
- is interest rate. Companies very often finance their L-T investments, as well as working capital,
with funds borrowed from banks and costs of these borrowings in Lithuania usually are based on
interbank offered rates EURIBOR and VILIBOR. Due to this reason, these measures are used as
interest rates.
Personal income, measured as disposable income or net earnings, is also influential indicator,
showing purchasing power of households. This is especially important for companies, which sell
their production to the end - users and the production is not of the first necessity, such as
refrigerators.
Growth of the gross domestic product (hereinafter - GDP) is the basic measure of the overall
economic situation in a country, thus affects results of individual companies, as well (Doornik et
al., 2009).
Amount of a country‟s export shows openness of its economy, thus it is important indicator for
companies that sell substantial part of their production in foreign countries. This measure mainly
can affect the level of company‟s inventories (what often constitutes a large part of current
assets).
Foreign direct investment (hereinafter - FDI) shows how much foreigners invest into L-T assets
in a country, thus can be related with the level of companies‟ L-T assets and may be significant
modelling this row of financial statements.
20
21
6-month VILIBOR and 6-month EURIBOR.
There is no available quarterly data of disposable inco me, thus estimating quarterly models average net monthly
earnings are used.
24
The last, but not the least macroeconomic indicator, which affects terms of money of both assets
and liabilities is inflation, measured as consumer price index.
All the data, necessary to construct previously mentioned variables, are secondary and publicly
available. The source of accounting variables is the companies‟ financial statements, taken from
internet page of exchange operator NASDAQ OMX Vilnius and internet pages of the selected
companies.
Specified models are applied to four Lithuanian companies, indicated in Table 4.
Table 4 Information about the companies, to which models are applied
Company
name
Abbreviation
TEO LT
AB
TEO
Rokiškio
Sūris AB
RSU
Snaigė AB
SNG
VST AB
VST
Acti vi ty Description
One of the largest telecommunication
companies in Lithuania
The largest and most advanced cheese
manufacturer in Lithuania and the Balt ic
States
The only producer of household
refrigerators in the Balt ic States
Distributor and supplier of electric
energy in the Western and Central
Lithuania
Data period / Number of
observations
Annual
Quarterly
1995 - 2009 /
15
2002 I – 2009 IV /
32
1998 - 2009 /
12
2003 I – 2009 IV /
28
-
2003 I – 2009 IV /
28
-
2003 IV – 2009 IV /
25
The main reason for the choice of these specific companies is that they have provided financial
statements for the longest period of time (Table 4). Furthermore, they are chosen to represent
different industry sectors, specified in Table 4.
Macroeconomic variables are taken from internet pages of state institutions (for example,
Department of Statistics to the Government of the Republic of Lithuania, the Bank of Lithuania and
so forth) and other reliable sources, such as Eurostat. Exact sources of each indicator are specified
in Table 3.
4.3. Procedures of Econometric Analysis of Data
The data, that is used, are time series according to their structure, thus before any kind of modelling
some procedures must be done in order to get reliable results. The procedures include testing for
seasonality, stationarity, multicollinearity, co- integration and causality between variables.
Moreover, various types of tests help to determine specification of models (Doornik et al., 2009, p.
5).
4.3.1. Seasonality and Stationarity Analysis
Very often economic data is non-stationary and / or has a seasonal component (Shumway, Stoffer,
2006, p. 18). These characteristics firstly can be observed plotting the data against time and
autocorrelation or partial autocorrelation graphs. The problem of seasonality, usually observed in
quarterly and monthly data, is not very serious and can be solved in the easiest way using seasonal
adjustment procedure, incorporated in almost every econometric - statistical software (for example,
EViews).
More attention should be paid on testing, if the data is stationary, as non-stationary data is like “a
particular episode [and] (…) it is not possible to generalize it” (Gujarati, 2004, p. 797) as well as
25
use for forecasts. Moreover, including such type of data into regression models can cause spurious
regression22 (Gujarati, 2004, p. 797).
General idea about the nature of time series, as well as existence of stationarity, can be gotten from
previously mentioned graphs (Bezuidenhout et al. 2008; Gujarati, 2004), but the most reliable
results can be achieved performing unit root tests. One of the most popular is Augmented Dickey Fuller (hereinafter - ADF) test, also performed in the works of Bezuidenhout (2008), De Medeiros
(2005) and Doornik et al. (2009).
ADF test also helps to determine, what type of non-stationarity (if any) a time series has: if it is
difference stationary or trend stationary. The type of non-stationarity is important to know in order
to apply appropriate transformation to make the series stationary (Bezuidenhout et al. 2008,
Gujarati, 2004). If a time series is difference stationary, this means that “the first differences of such
time series are stationary (…) therefore, the solution here is to take the first differences of the time
series” (Gujarati, 2004, p. 820) and use them for further modelling. Trend stationarity implies using
detrending procedure, when the series is regressed on time and the residuals left are suitable for
further usage (Gujarati, 2004).
4.3.2. Correlation Analysis
Correlation analysis is very useful before starting regression analysis. In case of the Thesis
equations forming SEM has a form of multiple regression, thus correlation analysis is necessary.
While existence of high correlation between exogenous and endogenous variables usually leads to
better modelling results, high correlation between exogenous variables, or regressors, causes the
problem, called multicollinearity. This phenomenon makes model estimation results imprecise;
more coefficients can appear to be statistically insignificant, while at the same time coefficient of
determination, R - squared, tends to be close to one (Gujarati, 2004, p. 350). According to Gujarati
(2004, p. 359), “the meaningful distinction is not between the presence and the absence of
multicollinearity, but between its various degrees”. This means, that it is not possible absolutely
avoid multicollinearity, but it is necessary to decide, what level of it is still acceptable. The simplest
way to avoid problems, concerned with multicollinearity, is constructing cross - correlation matrix
and not including regressors with correlation coefficient higher than 0.8, (Gujarati, 2004, p. 359)
into one equation.
Cross - correlation matrix also can be useful deciding not only which variables should not be
included into equations, but also which ones should surely be, as the “matrix indicates the intensity
and direction of the linear relationship among the variables” (Doornik et al., 2009, p. 6). Decision
on inclusion of variables into models is based on statistical significance of the correlation
coefficients. The significance is calculated under the null hypothesis, that correlation coefficient is
equal to zero, what means it “could arisen by chance” and would not “be found in the population
from which the sample was taken” (Bryman, 2008, p. 335).
4.3.3. Granger Causality Analysis
Previously mentioned cross - correlation matrix helps to determine strength of relationship between
variables, but does not tell anything about causality or direction of influence (Bezuidenhout, 2008,
Gujarati, 2004), thus it is difficult to decide, which variable is a cause and which one is a result.
Situation, when one variable influences another or, in other words, is useful forecasting it, is called
Granger causality. On the other hand, this is purely statistical information, but not logical causality,
thus economic conclusions must be done carefully (Kvedaras, 2005). In order to identify this,
22
Transformation of non-stationary time series helps to avoid spurious regression, but “may lead to misspecification
error” (Bezuidenhout, 2008, p. 29), if the variables are related in the long run, or co-integrated, and this relationship
may be lost after d ifferencing (Bezuidenhout et al. 2008, Gu jarati, 2004). Although in the case of the Thesis most of the
variables are non-stationary, co-integration is not tested, as the series are too short, and even existence of co -integration
would not be useful for further modelling.
26
standard Granger causality test (Gujarati, 2004, pp. 696 - 698), with null hypothesis, that one
variable does not Granger cause another, is used. The test is particularly useful deciding, which
variables should be included into VAR models.
4.4. Model of FCF Components Specification
Three possible ways of modelling time series data, SEM, VAR and VECM, were used by
researchers, mentioned in previous parts, who attempted to use macroeconomic data for modelling
and forecasting corporate performance. First two types of models 23 are applied in the Thesis, too.
4.4.1. SEM Theory and Model Specification
To model company‟s financial statements most of the analyzed researchers (Saltzman, 1967; Elliott,
1973; Beedles, 1977; De Medeiros, 2005; Christodoulou and McLeay, 2009) chose simultaneous
equation approach, as there was observed “two - way flow of influence among economic variables;
that is, one economic variable affects another economic variable(s) and is, in turn, affected b y it
(them)” (Gujarati, 2004, p. 715), thus valuation of single equation would be inappropriate. In
multiple equations endogenous variables are mutually or jointly dependent and the system is said to
be full, when number of equations or / and identities is equal to number of endogenous variables
(Gujarati, 2004; Greene, 2003). It noteworthy, that “endogenous variable (that is, regressand) in one
equation may appear as an explanatory variable (that is regressor) in another equation of the
system“ (Gujarati, 2004, p. 729). Besides current endogenous and exogenous variables in SEM
equations can be included lagged endogenous and exogenous variables. Both latter, as well as
current exogenous variables, are called predetermined variables (Gujarati, 2004; Greene 2003).
General form of structural SEM is:
(2)
where Y1i and Y2i (i = 1, …, n) are mutually dependent endogenous variables and X1i (i =
1,…, n) is an independent exogenous variable, u1i and u2i (i = 1, …, n) are the stochastic (random)
disturbance (or error) terms (Gujarati, 2004; De Medeiros 2005). The errors are assumed to be “well
behaved” (Greene, 2003, p. 379), what means independently distributed, having zero mean,
constant variation (homoscedastic) and uncorrelated (Račkauskas, 2003).
Before estimating the system “it is necessary to establish that the sample data actually contain
sufficient information to provide estimates of the parameters” (Greene, 2003, p. 421) or, in other
words, to solve an identification problem. The equation can be exactly identified, overidentified or
unidentified. In the case of exact identification there can be obtained unique values of structural
coefficients, while the system is said to be overidentified, when there is more than one estimator of
the parameters (Gujarati, 2004). The simplest method to check, whether a system is identified, is the
order condition, which states, that in order the equation in system of M (M – number of endogenous
variables in the model) simultaneous equations to be identified, the number of predetermined
variables excluded from the equation must not be smaller than the number of endogenous variables
included in that equation minus one. Shortly it can be written this way:
K –k ≥m –1
23
VECM models are not used, as available t ime series are too short and such type models would not be informative.
(3)
27
Where m is number of endogenous variables in a given equation; K is number of
predetermined variables in the model including the intercept; k is number of predetermined
variables in a given equation (Greene, 2004).
Although in some analyzed works (Saltzman, 1967; Beedles, 1977; Roucan-Kane, Ubilava and Xu,
2007) OLS method was used to estimate SEM, according to common econometric literature, “in
this situation the classical OLS method may not be applied because the estimators thus obtained are
not consistent 24 ” (Gujarati, 2004, p. 729). One from appropriate ways for the estimation is choosing
instrumental variables. Probably the most popular IV approach is two - stage least squares method
and from analyzed works it was used by Elliott (1973), Beedles (1977), De Medeiros (2005) and
Christodoulou and McLeay (2009). The 2SLS method is especially suitable for overidentified
equations and “the basic idea behind [it] is to replace the (stochastic) endogenous explanatory
variable by a linear combination of the predetermined variables in the model and use this
combination as the explanatory variable in lieu of the original endogenous variable” (Gujarati,
2004, p. 785).
Initially there is specified the following model:
4)
The final specification of the model is selected according to every specific company‟s data after
analysis of data (according to significance of correlation coefficients and results of Granger
causality test), model estimation and comparing some characteristics, showing “goodness” of the
models. The characteristics include:
Goodness - of - fit measures: adjusted R - squared, which similarly as simple R - squared shows,
how well dependent variable is explained by independent ones, but it “pena lizes R2 for the
addition of regressors which do not contribute to the explanatory power of the model“(EViews 5
User‟s Guide, 2004, p. 435); and Akaike (hereinafter - AIC) and Schwarz (hereinafter - SC)
criteria, which are the smaller the, better, as they provides additional “penalty” for adding more
regressors (Gujarati, 2004));
Significance of coefficients (jointly of all coefficients according to p - value of F - statistic 25 );
Autocorrelation of residuals according to Durbin - Watson statistic (hereinafter – DW), which
should be approximately equal to 2 in order to state that residuals are serially uncorrelated;
Normality of residuals according to Jarque - Bera test under the null hypothesis that series are
normally distributed.
4.4.2. VAR Theory and Specification
Vector autoregressive models are an alternative for SEM and were developed seeking to create “less
demanding and more manageable” (Garratt, Pesaran, Shin, 1998, p. 6) methods for modelling.
24
In SEM models variables usually correlate with disturbance terms and this causes bias of estimated parameters
(Gujarati, 2004; Greene, 2003)
25
On statistical significance of individual coefficients was not paid much attention and even some insignificant
variables were left in equations due to their economic importance.
28
VAR, as well as SEM, is built from several equations, each describing single endogenous variable.
The main differences from SEM there are that each endogenous variable is tried to explain mainly
by its and other endogenous variables‟ 26 lagged values, and there is no need to define strictly every
single equation looking for the system to be identified as well as to meet economic logic (Gujarati,
2004).
Generally using matrix notations VAR model can be written as follows:
5)
where Y is an n vector, containing each of the n endogenous variables included in the
model; B0 is an n vector of intercept terms; Bi (i = 1, …, p) are matrices of endogenous coefficients;
is matrix of exogenous coefficients; X an k vector, containing each of the k exogenous variables
included in the model; and u is n vector of error terms (Enders, 2003, EViews 5 User‟s Guide,
2004).
