Axioma Overview

“I want a second opinion”
Understanding and Leveraging Statistical
Risk Models in Portfolio Management
June 14th, 2016
Chris Canova, CFA – Vice President, Head of Equity Solutions
Anthony Renshaw, PhD – Equity Solutions Specialist
Chris Martin, MFE, CAIA, CIPM – Equity Solutions Specialist
Agenda
• Axioma Risk Models and Use Cases
• Types of Risk Models
• Explaining Statistical Models
• Case Study – US Large Cap Core Manager
• Framework for using Statistical Models
• Recent Trends and Observations
• Improved Risk Management and Portfolio Analysis
Better Results!
• Q&A
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Axioma US4 Equity Risk Model Suite
• Premise: Deliver multiple risk models on a daily basis to provide the most
comprehensive analysis along with the most choice and flexibility for
portfolio construction.
• Fundamental models with comprehensive factors for intuitive and detailed risk
analysis and performance attribution
• Statistical models can provide important additional risk and portfolio construction
insights
• Short and Medium Horizon Model variations to increase risk awareness in fast
changing environments, tuned to the portfolio strategy horizon
• Macro factor analysis to understand sensitivities for analyzing and stress testing the
impact of macro events
• Innovative methods - Robust Statistics and Dynamic Volatility Adjustment (DVA)
• Model Customization - The ability to customize important model features such as
the factors, horizon, and estimation universe to get the most out
• Extensive factor libraries from Axioma + the ability to incorporate your own libraries
or those through third-party Axioma partners such as CS HOLT
• A deep daily history with historical coverage since 1982 for most model variations
• Integration with the Goldman Sachs Shortfall model
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Benefits of Axioma’s Multiple Risk Models
• Confirm multiple estimates of risk Side-by-Side – “The whole is greater than
the sum of its parts”
• Investigate causes of differences in estimates as they occur – leading to
improved knowledge of the risk environment and potential issues
• Higher confidence in portfolio risk interpretations = better portfolio
management
• Trends distinguish differences in short term risks vs. persistent factor changes
• Using Fundamental, Statistical, and Macro models from different vendors can
be problematic:
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•
•
•
Different frequencies used for factor and specific returns
Different methods for estimating volatilities and specific risk
Different half-lives of the models
Comparison analysis can be misleading
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Understanding Statistical Models
• Like fundamental models, the following returns model is used to define the
factor returns and model structure:
• R = Bf + u
• B represents the model factor exposures
• f represents the model factor returns
• u represents the specific return
• In a fundamental risk model, B is pre-calculated, f is estimated
• In a macroeconomic model, f is pre-calculated, B is estimated
• In a statistical risk model – both B and f are estimated simultaneously in order
to maximize model explanatory power
• Benefit – statistical models are “free” to find sources of risk that may be
different from those pre-imposed by the model structure
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Overview of Model Methodology Benefits
• Fundamental Models
• Assumes factors are known in advance, estimates factor relationships
• Widely used for client reporting and communication
• Consistent framework for risk decomposition, performance attribution and portfolio
construction – can explain the factors driving current risk exposures in line with factors
explaining historical performance
• May not contain “all factors” relevant to practitioners
• May miss short-term temporary factors
• Statistical Models
• Makes no assumption of the factors in the model, allows factors to emerge based on
the estimation process (simultaneously identifies factor exposures and factor returns)
• Structure provides the ability to capture short-term risk phenomena
• Responsive and performs well in portfolio construction
• However, lacks intuition of factor exposures when used in isolation
• Macro Economic Risk Models
• Provides sensitivities to key macro factors
• Provides a stress testing framework to understand portfolio responses to macro
events/surprises
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Typical Risk Analytics Best Practices
• Risk decomposition to isolate the sources of expected risk
• % contribution to risk coming from common factors vs. specific risk
• Point-in-time (most recent) with historical time series trends
• Track factor exposures to ensure portfolio consistency
• Understand trends in style, industry, country, and currency exposures through time
• Asset-level risk analysis
• Understand the concentration of risk at the asset or grouped asset level
• % of risk, marginal contributions, implied expected return
• Factor-based performance attribution
• In the context of the risk model – are portfolio exposures paying off on a riskadjusted basis?
• Understand sources of return as a feedback loop to portfolio construction
• Reduce or remove problematic exposures and ensure asset-level return
expectations are translating into ongoing portfolio returns
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What is missing? Can we do better?
• A common goal in any investment process is to:
• Ensure the portfolio is taking exposures in line with the investment process
• Assets, factors, and investment themes
• Identify, understand, and potentially eliminate undesirable risks
• What if the fundamental model is missing important factors?
• There may be a grouping of assets that ex-post have surprising factor returns
• Using a single model could expose the portfolio (and often times lead to)
problematic exposures…
• How can we identify this additional risk?
• Our case study will propose additions to the typical process
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Case Study – US Active Manager
• US Large Cap Core Portfolio
• Typically holds 50-100 stocks (Quarterly holdings frequency)
• Benchmark: Russell 1000
• Target: 3.5% - 4% annualized tracking error
• Seeks to provide exposure to momentum and valuation-oriented factors for
long-term active out-performance
• Optimized using asset rankings while seeking to control active risk within the
tolerance range
• Includes sector and other style constraints
• Targets 80%-100% annualized turnover
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Active Risk Trend – Fundamental Model
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Active Risk Trend – Multiple Models
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Zoom to Last Nine Months
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Factor Risk Spreads
• Definition:
• Factor Risk Spread – The highest minus the lowest factor risk estimate
• Implications:
• Allows the client to monitor the general level of risk differences across risk models to
identify important trends and when differences arise in systematic risk
• In Practice:
• Time series analysis with daily updates provides early signals of possible changes in
risk regime and/or the emergence of non-traditional factor risk sources
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Statistical versus Fundamental Risk Spread
• Definition:
• Statistical risk model estimate minus the fundamental risk model estimate
• Implications:
• The statistical risk model identifies factor volatility that the fundamental risk model
has quantified as specific risk, or may have missed altogether. The risk is coming
from a factor that is not included in the “traditional” factor set.