Estimation of the VAR model can be done applying the usual OLS technology for each single
equation, as there is no problem of contemporaneous relationships between variables and residuals
of the models are white noise, estimators are consistent (Kvedaras, 2005; Gujarati, 2004).
The biggest problem with VAR modelling is choosing the reasonable number of variables and
appropriate lag length. The choice is restricted with number of available observations, as when there
are n variables and p lags in the model, it is necessary to have at least np + 1 observations in order
to estimate the VAR equations (Kvedaras, 2005). The choice is done according to AIC criterion,
which is the most appropriate for relatively small samples comparing to number of parameters
(“rule of thumb: n / k < 40” (Hu, 2007, p. 12), where n is number of observations, k is number of
estimated parameters).
As samples of the selected endogenous variables are not big (from 25 to 32 quarterly observations),
it is not possible to construct VAR using all exogenous variables listed in Table 3. The decision,
which variables to include into the models is made according to the results of Granger causality test,
described in Section 4.3.3.
Validity of VAR models is determined according to the same information criteria AIC and
properties of the residuals, which must be not autocorrelated in order the model to be proper for
further usage. This is tested using portmanteau Box - Pierce / Ljung - Box and LM tests with the
null hypothesis, that there is no serial correlation in selected number of la gs.
4.5. Forecasting with SEM and VAR Models
With the estimated SEM and VAR models are performed in-sample, or ex - post, forecasts. This
means that sample for modelling is reduced and forecasted values are compared with actual ones.
Two types of forecasts are carried out:
Static forecasts 27 : with SEM, estimated with annual data, for one period, that is the year 2009.
Dynamic forecasts 28 : with SEM and VAR, estimated with quarterly data, for four periods, that is
2009 I – 2009 IV.
26
There is no trouble to determine, which variab les are endogenous and which exogenous , as all variables in VA R are
said to be endogenous (Gujarat i, 2004), but sometimes variables, that are known to b e exogenous for sure, are included.
27
Static fo recasting performs one-step ahead forecasts, using actual historical data (EViews 5 User„s Guide, 2004).
29
Forecasting performance is measured calculating percentage deviation of forecasts from the actual
values. The deviation also is called forecast error. Using it forecast accuracy can be calculated
subtracting forecast error from one (Forecast accuracy (%) = 1 - Forecast error (%)) (MAPE and
Bias – Introduction, 2004 - 2009). The closer the error is to 0%, the better is accuracy of a forecast.
If the error is higher than 100% it “implies a zero forecast accuracy or a very inaccurate forecast”
(MAPE and Bias – Introduction, 2004 - 2009).
4.6. Choice of Software for Econometric Analysis of Data
There is a rather wide range of appropriate software for performing in previous section discussed
data analysis and modelling. This includes such statistical packages, as EViews, used mainly for
econometric analysis of time series, Gretl, which is an open - source software, applicable to various
fields of econometrics, and even programming language R, developed for various statistical and
econometric analyses.
Most of the analyzed authors did not specify, what software they used for the analysis of data and to
estimate the constructed models, but some of them did. For example Buus (2008), analyzing the
activity of listed and not listed companies used Gretl, while Roucan-Kane, et al. (2007), for the
empirical analysis of their work, devoted to the determining “how the firm‟s infrastructure, the
financial characteristics of a company (net income, sales), and the organizational structure (…)
affect R&D investments in the agricultural sector” (p. 1), chose R soft ware in order to estimate
constructed equations by OLS method.
In two of the analyzed works (Bezuidenhout et al., 2008; Cuddington and Khindanova, 2008)
empirical analysis was performed by EViews package. Bezuidenhout et al. (2008) just mentioned
the fact that the “analyses were conducted using the statistical software package, EViews” (p. 31).
More comprehensively the usage of this package for financial statement modelling and forecasting
was described by Cuddington and Khindanova (2008). They said that the model constructed in
EViews “has the advantage of being more transparent and easier to audit and debug” (p. 1) due to
special MODEL object in the package. Moreover, the object “can be used to calculate forecasts and
associated confidence intervals“(p. 1), which are useful measuring level of uncertainty in the
forecasts. Even though, in the paper single equation was estimated and forecasted, it was
emphasized that the MODEL object is suitable for SEM as well, thus is very useful constructing
and estimating models of the Thesis.
28
Dynamic forecasting performs mu lti-step ahead forecasts with the previous values, which are gotten solving specified
model (EViews 5 User„s Gu ide, 2004).
30
5. Empirical Research Report of FCF Components’ Econometric Modelling
In this part of the Thesis empirical research findings are reported. This includes econometric
analysis of the data, estimation of the specified models and forecasting results. The structure of the
report is organized according to succession of the hypotheses and objectives raised in the third part
of the Thesis.
5.1. Results of Seasonality and Stationarity Analysis
5.1.1. Seasonality and Stationarity of Macroeconomic Variables
The analysis is started using annual macroeconomic data, which has no seasonality, thus only
stationarity was tested. Plotted time series graphs of macroeconomic variables (Appendix 1, Figure
3 - Figure 6) can be observed, that most of them are not stationary. This fact is approved by ADF
test and its results are presented in Table 5. For further analysis and modelling are used detrended or
differenced variables. The former procedure is performed with trend stationary data, while the latter
– with difference stationary data.
Table 5 Results of ADF test for annual macroeconomic variables
Vari able
GDP_ G
DI
N_ EARN
EX
INFL
FDI
EURIB OR
VILIBOR
Type of stati onarity
p - value of ADF test
Further used variable name
Trend stationary
1 difference stationary
1st difference stationary
Trend stationary
Trend stationary
Trend stationary
Trend stationary
st
0.99
0.9
0.96
0.93
0.19
0.67
0.18
0.96
RESID_ GDP_ G
D_DI
D_N_ EARN
RESID_EX
RESID_INFL
RESID_FDI
RESID_EURIBOR
D_ VILIBOR
st
In quarterly macroeconomic data, especially GDP growth and Inflation, strong seasonal component
appears (Figure 1 - Figure 2), which must be removed and only then stationarity test can be
performed. After seasonal adjustment (hereinafter - SA), when cyclical seasonal movements from a
series are removed and the underlying trend component of the series are extracted (EViews 5 User„s
Guide, 2004, p. 324), short names of variables are changed to GDP_G_SA and INFL_SA.
In other quarterly macroeconomic variables seasonality is not observed (Appendix 1, Figure 7 Figure 9).
Figure 1 Time series graph of quarterly inflation, Figure 2 Time series graph of quarterly GDP
%
growth, %
Inflation
GDP growth
4
20
3
10
2
0
1
-10
0
-20
-1
-2
-30
2002 2003 2004 2005 2006 2007 2008 2009
2002 2003 2004 2005 2006 2007 2008 2009
31
ADF test showed that all quarterly macroeconomic variables are difference stationary, thus further
the first difference of the variables is used (Table 6).
Table 6 Results of ADF test for quarterly macroeconomic variables
Vari able
GDP_ G
N_ EARN
p - value of ADF test
0.43
0.95
D_ GDP_ G_SA
D_N_ EARN
EX
INFL
FDI
EURIB OR
VILIBOR
0.86
0.19
0.95
0.35
0.91
D_EX
D_INFL_SA
D_FDI
D_EURIBOR
D_ VILIBOR
5.1.2. Seasonality and Stationarity of Accounting Variables
Seasonality and stationarity of accounting variables is tested for every company‟s data separately.
Similarly as in the case of macroeconomic variables, there is no seasonal movement in annual
accounting data of the companies TEO and RSU (Appendix 1, Figure 10 - Figure 15), thus SA is
not necessary. ADF test results, presented in Appendix 2, Table 22, show all variables, except LTA
of RSU, being difference stationary. Therefore these variables are differenced, while LTA of RSU
is detrended.
Going further to quarterly accounting variables, in some of them seasonal component is observed.
Especially seasonality is common for manufacturing companies, RSU and SNG, as well as
electricity distributor and supplier, VS T, as their sales highly depend on season and thus influences
other variables, such as current assets or liabilities. The mentioned tendencies can be seen in graphs
presented in Appendix 1, Figure 16 - Figure 28, and the series, which are adjusted, have short
names with endings SA (Appendix 2, Table 23). In the Appendix 2, Table 23 also can be seen
results about stationarity of variables made after performing ADF test.
Analyzing the variables it was clearly observed that nearly most of them did not meet the
requirements of stationarity and seasonality and thus were not suitable for further analysis and
modelling. The adjustments and transformations made allow performing further research steps
without concern of unreliable results, which could appear due to inappropriate data.
5.2.
Results of Correlation Analysis
Correlation analysis is carried out using variables, which were made stationary and SA (these
procedures were performed for the variables, which where necessary, and they are described in
previous section). All cross - correlation matrixes are presented in Appendix 3, Table 24 - Table 29,
whereas in this section provided matrixes can be seen variables with the highest and statistically
significant correlations (probability levels for the significance of correlation coefficients are
provided in Appendix 4, Table 30 (Saal, 2006)).
Cross - correlation matrix, calculated between annual TEO accounting and macroeconomic
variables, shows that there are no high (> 0.5) correlations between accounting and macroeconomic
variables (Appendix 3, Table 24), while some of the accounting variables 29 correlate significantly
(Table 7).
.
29
D_OP and D_ CA do not correlate with any other variables significantly, thus are not presented in Table 9.
32
Table 7 Statistically significant correlations between annual TEO accounting variables
D_LTA
D_LTL
D_REV
D_CL
D_T_ EXP
D_T_ EXP
0.65**
0.39
0.85***
0.72***
1.00
D_CL
0.44
0.15
0.57**
1.00
D_REV
0.80***
0.71***
1.00
D_LTL
0.81***
1.00
D_LTA
1.00
Significance level: * 10%, ** 5%, *** 1%
The highest and the most statistically significant positive correlation (>0.7) can be observed
between D_LTA and D_LTL, D_LTA and D_REV, D_REV and D_T_EXP, D_REV and D_LTL,
and D_T_EXP and D_CL. Variables‟ positive relationship with D_REV can be explained so that in
order to get higher revenue, more expenses and investments into L-T assets are needed, thus more
have to be borrowed for long term, as well as spent for employees and other means necessary to
provide services. Current liabilities also increase due to need to finance expenditures, which, as was
mentioned, are related to revenue.
There can be observed some rather high and statistically significant correlations between annual
RSU accounting and macroeconomic variables, as well as between accounting variables themselves
(Table 8). Regarding the macroeconomic and accounting variables, the highest relationship is
between RESID_EX and D_CL, RESID_EX and D_OP, RESID_GDP_G and D_OP. Export is
significant to RSU as approximately 70% (in the year 2007 - 2008) of the company‟s production is
sold abroad, thus it is natural that the indicator influences the company‟s current liabilities and
operating profit. On the other hand, the sign of the correlation coefficient between RESID_EX and
D_OP is unexpected. According to economic logic, the higher export should mean the higher profit,
but the negative sign shows the opposite situation, which could be explained that for RSU export
market in the analyzed period was more expensive than profitable. High positive relationship
between RESID_GDP_G and D_OP can be explained by natural reaction of the company‟s growth
to the overall economic situation in the country. From accounting variables the highest correlation
is between D_COGS and D_REV, D_CL and D_OP, what shows that COGS increase in direct
proportion to revenue, while higher current liabilities reduce operating profit.
Table 8 Statistically significant correlations between annual RSU accounting and macroeconomic
variables
RES ID_ RES ID RES ID_ RES ID
EX
_FDI
GDP_ G _INFL
D_DI
D_N_ E D_COG
D_REV
ARN
S
D_OP
-0.74***
-0.45
0.81***
-0.67**
-0.23
-0.45
-0.45
D_CL
0.72**
0.37
-0.55*
0.59*
0.28
0.46
D_REV
0.36
0.03
-0.16
0.13
0.55*
D_COGS
0.67**
0.20
-0.53*
0.44
D_N_ EARN
0.83***
0.54*
-0.47
D_DI
RES ID_ INFL
0.68**
0.32
0.91***
RES ID_ GDP_ G
D_CL
D_OP
0.02
-0.84***
1.00
0.46
0.07
1.00
0.43
0.87***
1.00
0.57*
0.57*
1.00
0.69**
0.94***
1.00
-0.28
0.51
1.00
0.83***
-0.42
1.00
-0.58*
-0.47
1.00
RES ID_ FDI
0.76***
1.00
RES ID_ EX
1.00
33
Similarly as in annual data, in quarterly data case there are no strong relationships between TEO
accounting and macroeconomic variables, and only three correlation coefficients between
accounting variables are statistically significant with 1% significance level (Table 9). These
correlations are between RESID_LTA_SA and RESID_CL_SA, RESID_CL_SA and D_OP_SA,
and D_REV_SA and D_T_EXP_SA. As it was observed in previous matrixes, the latter relationship
is frequent and shows that total expenses increases proportionally to revenue. The first relationship
can not be explained directly, but probably impact appears through other variables, while the second
relationship means, that higher current liabilities, which in TEO case includes payables, wages and
various taxes, directly reduces profit.