• In Practice:
• In mid-2008, statistical model risk estimates expanded greatly and the fundamental
model risk estimate lagged. This signaled an early increase in volatility as well as
different risk sources.
• In mid/late 2015 and at the end of Q1 2016, we observed an additional shock in the
higher estimate of risk from the stat models compared with the fundamental models.
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All Models – Risk Trends and Spreads
1.50
1.00
0.50
0.00
SH Statistical - MH Fundamental Spread
SH Statistical - SH Fundamental Spread
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Dig a bit deeper – March 31, 2016
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Intuition via the Assets
• % of active risk is a useful
analytic – We can
decompose the
contributions to risk coming
from each asset in the
portfolio and benchmark
• All assets in the portfolio
and benchmark will sum to
100%
• Allows for useful groupings
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So where do we go?
• We can compare the % of Active Risk for each asset in the context of multiple risk models to
gather some intuition as to where the differences in risk are coming from.
• We now have information that can help us make some decisions around our comfort level with
these increased risks and where they come from.
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What Does History Tell Us?
PROCEDURE #1 – Goal: See how risk differs within groupings
• Take assets in the Russell 1000 universe
• Partition into deciles by Asset Risk difference (Stat minus Fund)
• Review comparisons of predicted and realized risk organized by year and deciles
PROCEDURE #2 – Goal: See how return differs within groupings
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•
•
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Take assets in the Portfolio and Russell 1000 universe
Partition into quintiles by difference in “% of Active Risk” (Stat minus Fund)
Create portfolios of Q1, Q5, and Q2-4.
Compute performance statistics of the resulting portfolios
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Risk Differences Identify Riskier Assets
2016 exhibited some of the highest realized risks over the last 8 years.
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Adjusting Positions – Performance Impact
Long term, HI %∆ TE hurt performance; since 2014, it was a winner.
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How can I use this information?
• Quantitative Active Managers
• Introduce a second risk constraint or objective penalizing risk coming from the
statistical model (in general, or when spreads suggest necessary)
• Adjust asset-level constraints to reduce exposure to assets with high delta
• Pre-screen for risk differences and incorporate into portfolio construction
• Low Vol – clear pattern of higher realized vol in these assets – Low Vol strategies –
research opportunity
• Fundamental/Quantamental Active Managers
• Adjust position sizes for problematic assets to ensure conviction is properly
implemented
• Long-Short Managers
• Explicitly hedge systematic risk as estimated by the stat models in addition to
fundamental model
• You are not factor neutral if you are optimizing with fundamental models
• There is a better “best hedge”
• Constrain assets with increased risk coming from the stat models
• Early warning signal on potential problem areas
• Passive/ETF/Tax Efficient Managers
• Constraint tracking error using multiple risk models
• Tighten asset bounds for assets with larger differences in risk estimates
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Recap of Recommended Approach
Step 1 - Risk Spreads
• Compare multiple risk predictions on the same graph/at the same time
• Extract current and historical maximum risk spread
• Examine different time periods – recent vs. long term
Step 2 - Risk Decomposition/Projection
• Fundamental risk model: % factor risk, % specific risk
• Statistical risk model: % factor risk, % specific risk
• Leave out the decomposition of risk at the factor level
• Projected statistical risk model: what % of risk is unexplained by fundamental
factors
Step 3 - Asset Level Analysis
• Rank positions by “% of Risk” or “% of Active Risk” or their differences
• Outliers account for a large fraction of risk budget –
are those bets intentional/worth it?
Step 4 – Seek to identify the source of this factors
• Custom Risk Model Using “Stat minus Fund” Factor – quantify return contribution
from this exposure
• Can be used to indicate which standard fundamental factors are most
impacted by the missing factor risk
• Identify other factor types that could be used to overlay into the Projected Risk
Decomposition (Alpha Factors, Macro Factors, Trading/Crowding Factors, etc.)
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Summary
• Fundamental models have articulated and captured the return swings and risk drivers to
important factors such as value, momentum, and volatility over the recent past
• However, there is additional systematic risk/return that needed explaining….
• Over this period we have seen (two separate instances) that additional systematic
volatility has existed in the market that the statistical models are properly capturing
• We articulate a framework to identify those risks, quantify the impact, and establish
intuition into the sources of those risks in a meaningful and actionable framework
• The effort and cost required to implement the proposed framework is low using Axioma
• Any portfolio manager, quant, or risk specialist tasked with managing exposures and
risks in order to maximize risk-adjusted returns should use the proposed framework in
an automated ongoing process
• Reach out to your Axioma consultant to learn more!
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Axioma Integration on FactSet
• Portfolio Construction
• Axioma Portfolio Optimizer
• Portfolio Simulation
• Portfolio Batch Optimizer
Insight article: “Moving From Research to Construction”
Part 1: Stock Selection
Part 2: Constructing Portfolios
Part 3: Simulation Performance
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Axioma Integration on FactSet
• Performance and Risk Reporting
• Portfolio Analysis
• Portfolio Dashboard
• Stress Testing
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Thank You
For additional information, contact: sales@axioma.com
Visit: www.axioma.com