Table 9 Statistically significant correlations between quarterly TEO accounting variables
D_OP_SA
RES ID_CL_SA
RES ID_LTA_SA
D_T_ EXP_SA
D_REV_SA
D_CA_SA
-0.23
-0.18
-0.33*
0.12
0.06
D_REV_SA
0.44**
-0.36**
-0.42**
0.53***
1.00
D_T_ EXP_SA
0.03
-0.20
-0.09
1.00
RES ID_LTA_SA
-0.33*
0.70***
1.00
RES ID_CL_SA
-0.49***
1.00
D_OP_SA
1.00
Macroeconomic factor, mostly related with RSU accounting variables, as in quarterly data case, is
D_EX, strongly positively interacting with D_CL_SA, D_CA_SA and D_COGS_SA. D_GDP_SA
is negatively related with D_LTA_SA, what is economically reasonless, while D_INFL_SA affects
the variable positively, meaning that higher price level determines higher value of long term assets.
The strongest relationship is between accounting variables D_REV_SA and D_COGS_SA, which
was explained before, and between D_CA_SA and D_CL_SA, what can mean that current assets
are financed from current liabilities. One more statistically significant with 1% level is negative
correlation between D_CL_SA and OP_SA, and meaning of it was explained discussing
correlations between TEO quarterly data. The cross - correlation matrix, consisting of variables,
between which correlations are statistically significant, is presented in Table 10.
Table 10 Statistically significant correlations between quarterly
macroeconomic variables
RSU accounting and
OP_SA
D_ EX
D_ GDP_
G_SA
D_INFL
_SA
D_CA_S
A
D_REV_SA
0.23
0.34*
0.32*
0.08
0.05
0.06
0.87***
-0.27
D_LTA_SA
-0.03
-0.33*
-0.50***
0.52***
-0.35*
-0.05
-0.39
1.00
D_COGS_SA
-0.01
0.52***
0.34*
0.01
0.25
0.27
1.00
D_CL_SA
-0.49***
0.64***
0.06
0.15
0.90***
1.00
D_CA_SA
-0.31
0.66***
0.19
0.00
1.00
D_INFL_SA
D_ GDP_ G_S
A
0.30
0.00
-0.45**
1.00
0.09
0.27
1.00
D_ EX
0.02
1.00
OP_SA
1.00
D_CL_S D_COGS D_LTA_ D_REV_
A
_SA
SA
SA
1.00
34
Correlation coefficients between variables from SNG financial statements do not show strong
relationship with macroeconomic variables, too. As it was expected, very high correlation (0.98)
exists between D_REV_SA and D_COGS_SA (Table 11). Rather strong relationship is also
between D_LTL_SA and D_CA_SA and D_LTL_SA and D_CL_SA. The latter is negative and can
be explained by company‟s politics to maintain stable level of total liabilities, thus increasing one
part, the other must decrease. Positive relationship between L-T liabilities and current assets might
be more accidental and not suitable for general conclusions.
Table 11 Statistically significant correlations between quarterly SNG accounting variables
D_CA_SA
D_CL_SA
D_COGS_SA
D_LTL_SA
D_REV_SA
D_REV_SA
0.39**
0.48**
0.98***
-0.03
1.00
D_LTL_SA
0.55***
-0.54***
0.00
1.00
D_COGS_SA
0.41**
0.46**
1.00
D_CL_SA
0.23
1.00
D_CA_SA
1.00
In VST case there is only one strongly statistically significant relationship between D_VILIBOR
and D_LTA (Table 12), but the sign of correlation coefficient is unexpected. While economic logic
says increasing interest rates should decrease investments into L-T assets, there the opposite
situation appears. Relationship between D_LTL and D_CA can not be directly explained.
D_N_EARN positive correlation with OP_SA means that higher earnings of people increase their
consumption, thus raising the company‟s profit. As price of electricity (directly and indirectly)
constitutes a significant part of consumer price index, according to which inflation is calculated,
positive correlation between D_INFL_SA and OP_SA is logical.
There exist some correlations statistically significant with 5% significance level, but whether they
are meaning, is viewed in the following section about Granger causality.
Table 12 Statistically significant correlations between quarterly VST accounting and
macroeconomic variables
D_INFL_SA
D_N_ EARN
D_VILIBOR
OP_SA D_CA
D_LTA D_LTL
D_REV_SA
0.27
0.40**
0.11
0.37*
-0.06
0.38*
0.14
D_LTL
0.01
0.20
0.06
0.17
0.50**
0.13
1.00
D_LTA
0.22
0.21
0.53***
0.33
0.02
1.00
D_CA
0.08
-0.04
-0.19
0.27
1.00
OP_SA
0.48**
0.47**
0.25
1.00
D_VILIBOR
-0.09
0.10
1.00
D_N_ EARN
0.10
1.00
D_INFL_SA
1.00
D_REV_SA
1.00
The correlation analysis supports the first hypothesis, raised in third part of the Thesis, that there are
statistically significant correlations between variables from financial statements and
macroeconomic variables are related with them. As it is seen from cross - correlation matrixes, not
many relationships are statistically significant, but these, which are, does not allow to reject the
hypothesis and allows expecting that other hypotheses will be supported, too.
35
5.3. Results of Granger Causality Analysis
Causality analysis with Granger test is performed only with quarterly data, as only this data is used
for VAR models (it was explained in previous chapters, that samples of annual data are too small to
construct and estimate VAR models). Appendix 5, Table 31 - Table 34 present results of the test 30 ,
where null hypothesis, that one variable does not Granger cause another, in cases, when modeled
variables (OP, CA, CL, LTA and LTL) are caused by other variables, were rejected with 10%
significance level.
As it was mentioned in section 4.3.3, economic conclusions from Granger test results should be
made prudently. Despite this the test results are helpful in verifying the second hypothesis of the
Thesis about “causal relationships between variables” from financial statements (De Medeiros,
2005) and macroeconomic variables. The hypothesis can not be rejected, as in all companies‟ cases
there are macroeconomic variables, which Granger cause accounting ones, as well as accounting
variables, which Granger cause another accounting ones (Appendix 5, Table 31 - Table 34).
From macroeconomic variables as Granger cause most often appears D_GDP_G_SA (six times),
D_EX, (four times), D_VILIBOR (three times), D_FDI (three times) and D_INFL_SA (three
times). From exogenous accounting variables the most influential according to Granger test are
transformed (seasonally adjusted and made stationary) revenue and COGS (or total expenditure in
TEO case). Thus all the mentioned variables should be included into most of the models.
5.4. Results of SEM Estimation
SEM models are constructed with both annual and quarterly data. Selection of independent
variables is based on the logic described in section 4.4.1. and according to significance of
correlation coefficients, results of Granger causality test as well as characteristics, showing
“goodness” of the models (the characteristics also are discussed in section 4.4.1.). All of the
specified equations are overidentified, thus systems are estimated 2SLS method. Coefficients with
asterisks, denoting their significance, adjusted R - squared measures and DW statistics are presented
in the tables below. Since most of the variables used for modelling are differenced or detrended and
seasonally adjusted, size of the estimated coefficients is not meaningful, thus only their signs are
interpreted further.
The first system of equations (Table 13) is estimated using TEO annual data and some
macroeconomic variables. Although DW statistics does not show serial correlation in residuals,
explanatory capability of the first two equations is extremely poor (adjusted R - squared is very
small or even negative), coefficients are insignificant and in the second equation residuals are not
normally distributed. Both correlation analysis and these results of the modelling shows that the
most important factors, influencing D_OP and D_CA were not found and D_REV as well as
RESID_INFL were included into the equations only assuming, that they might be economically
significant. D_CL, D_LTA and D_LTL are explained better with the equations. D_REV, included
into all latter equations, as expected, has positive influence on D_CL and D_LTA, while negative
sign in D_LTL‟s equation is meaningless, as the coefficient is statistically insignificant.
Unanticipated is negative sign before RESID_GDP_G in D_LTA equation, but it can be explained
with the fact, that TEO in the period of 2001 and 2006 several times devaluated their L-T assets
(mainly equipment of telecommunications and buildings), while at the same time GDP of the
country was growing. As it was expected D_LTA influences positively D_LTL, since usually
investments into L-T assets are financed from L-T loans.
30
Granger causality test was carried out including four lags and using stationary (level, 1 st difference or detrended) data.
36
Table 13 Summary of TEO annual SEM estimation results
Equation
Vari able
Intercept
D_LTA
D_OP
321.22
-
D_CA
12,984.2
-
D_CL
-11,702.1
-
D_LTA
-46,779.4*
-
D_LTL
7,478.35
0.66*
D_REV
RES ID_ INFL
RES ID_ GDP_ G
Adjusted R2
DW statistics
0.30
0.03
2.48
0.19
-2,823.5
-0.09
1.91
0.50**
0.27
2.15
1.58***
-9,074.2*
0.67
2.41
-0.11
0.57
1.74
P-value of
Jarque-Bera
test
0.82
0.01
0.80
0.88
0.94
The second system of equations (Table 14) is estimated using RSU annual data and a few
macroeconomic variables. In this case D_CA and D_LTL equations are not explanatory at all. The
latter, according to DW statistics, even has autocorrelated residuals. Comparing to the previously
mentioned equations, D_OP and D_CL are described with selected variables pretty well. More than
70% of their variation is explained with independent variables. As it is anticipated, D_REV and
RESID_DGP_G affect D_OP positively. D_CL and RESID_LTA are influenced negatively by
D_OP. The latter relationship can not be explained economically, while the former one says that
better financial situation of the company allows returning short term liabilities and paying to the
suppliers quicker. RESID_LTA is positively related with RESID_INFL, what can mean inflation
causing increase of price as well as value of long term assets.
Table 14 Summary of RSU annual SEM estimation results
Vari able
Intercept
D_OP
RES ID_LTA
D_REV
RES ID_ INFL
RES ID_ GDP_ G
RES ID_ EX
D_VILIBOR
Adjusted R2
DW statistics
P-value of
Jarque-Bera
test
D_OP
D_CA
Equation
D_CL
RES ID_LTA
D_LTL
-4,436.79
0.14*
-
14,958.3**
-0.11
-
15,330.5***
-0.57*
-
3,542.9
-0.52*
5,544.1*
-533.14
-0.001
-
9,267.6***
0.72
1.58
0.06
1.95
0.002
0.71
2.45
0.28
1.80
594.23
-0.17
1.11
0.66
0.67
0.55
0.72
0.65
Estimation results of SEM with TEO quarterly time series (Table 15) are even worse than with
annual data. All of the adjusted determination coefficients R - squared are low (0.06 – 0.26), DW
statistics in two of five equations (RESID_CL_SA and LTL) shows strong serial correlation, while
in the remainder it is moderate; and residuals are normally distributed with 5% significance level
only in RESID_LTA_SA equation. Positive effect of D_REV_SA on D_OP_SA is natural – both of
37
them changes in the same direction. Why D_OP_SA negatively affects RESID_CL_SA was
explained discussing RSU annual SEM; and negative effect on RESID_LTA_SA has no economic
explanation. EURIBOR‟s negative impact on RESID_CL_SA and RESID_LTA_SA is anticipated,
as higher interest rates reduce a wish to borrow as well as to invest borrowed money into L-T
assets.
Table 15 Summary of TEO quarterly SEM estimation results
Equation
Vari able
Intercept
D_OP_SA
RES ID_LTA_S
A
D_REV_SA
D_INFL_SA
D_ GDP_ G_SA
D_FDI
D_VILIBOR
D_ EURIBOR
-772.31
-4.46*
-
RES ID_LTA_S
A
6,460.7
-11.42**
-
82,800.9***
0.10
-5,062.6
-
-39,374.6*
-0.03
-94,122.7*
-4.19
-36,378.3
-
0.14
1.74
0.26
1.04
0.06
1.51
0.15
0.42
0.02
0.04
0.90
0.00
D_OP_SA
D_CA_SA
RES ID_CL_SA
1,172.4
-
4,543.6
-1.18
-0.06
0.60**
-412.66
-
Adjusted R2
0.16
DW statistics
2.11
P-value of
Jarque-Bera
0.00
test
LTL
RSU quarterly SEM (Table 16) is similarly explanatory as annual: two of five dependent variables
are explained with adjusted R - squared higher than 0.7, all residuals are normally distributed and
only one equation (OP_SA) has autocorrelated residuals. As in annual case, OP_SA has a negative
impact on D_CL_SA meaning that better results of the company is related with better turnover of
payables, as well as smaller short term loans. Negative impact of D_LTA_SA on D_LTL_SA is
unlikely to be logically explained. D_REV‟s negative impact on D_LTL_SA means, that getting
more revenue the company can invest into L-T assets more with smaller borrowings. D_INFL_SA
is increasing D_CL_SA, since growing prices mainly cause higher price of raw milk and other raw
materials to be paid to suppliers, thus payables for them increases and forces the company to
borrow more for working capital. GDP growth‟s and export‟s positive influence on some of the
variables is natural. It is difficult to explain, why D_EURIBOR positively affects OP_SA, while
increasing D_VILIBOR, as expected, cause higher borrowing expenses, thus higher D_LTL_SA.
38
Table 16 Summary of RSU quarterly SEM estimation results
OP_SA
D_CA_SA
Equation
D_CL_SA
Intercept
OP_SA
D_LTA_SA
D_CL_SA
D_REV_SA
D_INFL_SA
D_ GDP_ G_SA
D_ EX
6,925.7***
-0.06
117.25
-
449.91
-0.11
0.48*
0.01*
13,701.0**
-2.09***
6,993.3**
0.02***
-1,373.1
0.05
819.0**
-
-584.9
-0.15*
-0.05*
-
D_VILIBOR
D_ EURIBOR
Adjusted R2
DW statistics
P-value of
Jarque-Bera
test
2,436.8*
0.15
1.40
0.74
2.47
-3,470.7
0.72
2.49
-2,138.9
0.18
1.91
1,116.8*
0.11
2.20
0.97
0.59
0.14
0.70
0.88
Vari able
D_LTA_SA
D_LTL_SA
From SEM, estimated using SNG quarterly results (Table 17), only two equations (D_CA_SA and
D_CL_SA) have moderate adjusted R - squared (~ 0.5), what means that a lot of explanatory
variables are missing. Regarding the signs of the coefficients, D_REV_SA influences all except one
(D_LTL_SA equation, where D_REV_SA is not included) dependent variables positively, meaning
that its growth increases the company‟s profitability as well as expands working capital
components. Export‟s positive influence on current assets and liabilities can be explained so that
increasing export requires more raw materials and inventories, which are financed with current
liabilities. Negative signs of D_VILIBOR coefficients show, that due to increasing cost of
borrowing, the company can borrow less and this cause decreasing investments as well as profit.
Table 17 Summary of SNG quarterly SEM estimation results
Equation
Vari able
Intercept
D_LTL_SA
D_CL_SA
D_OP_SA
547.45
-0.08
D_CA_SA
-1,596.5
-
D_CL_SA
-92.85
-0.88
-
D_LTA
-417.88
-
D_LTL_SA
564.82
-0.07
D_REV_SA
D_ EX
D_VILIBOR
Adjusted R2
DW statistics
0.07
-3,543.0*
0.06
2.91
0.25**
0.01***
0.48
2.20
0.36***
0.01**
0.50
2.03
0.08
-2,119.3
-0.05
1.70
-4,857.7*
0.07
2.43
P-value of
Jarque-Bera
test
0.01
0.92
0.43
0.00
0.97
Results of VST quarterly estimation are unsatisfactory at all (Table 18), as three of five equations
have negative adjusted R - squared measures and the measures of other two equations are low, too.
The situation that there are no even moderate relationships between variables could be observed
from cross - correlation matrix, too. Some of the signs at significant coefficients are unexpected.
For example, it is difficult to explain, why D_GDP_G_SA influences OP_SA negatively, while
39
D_VILIBOR has a positive impact on D_LTA. These inaccuracies do not allow consider the model
at least somehow useful and reliable.
Table 18 Summary of VST quarterly SEM estimation results
Equation
Vari able
Intercept
D_LTL
D_CA
RES ID_CL_SA
D_REV_SA
D_N_ EARN
D_ GDP_ G_SA
D_VILIBOR
D_ EURIBOR
Adjusted R2
DW statistics
P-value of
Jarque-Bera
test
OP_SA
D_CA
RES ID_CL_SA
D_LTA
D_LTL
8,837.0***
0.62*
-990.36*
-
13,782.2
-0.91
-0.29
325.96
-
6,877.5
-0.28
13,261.2
-60,054.6*
8.19*
127,808.8***
-
24,332.9
-0.07
-2.92
42,288.5
0.15
1.33
-1.37
2.89
-0.72
1.84
0.32
1.73
-0.66
2.22
0.86
0.00
0.60
0.00
0.35
Quality of the above estimated SEM models suggests that with data, used in the research,
satisfactory such type models of FCF components can not be constructed for none of the companies.
Thus the third hypothesis should be partially rejected.
5.5. Results of VAR Estimation
The best estimated VAR models were selected according to absence of residual autocorrelation and
AIC criterion, including the exogenous variables, which were indicated by Granger causality test
results.
For TEO quarterly data was selected VAR model with two lags (hereinafter – VAR (2)), including
D_REV_SA, D_T_EXP_SA, D_GDP_G_SA and D_VILIBOR as exogenous variables (see
equations 6 - 10). Statistical significance of two lags indicates that impact between TEO account ing
variables is felt for two quarters. Residuals of the model are not serially correlated. Explanatory
capability of RESID_CL_SA, RESID_LTA_SA and LTL is really good (adjusted R - squared >
0.9), while small adjusted R - squared of D_OP_SA and D_CA_SA equations implies, that in order
to explain these dependent variables, it is essential to find additional independent ones.
TEO VAR (2)
D_OP_SA = 3,097.3 - 0.73 * D_OP_SA (-1) - 0.21 * D_OP_SA (-2) - 0.27 * LTL (-1) + 0.23
* LTL (-2) - 0.36 * RESID_CL_SA (-1) + 0.26 * RESID_CL_SA (-2) + 0.05 * D_CA_SA (-1)
+ 0.03 * D_CA_SA (-2) + 0.03 * RESID_LTA_SA (-1) - 0.005 * RESID_LTA_SA (-2) 646.59 * D_GDP_G_SA + 397.52 * D_VILIBOR + 1.39 * D_REV_SA - 0.49 *
D_T_EXP_SA
Adj. R2 = 0.37
(6)
D_CA_SA = 25,260.6 + 1.51 * D_OP_SA (-1) + 2.13 * D_OP_SA (-2) + 0.58 * LTL (-1) 0.71 * LTL (-2) + 1.18 * RESID_CL_SA (-1) - 1.28 * RESID_CL_SA (-2) - 0.85 * D_CA_SA
(-1) + 0.22 * D_CA_SA (-2) - 1.23 * RESID_LTA_SA (-1) + 1.28 * RESID_LTA_SA (-2) +
1,986.86 * D_GDP_G_SA - 873.24 * D_VILIBOR + 0.06 * D_REV_SA + 0.38 *
D_T_EXP_SA
Adj. R2 = -0.02
(7)
40
RESID_CL_SA = - 487.80 + 0.55 * D_OP_SA (-1) + 0.82 * D_OP_SA (-2) + 1.11 * LTL (1) - 0.99 * LTL (-2) + 2.45 * RESID_CL_SA (-1) - 1.63 * RESID_CL_SA (-2) - 0.81 *
D_CA_SA (-1) - 0.15 * D_CA_SA (-2) - 0.47 * RESID_LTA_SA (-1) + 0.46 *
RESID_LTA_SA (-2) + 3,953.7 * D_GDP_G_SA + 20,875.4 * D_VILIBOR - 3.25 *
D_REV_SA + 0.79*D_T_EXP_SA
Adj. R2 = 0.92
(8)
RESID_LTA_SA = 8,832+.3 0.30 * D_OP_SA (-1) + 0.26 * D_ OP_SA (-2) + 0.37 * LTL (-1)
- 0.45 * LTL (-2) + 0.46 * RESID_CL_SA (-1) - 0.41 * RESID_CL_SA (-2) - 0.16 *
D_CA_SA (-1) - 0.13 * D_CA_SA (-2) + 1.41 * RESID_LTA_SA (-1) - 0.46 *
RESID_LTA_SA (-2) - 119.30 * D_GDP_G_SA + 12,228.8 * D_VILIBOR - 1.85 *
D_REV_SA + 0.39 *D_T_EXP_SA
Adj. R2 = 0.96
(9)
LTL = 8,210.4 + 0.29 * D_OP_SA (-1) + 0.33 * D_OP_SA (-2) + 0.37 * LTL (-1) + 0.30 *
LTL(-2) - 0.39 * RESID_CL_SA (-1) + 0.46 * RESID_CL_SA (-2) + 0.25 * D_CA_SA (-1) +
0.22 * D_CA_SA (-2) + 0.12 * RESID_LTA_SA (-1) - 0.09 * RESID_LTA_SA (-2) – 2,406.4
* D_GDP_G_SA – 8,956.6 * D_VILIBOR + 1.80 * D_REV_SA - 0.13 * D_T_EXP_SA
Adj. R2 = 0.92
(10)
For RSU quarterly data selected VAR model with one lag (hereinafter – VAR (1)), including
D_COGS_SA, D_EX_SA, D_N_EARN_SA, D_FDI and D_EURIBOR as exogenous variables
(see equations 11 - 15), is satisfactory according to properties of residuals, but adjusted R - squared
coefficients shows, that variables are not explained well. This time only one lag is statistically
significant, what means that only values from previous quarter influence the current ones.
RSU VAR (1)
OP_SA = 6,515.6 - 0.005 * OP_SA (-1) + 0.11 * D_CA_SA (-1) - 0.24 * D_CL_SA (-1) - 0.13
* D_LTA_SA (-1) + 0.46 * D_LTL_SA (-1) + 5,199.4 * D_EURIBOR - 0.003 * D_EX + 0.001 Adj. R2 = 0.11
* D_FDI + 18.00 * D_N_EARN + 0.05 * D_COGS_SA
(11)
D_CA_SA = –10,009.3 + 1.31 * OP_SA (-1) - 0.65 * D_CA_SA (-1) + 0.72 * D_CL_SA (-1) 0.56 * D_LTA_SA (-1) - 1.19 * D_LTL_SA (-1)– 1,4481.8 * D_EURIBOR + 0.02 * D_EX 0.0003 * D_FDI - 73.59 * D_N_EARN + 0.08 * D_COGS_SA
Adj. R2 = 0.34
(12)
D_CL_SA = –14,345.5 + 1.87 * OP_SA (-1) - 0.47 * D_CA_SA (-1) + 0.86 * D_CL_SA (-1) 0.63 * D_LTA_SA (-1) - 1.08 * D_LTL_SA (-1) – 21,771.9 * D_EURIBOR + 0.02 * D_EX 0.003 * D_FDI - 8.18 * D_N_EARN + 0.07 * D_COGS_SA
Adj. R2 = 0.38
(13)
D_LTA_SA = –4,797.0 + 0.66 * OP_SA (-1) - 0.32 * D_CA_SA (-1) + 0.35 * D_CL_SA (-1) 0.50 * D_LTA_SA (-1) + 0.83 * D_LTL_SA (-1) - 207.10 * D_EURIBOR - 0.003 * D_EX 0.002 * D_FDI + 45.67 * D_N_EARN + 0.02 * D_COGS_SA
Adj. R2 = 0.18
(14)
D_LTL_SA = 720.69 - 0.15 * OP_SA (-1) - 0.09 * D_CA_SA (-1) + 0.04 * D_CL_SA (-1) +
0.04 * D_LTA_SA (-1) - 0.12 * D_LTL_SA (-1) + 1,064.48 * D_EURIBOR + 0.001 * D_EX + Adj. R2 = -0.19
0.0002 * D_FDI - 8.72 * D_N_EARN - 0.05 * D_COGS_SA
(15)
With SNG quarterly data, was estimated VAR (1) model, too. Into the model included exogenous
variables are D_COGS_SA, D_REV_SA, D_EX, D_GDP_G_SA and D_INFL_SA (see equations
16 - 20). Explanatory capability of four from five equations is moderate, with adjusted R - squared
varying from 0.39 to 0.58 and only equation of D_LTL_SA does not explain the dependent
variable at all.
SNG VAR (1)
D_OP_SA = 0.37 * D_CA_SA (-1) - 0.30 * D_CL_SA (-1) + 0.10 * D_LTA (-1) - 0.44 *
D_LTL_SA (-1) - 0.73 * D_OP_SA (-1) - 0.42 - 1.53 * D_COGS_SA + 1.30 * D_REV_SA 0.001 * D_EX + 439.12 * D_GDP_ G_SA + 2,530.8 * D_INFL_SA
Adj. R2 = 0.49
D_CA_SA = –2,658.9 + 0.22 * D_CA_SA (-1) - 0.25 * D_CL_SA (-1) + 0.22 * D_LTA (-1) 0.03 * D_LTL_SA (-1) - 0.60 * D_OP_SA (-1) + 2.09 * D_COGS_SA - 1.66 * D_REV_SA + Adj. R2 = 0.58
0.01 * D_EX + 736.02 * D_GDP_G_SA + 3,591.5 * D_INFL_SA
(16)
(17)
41
D_CL_SA = 1,430.5 + 0.62 * D_CA_SA (-1) - 0.47 * D_CL_SA (-1) + 0.16 * D_LTA (-1) 0.32 * D_LTL_SA (-1) + 0.69 * D_OP_SA (-1) - 0.63 * D_COGS_SA + 0.68 * D_REV_SA +
0.002 * D_EX + 47.17 * D_GDP_G_SA + 3,912.9 * D_INFL_SA
Adj. R2 = 0.50
(18)
D_LTA = 498.07 + 0.82 * D_CA_SA (-1) + 0.01 * D_CL_SA (-1) + 0.37 * D_LTA (-1) - 0.03
* D_LTL_SA (-1) + 0.24 * D_OP_SA (-1) + 0.19 * D_COGS_SA - 0.41 * D_REV_SA - 0.01 *
D_EX + 1,873.5 * D_ GDP_ G_SA + 793.66 * D_INFL_SA
Adj. R2 = 0.39
(19)
D_LTL_SA = –1,454.19 + 0.09 * D_CA_SA (-1) + 0.09 * D_CL_SA (-1) + 0.15 * D_LTA (-1)
+ 0.18 * D_LTL_SA (-1) - 0.59 * D_OP_SA (-1) + 2.44 * D_COGS_SA - 2.23 * D_REV_SA +
0.001 * D_EX + 1,820.79 * D_GDP_ G_SA + 1,533.5 * D_INFL_SA
Adj. R2 = 0.01
(20)
The best from all estimated models with VST quarterly data appeared VAR (1) (see equations 21 25). AIC criteria and properties of residuals indicated to choose the model only with endogenous
variables, although adjusted R - squared measures of the equations are low or negative. It is
obvious, that even VAR methodology is not suitable for VST data or there are other really
influential factors that were not indicated in the Thesis.
VST VAR (1)
OP_SA = 6,978.4 + 0.43 * OP_SA (-1) - 0.02 * D_CA (-1) + 0.10 * RESID_CL_SA (-1) + 0.01
* D_LTA (-1) + 0.001 * D_LTL (-1)
Adj. R2 = 0.18
(21)
D_CA = 13,927.8 + 3.08 * OP_SA (-1) - 0.19 * D_CA (-1) - 0.90 * RESID_CL_SA (-1) + 0.05
Adj. R2 = 0.19
* D_LTA (-1) - 0.70 * D_LTL (-1)
(22)
RESID_CL_SA = -185.62 + 0.23 * OP_SA (-1) - 0.05 * D_CA (-1) + 0.71 * RESID_CL_SA (1) - 0.04 * D_LTA (-1) + 0.05 * D_LTL(-1)
Adj. R2 = 0.55
(23)
D_LTA = -38,519.1 + 3.06 * OP_SA (-1) - 0.37 * D_CA (-1) - 0.16 * RESID_CL_SA (-1) Adj. R2 = -0.14
0.10 * D_LTA (-1) + 0.17 * D_LTL (-1)
(24)
D_LTL = 18,726.7 + 1.04 * OP_SA (-1) + 0.10 * D_CA (-1) - 1.35 * RESID_CL_SA (-1) Adj. R2 = -0.02
0.07 * D_LTA (-1) - 0.43 * D_LTL (-1)
(25)
Situation with quality of VAR models is better than of SEM. Since some of the models‟ equations
has unsatisfactory adjusted R - squared measures, in some of the equations adjusted R - squared are
higher than 0.9, what means that in these equations dependent variables are explained quite well.
Rather good explanatory capability of the models also can be seen in Appendix 6, Figure 29 Figure 32, where actual and with VAR models fitted data are plotted. Also all the selected VAR
models has serially uncorrelated residuals. These facts do not allow rejecting the third hypothesis
about explaining FCF components by other accounting and / or macroeconomic variables using
econometric models.
5.6. Forecasts of FCF Components
From previous sections about SEM and VAR estimation presumptions can be done, that it is
unlikely to make good forecasts with the estimated models. Despite this, forecasts are performed.
Annual data are forecasted with SEM models, while quarterly with both SEM and VAR.
5.6.1. Forecasts with SEM
Static forecasts, made with SEM, estimated with annual data of TEO and RSU for the year 2009
strongly deviates from the actual values (Table 19 - Table 20). Especially bad forecasting
performance is of the TEO equations D_LTA and D_LTL and average forecast error of TEO annual
SEM is higher than 1,000%, indicating absolutely inaccurate forecast. Although adjusted R -
42
squared of the equations was not bad (respectively 0.67 and 0.57) in the forecasting period, actual
and predicted values extremely diverged.
Table 19 Deviation of TEO annual SEM forecasted values from the actual ones
Endogenous vari able
D_OP
D_CA
D_CL
D_LTA
D_LTL
Average
Devi ation
121%
96%
28%
1,086%
5,254%
1,317%
In RSU case forecasts of RESID_LTA and D_LTL the most significantly deviate from the actual
values (forecast error is 123% on average). Deviation of the latter variable‟s forecast could be
predicted due to negative adjusted R - squared measure. On the other hand the smallest D_CA
deviation could not be anticipated from the equations adjusted R - squared, which is equal to 0.06.
Thus adjusted R - squared measure does not help predicting, whether an equation forecasts well.
Table 20 Deviation of RSU annual SEM forecasted values from the actual ones
Endogenous vari able
Devi ation
D_OP
D_CA
D_CL
RES ID_LTA
D_LTL
Average
39%
14%
59%
98%
147%
71%
Dynamic forecasts with quarterly SEM for 2009 I – 2009 IV are not better than with annual models
(approximately half of equations has forecast error higher than 100%), thus they are presented only
in Appendix 7, Table 35 - Table 38.
5.6.2. Forecasts with VAR
Forecasts with VAR models (Table 21; Appendix 7, Table 39 - Table 41) in some cases look better
than ones made with SEM models. For example in TEO case RESID_LTA_SA forecasts averagely
deviates only 11% from the actual values for all four quarters. Deviations of RESID_LTA and LTL
also are not extremely big (on average 20% and 50% respectively for all four quarters). Differently
than in SEM case forecasting accuracy of the equations could be anticipated from adjusted R squared. The measure for the mentioned equations is higher than 0.9, while for the equation, which
forecasts the worst (D_CA_SA) adjusted R - squared is negative. Similar relation with prediction
ability and adjusted R - squared measure can be observed in the models of other companies, as well.
While some equations (RESID_CL_SA and RESID_LTA_SA) of the VAR model for TEO look
rather good (forecast errors do not exceed 20% on average), they can not be extracted form the
model and used for the forecasting separately. Similar situation appears in the models of the other
companies, thus it should be said, that the models forecast not so well, that the models would be
suitable for ex - ante forecasts.
Table 21 Deviation of TEO quarterly VAR forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
D_OP_SA
102%
178%
121%
9%
103%
D_CA_SA
19%
203%
135%
642%
250%
RES ID_CL_SA
5%
71%
2%
2%
20%
RES ID_LTA_SA
0.5%
8%
11%
15%
11%
LTL
103%
36%
57%
3%
50%
The analysis of the above discussed forecasts with both SEM and VAR models should help to
verify the fourth hypothesis of the Thesis, whether such type of econometric models are useful for
43
short - term forecasting of financial statement items according to terms of forecast error. But the
decision, concerned with the hypothesis, is not easy to adopt. In the light of the research made, the
hypothesis can be rejected in both SEM and VAR case, as forecast error in most cases exceeds
100%. One the other hand the decision can not be general for all cases and this is discussed more
comprehensively in the following part of the Thesis.
44
6. Discussion on Results of Econometric Modelling of FCF Components
This part of the Thesis is devoted for discussion, concerned with the findings of the empirical
research. Firstly, the findings are summarized discussing whether they support the hypotheses
raised. Secondly, they are reviewed in the context of existing literature. Lastly limitations of the
study and suggestions, how other research or practical users should apply and improve the research,
are provided.
6.1.
Overview of the Significant Findings of the Empirical Research and Verification of the
Thesis Hypotheses
Initial econometric analysis of the data included tests for seasonality, stationarity, correlation and
Granger causality analysis. Further SEM and VAR models were constructed and estimated and
lastly forecasts with the models were performed.
Regarding the seasonality, graphs of the variables showed that most of quarterly accounting
variables (of all selected companies) had seasonal components, while in the macroeconomic
variables it was observed only in GDP growth and inflation. In order the seasonality do not interfere
with further analysis it was removed seasonally adjusting the variables, where necessary.
Stationarity analysis of the data showed that only a few variables were level stationary. Most of
them (from both accounting and macroeconomic variables) were found first difference stationary,
while the reminder – trend stationary. The latter situation most frequently appeared between annual
macroeconomic variables, which moved to the one direction significantly in the sample period (for
example export and FDI). Trend stationarity was rarely found in the accounting variables,
indicating that there was no constant growth or decline in the financial statement items of the
analyzed companies in the respective period.
Correlation analysis, made with seasonally adjusted and stationary data, was performed in order to
verify the first Thesis‟ hypothesis, if there are statistically significant correlations between the
accounting variables themselves and the macroeconomic variables. In all the constructed cross correlation matrixes a number of the coefficients appeared to be statistically significant. In TEO
annual data case statistically significant were only correlations between accounting variables, while
in RSU annual data cross - correlation matrix could be observed some rather high correlation
coefficients between the macroeconomic and accounting variables, as well. Similar situation was
noticed calculated correlations with TEO and RSU quarterly data. SNG case was similar to TEO, as
there the accounting variables were interdependent, but not related with the macroeconomic ones.
Regarding the VST cross - correlation matrix can be said that only a few from both macroeconomic
and the company‟s variables were related significantly. But summarizing the situation there is not
enough evidence to reject the hypothesis, as interdependence between all types of variables was
perceived.
The results of Granger causality analysis allow confirming the second hypothesis about “causal
relationships between variables” (De Medeiros, 2005). In all of the analyzed companies data were
found 11 – 14 variables, which Granger caused the modeled variables. In TEO situation three from
14 macroeconomic variables Granger caused the modeled ones, for RSU data – seven from 11,
SNG – four from 13, and VST – 8 from 12. Thus the hypothesis can not be rejected.
Although there were found significant correlations between variables of all the companies and
economic specification of the models is reasonable, finding satisfactory SEM models was not an
easy task. Even in the best according to various criteria annual a nd quarterly SEM models appeared
all types of in such models possible problems: insignificant coefficients, unexpected signs at the
coefficients, low or even negative adjusted R - squared measure, serially correlated and not
45
normally distributed residuals of separate equations. All these factors mainly show, that for most of
the dependent variables were not found correct explanatory variables and SEM models could not
explain FCF components of selected companies well.
Explanatory capability, indicated by adjusted R - squared measures, of VAR models is better than of
SEM. Besides this, residuals of the models are not serially correlated. Although into the models
were included the same exogenous variables as in SEM, situation was surely improved by lagged
values of dependent variables. This means that the modeled FCF components were influenced by
their past values possibly more than by selected macroeconomic variables.
Situation, concerned with describing items, necessary to calculate FCF, with SEM and VAR models
is rather controversial. While the estimated SEM models imply rejecting the hypothesis of
possibility to explain FCF components by accounting and macroeconomic variables using
econometric models, VAR models do not allow doing so with certainty, as there are rather well
explained variables. Taking into consideration the fact that length of the samples, used for the
modelling, were quite short and there was not tried to include company specific variables, the
hypothesis can be rejected partially in this situation, but not in general. Moreover, there exists a
possibility of finding more complex types of econometric models for describing times series from
the field of corporate finance, thus the hypothesis should not be rejected.
The last hypothesis of the Thesis says that econometric models are useful for short - term (one year
ahead) forecasting of financial statement items according to terms of forecast error. Verification of
the hypothesis is done calculating to forecast deviations of the estimated SEM and VAR models.
While none of the models forecasted so well that the hypothesis could not be rejected, it would be
arguable to reject the hypothesis generally. Reasons for the doubts are concerned with in the
previous paragraph mentioned factors, concerned with estimation of the models (sample size and
not inclusion of specific variables), as well as the period of estimation. It is noteworthy, that the
year 2009, which was used for the forecasting, was exceptional in the context of sample period. The
quarterly time series do not include previous crises, and thus it was difficult to forecast values in the
overall economic situation, which at least partly did not appear in the period of modelling. As it was
said by Elliott in 1973, “Predicting an individual corporation‟s profits as the economy moves into a
recession is a difficult task.” (p. 1522). Due to these reasons the hypothesis of the models‟
usefulness for forecasting is partially supported.
6.2. Overview of the Findings in Context of the Analyzed Literature
None of the analyzed research, concerned with application of econometrics in corporate finance,
was identical to the one, done writing the Thesis, in such aspects as selected variables, types of
companies, used methods and so on. Thus the results and conclusions also differ and in some cases
are difficult to compare.
Starting from the variables 31 , it is worth mentioning, that some of the authors tried to include rather
complex derivative variables. For example, Saltzman (1967) used various types of expenses,
deflated by wage rate; Elliott (1973) used Almon-weights, accelerator mechanism and moving
averages of variables. Not all the variables proved to be significant nor improved the models, thus
due to simplicity was decided not to include them constructing models in the research of the Thesis.
Other researchers, who analyzed monopolistic companies (De Medeiros, 2005; Doornik et al.,
31
It is reasonable to point out importance of exogenous variables, as endogenous ones were selected according to the
specific aims of each author (for example, Beedles (1977) modeled variables, which, according to him, are the g oals
that companies often select to maximize).
46
2009), could include into the models such really influential variables, as supply and demand, which
for non-monopolistic companies are difficult to measure 32 .
Going further to validity and credibility of the results, these of a few analyzed works are
questionable. For instance, Saltzman (1967) and Beedles (1977) did not solve problems with
multicollinearity, no one of these, as well as De Medeiros (2005) and Elliott (1973) did not test, if
their used variables were stationary or / and did nothing in order to make them stationary. Also
Saltzman (1967) and Beedles (1977) used OLS method, which is inappropriate when systems are
overidentified, to estimate the models. Due to these reasons their statements, that models and
forecasts made with them are good, should be treated with caution.
The findings of the Thesis mostly are comparable with the ones of research of Doornik et al.
(2009), which made nearly all steps, performed in the Thesis. Their performed stationarity analysis
showed that similarly, as was found in the Thesis, most of the variables are non-stationary, thus
they were differenced for further usage. Correlation and causality a nalysis, carried out with the
variables, also showed some relationships 33 between accounting and macroeconomic variables, as
well as Granger causes. The authors could use both co- integration analysis and VECM instead of
VAR models, while in case of the Thesis it was beside the purpose to use, since for such type of
modelling available time series were not sufficient enough. In the research of Doornik et al. (2009)
estimated model one equation also had negative adjuster R - squared measure. The fact vindicates
the models of the Thesis a bit, as well as forecasts, carried out in the mentioned research, that also
deviate from the actual values. Although the deviation is not such big (up to 24%) as in situation of
the Thesis, the findings that forecast made with VAR “are considered superior to simultaneous
equation models” (Doornik et al. 2009, p. 2) coincide.
6.3. Limitations of the Study
Obtaining better results of the research was restricted with some crucial limitations. Mostly can be
pointed out the shortage of data, causing the other restrictions, such as ability to include wider
variety of exogenous variables (or lagged values of them), as well as using more sophisticated
econometric methods (such as VECM).
As it was mentioned in previous sections, rather short time series did not capture all business cycle,
thus ability to construct really explanatory models decreased. Moreover, in such situation “stable
population parameters is certainly open to question” (Beedles, 1977, p. 1230) and thus it was
impossible to make general conclusions, which exogenous variables were really helpful explaining
the endogenous ones for all the companies.
The models, estimated using too small samples, led to poor forecasting capability. Of course the
forecasting performance was affected by the fact that forecasted period was of the overall economic
downturn. But, on the other hand, it should not interfere with well- functioning models, as
forecasting is the main purpose and applicability of such models in practice of companies.
What is more, it is obvious, that it is impossible to specify economically general models for
companies from all sectors. Although there are macroeconomic factors (for example, GDP growth
and interest rates), which undoubtedly are related with endogenous variables in most of the cases,
company specific variables should be really influential and therefore useful for modelling. Such
variables could be price of milk (in RSU case), increase of number of mobile phone users (in TEO
case) and so forth. If the company is a monopolist, it is worth including aggregate market measures,
32
Salt zman (1967) tried to include demand and supply into his model for the company, working in an oligopolistic
market, thus complicat ing selecting the data and making the model not very cred ible.
33
Statistical significance of the correlation coefficients was not tested, thus it is unclear, how much and which variables
are related significantly.
47
such as demand or supply, as did De Medeiros (2005) and Doornik et al. (2009) analyzing Brazilian
petrol producer. On the other hand, desire to include such variables may be restricted with the lack
of data.
Too small number of observations also narrowed the range of options to choose the types of
models. With annual data even VAR models could not be estimated, while, as it is stated in
literature (Gujarati, 2004; Doornik et al., 2009) and was observed in the research, precisely this type
of models forecasts better. Regarding the quarterly data, the series was scarcely sufficient to
estimate VAR models including only one or two lags of endogenous and not including lagged
exogenous variables, whereas it is likely that, for example, four lags of all variables might be
useful 34 . Moreover, an extension of VAR, VECM, could not be used. This type of model provided
rather good results in Doornik et al. (2009) research. In the circumstances of the Thesis it also could
be beneficial, as a lot of the variables used were first difference stationary (integrated in the first
order) and thus could be co- integrated, what would imply usage of VECM. But the models were not
tried to construct, as co-integration vector should be observed in the long – term, what is impossible
to measure.
There are also two more limitations, concerned with practical usage of forecasted values. Firstly, in
practice are needed ex - ante forecasts, thus before carrying out such forecasts projections of
exogenous variables must be done. Projections of macroeconomic variables usually are provided by
state institutions (for example, Ministry of Finance), while accounting variables, used as exogenous
ones, should be forecasted by companies themselves. Secondly, since models usually are estimated
with data, which are made stationary (detrended or differenced) and seasonally adjusted, the
numbers, received from the model must be converted into real values in order to use them
practically (for financial analysis, budgeting or valuation). When the modeled variables are
differenced, real values can be received step by step adding the forecasted difference with previous
real value (or the calculated one). In case the forecasted variable is detrended, real values can be
calculated inserting with model forecasted values into the regressions, used for detrending. If there
are modeled and forecasted seasonally adjusted values, the size of eliminated seasonal component
should be taken into account and, perhaps, added to the forecasted value.
6.4. Implications for Further Research and Practical Application
Implications for further research are based on the limitations, enumerated in the previous section.
First of all, similar research can be performed with larger samples and with more company specific
variables. Besides the fact that having more data SEM models might appear better, this also would
allow to estimate VAR models with more lags, to use VECM method, as well as estimate the latter
types of models with annual data. Estimated VAR-VECM models with both annual and quarterly
data and carried out forecasts can be compared and used for different purposes.
Usage of forecasts, received from the models, is one of the main reasons of any financial modelling.
Selecting to model FCF components in the Thesis mainly was made on purpose to show, whether it
is possible to forecast them and use for DCF valuation. This method requires several years of
forecasts (forecasting period is not strictly determined, but usually ranges from three to ten years),
thus annual models would suit better. On the other hand, if compared quarterly and annual models,
the former are superior, thus quarterly forecasts can be carried out and then received values for
quarter summed up making the annual ones. Also quarterly forecasts can be used for budgeting.
Since most of the companies in Lithuania do not provide their financial data for public usage
(except the ones, which are listed in stock exchanges), it can be recommended to perform the
34
Four lags were included into the model, estimated by Doornik et al. (2009), and it is logical that in quarterly data
impact of variables can be observed for four quarters (one year) period.
48
research not for outside researchers or analysts, but for analysts or other financial specialists of the
companies. They not only have a possibility to use longer time series of accounting variables, but
also are better aware of factors, mostly influencing company‟s activity. The latter reason makes
selecting company specific variables easier task for them and, of course, receiving the variables
from various market research, which often are done by companies.
49
7. Conclusions
The aim of the Thesis was to verify the hypothesis, whether financial statement items, or speaking
more precisely, items necessary to calculate a company‟s FCF, can be modelled using econometric
techniques involving both accounting and macroeconomic variables. In order to solve the problem,
how to model and forecast the selected items, after comprehensive analysis of literature from the
relevant field, was decided to use SEM and VAR methods for modelling. Also were selected four
Lithuanian companies, TEO LT, Rokiškio sūris, Snaigė and VST, and from their financial statements
taken accounting data the variables were formed. According to the length and frequency of the
variables, preliminary were determined, which macroeconomic variables, that were assumed to be
influential (also after analysis of the literature), are available and could be used for further analysis.
The analyzed research and econometric literature suggested steps of analysis, which was necessary
to pursue the aim and to solve the problem. Thus initially was performed econometric analysis to
ascertain, whether the data has signs of seasonality and are stationary. Further correlation and
Granger causality analysis was done. After this followed construction and estimation of SEM and
VAR models. The last and the most important step was carrying out forecasts with the models and
comparing them to the actual data.
The findings of the empirical research include the main results of all previously mentioned analysis.
Most of quarterly accounting variables and some macroeconomic ones had seasonal components,
thus respective variables were seasonally adjusted. Very often economic time series turn to be nonstationary. Such characteristic was observed in the majority of analyzed variables, too. The situation
was improved making them stationary (depending on the type of non-stationarity) differencing or
detrending. Correlation analysis confirmed the hypothesis that there are simple relationships
between accounting variables themselves and macroeconomic variables as there were found
statistically significant correlation coefficients. However significant correlations more frequently
were observed between accounting variables themselves than between accounting and
macroeconomic variables. The hypothesis of causality between variables was also not rejected.
Satisfactory outcomes of correlation and causality analysis led to constructing and estimating SEM
models, which, unfortunately, did not show good enough findings in points of insignificance of
coefficients, low adjusted R - squared and insufficient other goodness - of - fit measures. Such
results induced to state that in case of the research SEM models were not suitable. After SEM
constructed VAR models appeared to be superior to the former ones comparing them in aspects o f
previously indicated goodness - of - fit statistics. The main reason for the improvement could be
lagged values of dependent variables, which were included into the models. Although even VAR
models are not perfect, the previous findings lead to the conclusion that they are more suitable to
model accounting variables. As it could be expected, in forecasting VAR models also performed
better than SEM and this is compatible with the results of previous research.
While the results, which were obtained by the researchers, who used SEM models in their works,
differ from the Thesis‟, the findings of the Thesis mostly are similar to the ones of research of
Doornik et al. (2009). Their performed analogous steps of analysis also showed similar findings,
including non-stationarity of variables, simple and causal relationships between macroeconomic
variables. Despite the fact, that the researchers had longer time series and could use VECM instead
of VAR, the main conclusions, about superior forecasts of such vector models compared to SEM
coincide.
Although research carried out in the Thesis did not provide very good results, it was shown that
there is possibility to model company‟s FCF components using econometric techniques, such as
SEM and especially VAR-VECM, involving not only accounting, but also macroeconomic
variables. It was ascertained, that for such modelling it is necessary to have as long as possible time
series in order to get the most valid models, which could be credibly used for forecasting. What is
more, it was realized, that there is no single specification (in terms of variables for sure to be
50
included) of models, which would be suitable for all companies, as every company is influenced by
specific factors.
Since such type of research in Lithuania was not carried out (or was not publicized), it is worth
doing similar analyses and especially trying to use the ideas provided in the Thesis practically in
companies‟ activity.
51
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35. Saltzman, S. (1967). An Econometric Model of a Firm. The Review of Economics and
Statistics, 49, 3, 332-342
36. SAS/ETS User's Guide (1999). SAS Institute Inc. [On- line]. Retrieved May 05, 2010 from
Colorado State University internet page
http://www.colostate.edu/Services/ACNS/swmanuals/sasdoc/sashtml/ets/chap19/sect32.htm
37. Shumway, R. H., Stoffer, D. S. (2006) Time Series Analysis and Its Applications With R
Examples (2nd ed.). Springer.
38. Tsay, R. S. (2005) Analysis of Financial Time Series (2nd ed.). Wiley-Interscience
53
Appendices
Appendix 1
Time series graphs of macroeconomic variables
Figure 3 Time series graphs of annual GDP Figure 4 Time series graphs of net average
growth and inflation, %
monthly earnings and disposable income (yearly
data), LTL
1800
40
1600
30
1400
20
1200
1000
10
800
0
600
-10
400
200
-20
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
GDP growth
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
Net average monthly earnings
Inflation
Disposable income
Figure 5 Time series graphs of EURIBOR and Figure 6 Time series graphs of FDI stock and
VILIBOR, average of the year, %
export (yearly data), ths. LTL
6.0E+07
20
5.0E+07
16
4.0E+07
12
3.0E+07
8
2.0E+07
4
1.0E+07
0.0E+00
0
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
EURIBOR
VILIBOR
Export
Figure 7 Time series graphs of EURIBOR and Figure 8 Time series graphs of net average
VILIBOR, average of the quarter, %
monthly earnings (quarterly data), LTL
1800
9
8
1600
7
6
1400
5
1200
4
3
1000
2
800
1
0
600
2002
2003
2004
2005
EURIBOR
2006
2007
VILIBOR
2008
2009
2002
2003
2004
2005
2006
2007
Net earnings
2008
2009
54
Figure 9 Time series graphs of FDI stock and
export (quarterly data), ths. LTL
3.60E+07
3.20E+07
2.80E+07
2.40E+07
2.00E+07
1.60E+07
1.20E+07
8.00E+06
4.00E+06
2002 2003 2004 2005 2006 2007 2008 2009
Export
Time series graphs of accounting variables
Figure 10 Time series graphs of annual TEO L- Figure 11 Time series graphs of annual TEO LT assets, revenue and total expenses, ths. LTL
T liabilities and current liabilities, ths. LTL
700000
2000000
600000
1600000
500000
1200000
400000
300000
800000
200000
400000
100000
0
0
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
L-T assets
Revenue
Total expenses
Figure 12 Time series graphs of annual TEO LT assets and current assets, ths. LTL
2000000
1600000
1200000
800000
400000
0
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
Current assets
L-T assets
95 96 97 98 99 00 01 02 03 04 05 06 07 08 09
Current liabilities
L-T liabilities
55
Figure 13 Time series graphs of annual RSU Figure 14 Time series graphs of annual RSU LCOGS and revenue, ths. LTL
T assets and current assets, ths. LTL
700000
240000
600000
200000
500000
160000
400000
120000
300000
80000
200000
100000
40000
98 99 00 01 02 03 04 05 06 07 08 09
Costs of goods sold
98 99 00 01 02 03 04 05 06 07 08 09
Revenue
L_T assets
Current assets
Figure 15 Time series graphs of annual RSU LT liabilities, operating profit and current
liabilities, ths. LTL
200000
160000
120000
80000
40000
0
-40000
98
99
00
01
02
03
04
05
06
07
08
09
L-T liabilities
Operating profit
Current liabilities
Figure 16 Time series graphs of quarterly TEO Figure 17 Time series graphs of quarterly TEO
current and L-T assets, ths. LTL
current and L-T liabilities, ths. LTL
600000
2000000
500000
1600000
400000
1200000
300000
800000
200000
400000
100000
0
0
2002 2003 2004 2005 2006 2007 2008 2009
Current Assets
L-T assets
2002 2003 2004 2005 2006 2007 2008 2009
Current liabilities
L-T liabilities
56
Figure 18 Time series graphs of quarterly TEO
operating profit, total expenses and revenue, ths.
LTL
300000
250000
200000
150000
100000
50000
0
-50000
2002 2003 2004 2005 2006 2007 2008 2009
Operating profit
Total expenses
Revenue
Figure 19 Time series graphs of quarterly RSU Figure 20 Time series graphs of quarterly RSU
current assets and liabilities and L-T assets, ths. operating profit, L-T liabilities and operating
LTL
expenses, ths. LTL
280000
50000
240000
40000
200000
30000
160000
20000
120000
10000
80000
0
40000
-10000
2003
2004
2005
2006
2007
2008
2009
Figure 21 Time series graphs of quarterly RSU
revenue and COGS, ths. LTL
240000
200000
160000
120000
80000
40000
2004
2005
2006
Costs of goods sold
2007
2004
2005
2006
2007
Operating profit
L-T liabilities
Operating expenses
Current assets
Current liabilities
L-T assets
2003
2003
2008
Revenue
2009
2008
2009
57
Figure 22 Time series graphs of quarterly SNG Figure 23 Time series graphs of quarterly SNG
operating expenses and operating profit, ths. current and L-T assets, ths. LTL
LTL
180000
30000
160000
20000
140000
10000
120000
100000
0
80000
-10000
60000
40000
-20000
2003
2004
2005
2006
2007
Operating exenses
2008
2009
2003
2004
Operating profit
2005
2006
2007
Current assets
2008
2009
L-T assets
Figure 24 Time series graphs of quarterly SNG Figure 25 Time series graphs of quarterly SNG
current and L-T liabilities, ths. LTL
COGS and revenue, ths. LTL
160000
140000
140000
120000
120000
100000
100000
80000
80000
60000
60000
40000
40000
20000
20000
0
0
2003
2004
2005
2006
Current liabilities
2007
2008
2009
2003
2004
L-T liabilities
2005
2006
2007
Costs of goods sold
2008
2009
Revenue
Figure 26 Time series graphs of quarterly VST Figure 27 Time series graphs of quarterly VST
L-T assets and liabilities, ths. LTL
current assets and revenue, ths. LTL
900000
3000000
800000
2500000
700000
600000
2000000
500000
1500000
400000
300000
1000000
200000
500000
100000
0
0
2004
2005
2006
L-T assets
2007
2008
L-T liabilities
2009
2004
2005
2006
Current assets
2007
2008
Revenue
2009
Figure 28 Time series graphs of quarterly VST
current liabilities, COGS and operating profit,
ths. LTL
300000
200000
100000
0
-100000
2004
2005
2006
2007
2008
Current liabilities
Costs of goods sold
Operating profit
2009
58
59
Appendix 2
ADF test results of accounting variables
Table 22 Results of ADF test for annual accounting variables
Vari able
P-value of ADF test
TEO
LTA
LTL
OP
REV
CA
CL
T_ EXP
0.31
0.25
0.38
0.53
0.78
0.48
0.76
D_LTA
D_LTL
D_OP
D_REV
D_CA
D_CL
D_T_EXP
Trend stationary
0.56
RESID_LTA
0.37
0.29
0.97
0.98
0.87
0.76
0.84
D_ LTL
D_OP
D_ REV
D_ CA
D_ CL
D_ O_ EXP
D_COGS
RS U
LTA
LTL
OP
REV
CA
CL
O_ EXP
COGS
st
60
Table 23 Results of ADF test for quarterly accounting variables
Vari able
LTA
LTL
OP
REV
CA
CL
T_ EXP
P-value of ADF test
TEO
Trend stationary
Level stationary
st
Trend stationary
0.99
0.00
0.50
0.20
0.71
0.84
0.79
RESID_LTA_SA
LTL
D_OP_SA
D_REV_SA
D_CA_SA
RESID_CL_SA
D_T_EXP_SA
0.62
0.84
0.02
0.68
0.88
0.91
0.65
D_LTA_SA
D_LTL_SA
OP_SA
D_REV_SA
D_CA_SA
D_CL_SA
D_COGS_SA
0.74
0.60
0.52
0.93
0.51
0.58
0.84
D_LTA
D_LTL_SA
D_OP_SA
D_REV_SA
D_CA_SA
D_CL_SA
D_COGS_SA
0.62
0.72
0.00
0.94
0.11
0.91
0.39
D_LTA
D_LTL
OP_SA
D_REV_SA
D_CA_SA
RESID_CL_SA
D_COGS_SA
RS U
LTA
LTL
OP
REV
CA
CL
COGS
st
Level stationary
SNG
st
LTA
LTL
OP
REV
CA
CL
COGS
LTA
LTL
OP
REV
CA
CL
COGS
Level stationary
Trend stationary
st
VST
61
Appendix 3
Cross-correlation matrices
Table 24 Cross-correlation matrix between annual TEO accounting and macroeconomic variables
RES ID_ EX RES ID_ GDP_ G RES ID_ INFL
D_T_ EXP
D_CL
D_CA
D_REV
D_OP
D_LTL
D_LTA
D_N_ EARN
D_DI
RES ID_ INFL
RES ID_ GDP_ G
RES ID_ EX
0.32
0.33
-0.03
0.41
0.14
0.20
0.28
0.88
0.77
0.74
0.07
1.00
-0.02
0.03
0.20
-0.11
-0.18
-0.35
-0.36
0.48
0.55
-0.32
1.00
-0.02
0.06
-0.16
0.10
0.09
0.00
0.00
0.66
0.64
1.00
D_DI
D_N_ EARN
D_LTA
D_LTL
D_OP
D_REV
D_CA
D_CL
D_T_ EXP
0.08
0.18
0.06
0.17
0.08
-0.05
-0.09
0.93
1.00
0.18
0.28
-0.02
0.22
0.01
0.05
0.07
1.00
0.65
0.44
-0.16
0.80
0.41
0.81
1.00
0.39
0.15
0.21
0.71
0.45
1.00
-0.07
-0.07
-0.07
0.32
1.00
0.85
0.57
0.22
1.00
0.10
0.29
1.00
0.72
1.00
1.00
Table 25 Cross-correlation matrix between annual RSU accounting and macroeconomic variables
RES ID_ E RES ID_ RES ID_ RES ID_ RES ID_
URIB OR
EX
FDI
GDP_ G
INFL
D_OP
-0.46
-0.74
-0.45
0.81
-0.67
D_CL
0.53
0.72
0.37
-0.55
0.59
D_CA
-0.23
-0.10
-0.05
0.18
-0.07
D_REV
0.58
0.36
0.03
-0.16
0.13
D_LTL
-0.11
-0.12
0.15
0.03
-0.11
D_COGS
0.71
0.67
0.20
-0.53
0.44
RES ID_LTA
0.11
-0.21
-0.40
-0.24
-0.48
D_VILIBOR
0.23
0.17
0.10
0.09
0.25
D_N_ EARN
0.58
0.83
0.54
-0.47
0.69
D_DI
0.46
0.68
0.32
-0.28
0.51
RES ID_ INFL
0.58
0.91
0.83
-0.42
1.00
RES ID_ GDP_ G
-0.55
-0.58
-0.47
1.00
RES ID_ FDI
0.70
0.76
1.00
RES ID_ EX
0.71
1.00
RES ID_ EURIBOR
1.00
D_DI
-0.23
0.28
-0.02
0.55
-0.10
0.57
-0.06
0.57
0.94
1.00
D_N_ E
ARN
-0.45
0.46
-0.05
0.43
0.01
0.57
-0.01
0.56
1.00
D_VILI RES ID_ D_COG
BOR
LTA
S
-0.03
-0.27
-0.45
0.01
0.35
0.46
0.35
-0.28
-0.42
-0.23
0.31
0.87
0.30
0.28
-0.33
-0.20
0.40
1.00
-0.31
1.00
1.00
D_LTL
D_REV
D_CA
D_CL
D_OP
-0.08
-0.02
0.27
-0.30
1.00
0.02
0.07
-0.45
1.00
-0.06
0.28
1.00
-0.84
1.00
1.00
62
Table 26 Cross-correlation matrix between quarterly TEO accounting and macroeconomic variables
D_CA_SA
D_REV_SA
D_ EURI
BOR
0.14
0.14
D_OP_SA
D_T_ EXP_SA
RES ID_LTA_SA
RES ID_CL_SA
LTL
D_N_ EARN
D_VILIBOR
D_INFL_SA
D_ GDP_ G_SA
D_FDI
D_ EX
D_ EURIBOR
0.01
0.20
-0.44
-0.29
0.01
0.22
-0.04
-0.17
0.06
0.30
0.63
1.00
D_ EX
D_FDI
0.18
-0.05
0.30
0.32
-0.18
0.19
-0.26
-0.19
0.01
0.51
-0.38
-0.01
0.24
0.42
1.00
-0.03
0.13
-0.32
-0.26
-0.04
0.19
-0.02
0.03
0.26
1.00
D_ GDP_ D_INFL_ D_VILIB D_N_ EA
G_SA
SA
OR
RN
0.10
-0.11
-0.16
0.09
-0.17
0.08
0.47
0.22
-0.22
-0.07
-0.02
0.01
-0.06
0.10
-0.20
-0.39
1.00
0.13
-0.03
-0.24
-0.09
-0.10
0.08
0.01
1.00
0.31
-0.08
-0.26
-0.10
-0.35
0.13
1.00
0.08
-0.03
-0.42
-0.19
-0.13
1.00
LTL
-0.02
-0.41
-0.03
-0.07
0.39
0.13
1.00
RES ID_C RES ID_L
L_SA
TA_SA
-0.18
-0.33
-0.36
-0.42
-0.49
-0.20
0.66
1.00
D_T_ EX
P_SA
0.12
0.53
-0.33
-0.09
1.00
0.03
1.00
D_OP_S D_REV_ D_CA_S
A
SA
A
-0.23
0.06
1.00
0.44
1.00
1.00
Table 27 Cross-correlation matrix between quarterly RSU accounting and macroeconomic variables
D_REV_SA
D_LTL_SA
D_LTA_SA
D_ EURI
BOR
0.45
0.14
-0.30
D_COGS_SA
D_CL_SA
D_CA_SA
OP_SA
D_N_ EARN
D_VILIBOR
D_INFL_SA
D_ GDP_ G_SA
D_FDI
D_ EX
D_ EURIBOR
0.34
0.16
0.28
0.24
0.21
-0.04
-0.11
0.11
0.31
0.63
1.00
D_ EX
D_FDI
0.34
-0.05
-0.33
0.25
-0.04
-0.33
0.52
0.64
0.66
0.02
0.51
-0.41
0.00
0.27
0.43
1.00
0.21
0.17
0.31
0.29
0.20
-0.03
-0.03
0.29
1.00
D_ GDP_ D_INFL_ D_VILIB D_N_ EA
G_SA
SA
OR
RN
0.32
0.08
-0.17
0.25
-0.02
-0.31
0.20
-0.18
-0.50
0.52
0.18
0.04
0.34
0.06
0.19
0.09
0.12
-0.22
-0.45
1.00
0.01
0.15
0.00
0.30
0.09
-0.06
1.00
-0.30
-0.34
-0.38
-0.05
0.12
1.00
0.33
0.40
0.25
-0.11
1.00
OP_SA
0.23
-0.16
-0.03
-0.01
-0.49
-0.31
1.00
D_CA_S
A
0.05
0.22
-0.35
0.25
0.90
1.00
D_CL_S D_COGS D_LTA_ D_LTL_
A
_SA
SA
SA
0.06
0.87
-0.27
-0.26
0.01
-0.23
-0.27
1.00
-0.05
-0.39
1.00
0.27
1.00
1.00
D_REV_
SA
1.00
63
Table 28 Cross-correlation matrix between quarterly SNG accounting and macroeconomic variables
D_ EURI
BOR
D_ EX
D_FDI
D_REV_SA
D_OP_SA
D_LTL_SA
D_LTA
D_COGS_SA
D_CL_SA
D_CA_SA
D_N_ EARN
D_VILIBOR
D_INFL_SA
D_ GDP_ G_SA
D_FDI
0.05
0.02
0.18
0.16
0.07
0.10
0.36
0.21
-0.04
-0.11
0.11
0.31
0.16
0.10
0.17
0.05
0.12
0.29
0.66
0.51
-0.41
0.00
0.27
0.43
0.10
0.16
-0.22
0.06
0.05
0.19
0.13
0.20
-0.03
-0.03
0.29
1.00
D_ EX
D_ EURIBOR
0.63
1.00
1.00
D_ GDP_ D_INFL_ D_VILIB D_N_ EA D_CA_S
G_SA
SA
OR
RN
A
0.07
0.32
0.31
0.19
0.01
-0.24
0.07
0.12
-0.22
-0.45
1.00
0.37
-0.01
-0.10
-0.04
0.41
0.42
0.26
0.09
-0.06
1.00
-0.26
-0.31
-0.24
-0.14
-0.19
-0.06
-0.35
0.12
1.00
0.11
0.00
0.02
0.17
0.12
0.21
0.28
1.00
0.39
0.18
0.55
-0.12
0.41
0.23
1.00
D_CL_S D_COGS
A
_SA
0.48
-0.28
-0.54
0.22
0.46
1.00
0.98
0.06
0.00
0.11
1.00
D_LTA
0.13
0.04
0.01
1.00
D_LTL_
SA
D_OP_S
A
D_REV_
SA
-0.03
0.30
1.00
0.14
1.00
1.00
D_LTA
D_LTL
0.38
0.13
1.00
0.14
1.00
Table 29 Cross-correlation matrix between quarterly VST accounting and macroeconomic variables
D_ EURI
BOR
D_ EX
D_FDI
D_REV_SA
D_LTL
D_LTA
D_COGS_SA
D_CA
RES ID_CL_SA
OP_SA
D_VILIBOR
D_N_ EARN
D_INFL_SA
D_ GDP_ G_SA
0.34
0.08
0.03
0.08
0.03
0.17
0.08
-0.06
0.20
-0.13
0.08
0.22
0.10
0.00
0.08
0.01
-0.04
0.11
-0.43
0.51
0.00
0.26
0.48
-0.12
0.06
-0.04
0.00
0.30
0.16
-0.06
0.19
-0.01
0.32
D_FDI
D_ EX
D_ EURIBOR
0.32
0.63
1.00
0.44
1.00
1.00
D_ GDP_ D_INFL_ D_N_ EA D_VILIB
G_SA
SA
RN
OR
-0.10
-0.09
-0.31
0.05
0.10
0.18
-0.33
-0.31
0.13
-0.58
1.00
0.27
0.01
0.22
-0.01
0.08
-0.22
0.48
-0.09
0.10
1.00
0.40
0.20
0.21
0.27
-0.04
-0.11
0.47
0.10
1.00
0.11
0.06
0.53
-0.24
-0.19
-0.07
0.25
1.00
OP_SA
0.37
0.17
0.33
0.01
0.27
0.04
1.00
RES ID_
CL_SA
-0.01
-0.18
-0.23
0.04
0.08
1.00
D_CA
-0.06
0.50
0.02
-0.27
1.00
D_COGS
_SA
0.16
0.07
-0.20
1.00
D_REV_
SA
1.00
Appendix 4
Probability levels for the significance of correlation coefficients
Table 30 Two-tailed probability levels for the significance of correlation coefficients
N
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
22
24
26
28
30
40
0.1
0.80
0.73
0.67
0.62
0.58
0.55
0.52
0.50
0.48
0.46
0.44
0.43
0.41
0.40
0.39
0.38
0.36
0.34
0.33
0.32
0.31
0.26
Source: Saal, W. , 2006
0.05
0.88
0.81
0.75
0.71
0.67
0.63
0.60
0.58
0.55
0.53
0.51
0.50
0.48
0.47
0.46
0.44
0.42
0.40
0.39
0.37
0.36
0.31
0.01
0.96
0.92
0.87
0.83
0.80
0.77
0.73
0.71
0.68
0.66
0.64
0.62
0.61
0.59
0.57
0.56
0.54
0.51
0.50
0.48
0.46
0.40
65
Appendix 5
Results of Granger causality test
Table 31 Results of Granger causality test of quarterly TEO accounting and macroeconomic variables
Null Hypothesis:
F-Statistic
Probability
LTL does not Granger Cause D_OP_SA
11.0571
0.00010
RESID_ CL_SA does not Granger Cause D_OP_ SA
10.0306
0.00019
D_ GDP_ G_SA does not Granger Cause D_OP_SA
5.52330
0.00442
LTL does not Granger Cause D_CA_SA
2.73505
0.06132
D_ GDP_ G_SA does not Granger Cause RESID_CL_ SA
2.85879
0.05377
D_ VILIBOR does not Granger Cause RESID_ CL_SA
4.58480
0.00995
LTL does not Granger Cause RESID_ CL_SA
14.3942
1.4E-05
RESID_ LTA_SA does not Granger Cause RESID_CL_SA
3.30457
0.03245
D_ REV_ SA does not Granger Cause RESID_ CL_SA
3.96195
0.01774
D_ OP_SA does not Granger Cause RESID_CL_ SA
3.50193
0.02778
D_ CA_SA does not Granger Cause RESID_ CL_SA
3.47866
0.02844
D_T_ EXP_SA does not Granger Cause RESID_ LTA_SA
7.68161
0.00086
RESID_ CL_SA does not Granger Cause LTL
18.2160
2.7E-06
RESID_ LTA_SA does not Granger Cause LTL
22.9966
4.6E-07
TEO quarterl y
Table 32 Results of Granger causality test of quarterly RSU accounting and macroeconomic variables
Null Hypothesis:
F-Statistic
Probability
D_FDI does not Granger Cause OP_SA
4.25604
0.01845
D_ EURIBOR does not Granger Cause D_CA_SA
4.10066
0.02097
D_FDI does not Granger Cause D_CA_SA
5.17906
0.00898
D_ N_EA RN does not Granger Cause D_CA_SA
4.32867
0.01739
D_ CL_SA does not Granger Cause D_CA_SA
3.02483
0.05425
D_FDI does not Granger Cause D_CL_SA
5.06738
0.00976
D_ EURIBOR does not Granger Cause D_LTA
3.34228
0.04050
D_ EX does not Granger Cause D_ LTA
5.35417
0.00789
D_ CA_SA does not Granger Cause D_LTA
2.71021
0.07323
D_ COGS_SA does not Granger Cause D_LTA
4.00557
0.02271
D_ LTL does not Granger Cause D_LTA
2.38246
0.10124
RS U quarterly
66
Table 33 Results of Granger causality test of quarterly SNG accounting and macroeconomic variables
Null Hypothesis:
F-Statistic
Probability
D_ COGS_SA does not Granger Cause D_OP_SA
2.48101
0.09173
D_ LTA does not Granger Cause D_OP_SA
4.02478
0.02234
D_ REV_ SA does not Granger Cause D_OP_SA
2.41272
0.09821
D_ EX does not Granger Cause D_CA_SA
2.60041
0.08152
D_ GDP_ G_SA does not Granger Cause D_CA_SA
4.10158
0.02096
D_ GDP_ G_SA does not Granger Cause D_CL_ SA
2.80647
0.06673
D_ CA_SA does not Granger Cause D_CL_SA
4.49126
0.01525
D_ LTL_ SA does not Granger Cause D_CL_SA
5.71027
0.00611
D_ COGS_SA does not Granger Cause D_CL_SA
2.92265
0.05973
D_INFL_ SA does not Granger Cause D_LTA
4.95659
0.01062
D_ CL_SA does not Granger Cause D_ LTA
5.72542
0.00605
D_ COGS_SA does not Granger Cause D_LTA
3.22681
0.04499
D_ REV_ SA does not Granger Cause D_LTA
2.78127
0.06837
SNG quarterly
Table 34 Results of Granger causality test of quarterly VST accounting and macroeconomic variables
Null Hypothesis:
F-Statistic
Probability
D_ GDP_ G_SA does not Granger Cause OP_SA
3.51751
0.04402
D_CA does not Granger Cause OP_SA
3.41820
0.04761
D_EX does not Granger Cause D_CA
25.4364
1.6E-05
D_ GDP_ G_SA does not Granger Cause D_ CA
2.53540
0.10007
D_INFL_SA does not Granger Cause D_CA
3.34781
0.05036
D_ VILIBOR does not Granger Cause D_CA
3.89749
0.03292
D_LTA does not Granger Cause D_CA
23.9406
2.2E-05
D_ VILIBOR does not Granger Cause RESID_ CL_SA
2.62021
0.09282
D_COGS_SA does not Granger Cause RESID_CL_SA
3.22540
0.05559
D_EX does not Granger Cause D_LTL
2.57200
0.09686
D_INFL_SA does not Granger Cause D_ LTL
2.71389
0.08551
D_LTA does not Granger Cause D_LTL
14.6965
0.00022
VST quarterly
Appendix 6
Actual and with VAR models fitted data
Figure 29 TEO quarterly actual and with VAR fitted data
D_CA_SA
160000
D_OP_SA
40000
30000
120000
20000
80000
10000
40000
0
-10000
0
-20000
-40000
-30000
-80000
-40000
2002 2003 2004 2005 2006 2007 2008 2009
Actual
2002 2003 2004 2005 2006 2007 2008 2009
D_CA_SA (Bas eline)
Actual
L-T liabilities
600000
D_OP_SA (Bas eline)
RESID_CL_SA
250000
200000
500000
150000
400000
100000
300000
50000
0
200000
-50000
100000
-100000
0
-150000
2002 2003 2004 2005 2006 2007 2008 2009
L-T liabilities
L-T liabilities (Bas eline)
RESID_LTA_SA
300000
200000
100000
0
-100000
-200000
-300000
2002 2003 2004 2005 2006 2007 2008 2009
Actual
RESID_LTA_SA (Bas eline)
2002 2003 2004 2005 2006 2007 2008 2009
Actual
RESID_CL_SA (Bas eline)
67
Figure 30 RSU quarterly actual and with VAR fitted data
D_CA_SA
D_CL_SA
60000
60000
40000
40000
20000
20000
0
0
-20000
-20000
-40000
-60000
-40000
2003
2004
2005
Actual
2006
2007
2008
2009
2003
D_CA_SA (Bas eline)
2004
2005
Actual
D_LTA_SA
2006
2007
2008
2009
D_CL_SA (Bas eline)
D_LTL_SA
20000
6000
15000
4000
10000
2000
5000
0
0
-2000
-5000
-4000
-10000
-6000
-15000
-8000
2003
2004
2005
Actual
2006
2007
2008
2009
D_LTA_SA (Bas eline)
OP_SA
20000
15000
10000
5000
0
-5000
2004
2005
Actual
2006
2007
2008
OP_SA (Bas eline)
2004
2005
Actual
25000
2003
2003
2009
2006
2007
2008
D_LTL_SA (Bas eline)
2009
68
Figure 31 SNG quarterly actual and with VAR fitted data
D_CA_SA
D_CL_SA
30000
40000
20000
30000
10000
20000
0
10000
-10000
0
-20000
-10000
-30000
-20000
-40000
-30000
2003
2004
2005
Actual
2006
2007
2008
2009
2003
D_CA_SA (Bas eline)
2004
2005
Actual
D_LTA
2006
2007
2008
2009
D_CL_SA (Bas eline)
D_LTL_SA
30000
30000
20000
20000
10000
10000
0
-10000
0
-20000
-10000
-30000
-20000
-40000
-30000
-50000
2003
2004
2005
Actual
2006
2007
2008
2009
D_LTA (Bas eline)
D_OP_SA
20000
10000
0
-10000
-20000
-30000
2004
2005
Actual
2006
2007
2008
D_OP_SA (Bas eline)
2004
2005
Actual
30000
2003
2003
2009
2006
2007
2008
D_LTL_SA (Bas eline)
2009
69
Figure 32 VST quarterly actual and with VAR fitted data
D_CA
D_LTA
600000
800000
400000
600000
200000
400000
0
200000
-200000
0
-400000
-200000
-600000
-800000
-400000
2004
2005
2006
Actual
2007
2008
2009
2004
D_CA (Bas eline)
2005
2006
Actual
D_LTL
2007
2008
2009
D_LTA (Bas eline)
OP_SA
500000
60000
400000
40000
300000
20000
200000
0
100000
-20000
0
-40000
-100000
-60000
-200000
-80000
2004
2005
2006
Actual
2007
2008
2009
D_LTL (Bas eline)
RESID_CL_SA
60000
40000
20000
0
-20000
-40000
-60000
-80000
2005
Actual
2006
2007
2008
2005
Actual
80000
2004
2004
2009
RESID_CL_SA (Bas eline)
2006
2007
2008
OP_SA (Bas eline)
2009
70
Appendix 7
Deviations of with models forecasted values from the actual ones
Table 35 Deviation of TEO quarterly SEM forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
D_OP_SA
57%
173%
161%
57%
112%
D_CA_SA
18%
307%
86%
942%
338%
RES ID_CL_SA
78%
8%
102%
31%
55%
RES ID_LTA_SA
42%
59%
108%
63%
68%
614%
448%
318%
1,209%
345%
LTL
Table 36 Deviation of RSU quarterly SEM forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
OP_SA
68%
84%
31%
25%
52%
D_CA_SA
18%
211%
49%
33%
78%
D_CL_SA
33%
89%
9%
22%
38%
D_LTA_SA
53%
6%
132%
60%
63%
D_LTL_SA
71%
205%
147%
766%
297%
Table 37 Deviation of SNG quarterly SEM forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
D_OP_SA
231%
934%
90%
70%
331%
D_CA_SA
4%
49%
97%
58%
52%
D_CL_SA
85%
50%
65%
98%
75%
D_LTA_SA
96%
149%
94%
287%
157%
D_LTL_SA
112%
82%
100%
171%
116%
Table 38 Deviation of VST quarterly SEM forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
OP_SA
24%
65%
13%
166%
67%
D_CA
75%
92%
68%
125%
90%
RES ID_CL_SA
5%
114%
57%
109%
71%
D_LTA
11%
58%
148%
215%
108%
D_LTL
77%
110%
300%
119%
152%
71
Table 39 Deviation of RSU quarterly VAR forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
OP_SA
3%
18%
169%
47%
59%
D_CA_SA
2%
32%
51%
6%
23%
D_CL_SA
15%
5%
69%
54%
36%
D_LTA_SA
33%
27%
47%
64%
43%
D_LTL_SA
51%
265%
156%
315%
197%
Table 40 Deviation of SNG quarterly VAR forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
D_OP_SA
246%
3,955%
32%
35%
79%
D_CA_SA
10%
12%
79%
32%
33%
D_CL_SA
32%
14%
19%
158%
56%
D_LTA_SA
16%
247%
18%
464%
186%
D_LTL_SA
13%
130%
256%
187%
147%
Table 41 Deviation of VST quarterly VAR forecasted values from the actual ones
2009 I
2009 II
2009 III
2009 IV
Average
OP_SA
30%
173%
65%
340%
152%
D_CA
42%
35%
47%
83%
52%
RES ID_CL_SA
76%
98%
156%
65%
99%
D_LTA
73%
102%
93%
104%
93%
D_LTL
152%
142%
282%
114%
173%
72

econometric modelling and forecasting company`s fcf components

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