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INVESTOR PRESENTATION Systematic Fundamental Equity Models New Directions Prepared for FactSet Symposium 2015
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Differing views on the state of quant equity Are fundamental models still valuable or do we all need to move to scraping big
data, NLP and machine learning?
Camp1: If you find a factor that has a great story, great data, and that no one else is on to yet, that's alpha. If you implement the big four better than someone else, that's alpha. Cliff Asness Camp2: scientific investing, as a superior sub-set of quant, should focus on identifying new investment ideas and continually improving their implementationHowever, a new idea should not be confused with just another signal that captures a value premium in a slightly different way to all the others --- Ron Kahn
Fundamentals, attention and sentiment still drive relative values. Good insights and
careful modelling can be used to extract alpha from fundamental data Skill modelling, conditioning and contextual analysis Better models using customer/competitor relationships Extending models to industry and country returns and dynamic signal timing
Systematic Fundamental Models: New Directions
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Rise and Fall of Accruals Anomaly: Live vs. Backtest
Live Trading at AG
Backtest from 2002
Source: AG. US All-Cap universe monthly decile spreads of returns adjusted for systematic risk factors.
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Accruals in Europe: Live vs. Backtest
Live Trading
Source: AG. EU All-Cap universe monthly decile spreads of returns adjusted for systematic risk factors.
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Recent papers (Green, Hand and Zhang (2013), Mclean and Pontiff (2014), Harvey, Liu and Zhu (2015)
Decay out of sample ~25% Decay post publication on SSRN ~30%
Significant evidence of trading and decay of predictive ability, however residual predictive ability remains
Academic Research on Idea/Signal Decay
Output rate for new research has increased substantially over the last decade, as has data mining risk
Behavioral sources that generate mispricing are sticky (Barber and Odean, 2011)
Green, Hand and Zhang (2013), The Supraview of Return Predictive Signals.
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Basic linear factor model (Rosenberg/Grinold) framework Includes country, industry and other systematic factors
Framework for Discussion
Typical signal processing setup
1. Winsorize and standardize the fundamental ratio 2. Decompose into country, industry, factor projections and residual component
(neutralize) 3. Rescale residual to stdev=1 and evaluate residual as return predictor via tile
spreads and FM factor returns
titj
k
jijtti frfR ,,
1,, +=
=
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Even for the well know signals from academia, careful implementation can improve
the signal to noise ratio and IR relative to generic versions.
Example: Change in asset turnover () = 4
=
=3
A common fundamental signal, and a component of Piotroskis F score Common implementation pitfall: it suffers from being scaled by the time-series
volatility of asset turnover Solution: Re-scaling the signal by the inverse of its asset turnover volatility
= 120 ()=19
= 1 ( )
Implementation of Alpha: Example of
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Change in Asset Turnover Pre/Post Volatility Scaling
Source: AG. US all-cap universe monthly decile spreads of returns adjusted for systematic risk factors.
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What is Skill Modelling?
= : asset volatility z: normalized forecasting score IC: information coefficient. Usually assumed to be constant
Now we expand this formula to incorporate ideas of contextual analysis:
= () () : a function of fundamental characteristics = (1, 2, , )
What is in :
Growth and profitability proxies Earnings predictability and information uncertainty Value/growth life cycle proxies Country, industry and size
Why Skill Modeling? Better signal attribution and ease of diagnosing out of sample performance Parsimonious models generate non-linear effects and dynamic variation in effective
signal weights
Skill Modeling As a Framework for Improving Signals
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Consider a simple measurement of topline growth:
= ( 1)
Intuitively, we include asset growth as a conditioning variable Recent top-line trends matter most for growth investors. Holders of lower PE
stocks care more about earnings, i.e. sales*margin
Skill Modeling: Example R
ealiz
ed IC
Asset Growth Group Rank
Realized 3-Month IC
IC F
unct
ion
Asset Growth Score (Normalized)
Sigmoid Weighting Function
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Performance: Sales Growth Pre/Post Skill Model
Source: AG. US all-cap universe monthly decile spreads of returns adjusted for systematic risk factors.
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Performance: US Accruals Pre/Post Skill Model
Source: AG. US all-cap universe monthly decile spreads of returns adjusted for systematic risk factors.
Asset and sales growth conditioning added
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Customer sales
growth
Firm specific sales
growth
Competitor trend in sales
growth
Combined Model
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Using Customer/Competitor Relationships to Enhance Models
Customer and competitor relationship data be utilized for more focused models and to capture lead-lag relationships
Customer sales growth positively predicts returns, as does competitors sales growth acceleration
Simple linear combinations improve on industry-relative measures, and skill modeling can lead to further improvement
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Relationship Based Sales Growth
Source: AG, FactSet. US Revere universe monthly decile spreads of returns adjusted for systematic risk factors.
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Competitor Neutralized EBITDA/EV
Source: AG. US all-cap universe monthly decile spreads of returns adjusted for systematic risk factors.
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Bottom up analysis for many fundamental ratios can generate useful signals for predicting industry and country returns
Low breadth and IR, but attractive return spreads Statistical analysis is less conclusive
Weak significance requires more conviction in the idea
Weighted FM regressions with industry factor returns as the dependent variable is a useful framework
Differs meaningfully from predicting industry cap-weighted returns Down-weight more variable industry means/coefficients De-emphasize thin industries in the analysis
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Industry and Country Predictions
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Becoming the norm rather than the exception, at least in marketing materials Value spreads are intuitive, but the empirics arent robust
Adjusting for differences in forecast earnings growth is important. When growth expectations are less diverse in the cross section, then value
spreads should be tighter. Time series scoring can help
Other approaches Structured empirical models, i.e. Kalman filters Machine learning not well suited to intermediate or low frequency cycles and
longer forecast horizons, but potentially effective where there is good time-series breadth
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Dynamic Weighting of Signals
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Fundamentals, and their effects on attention and sentiment, remain the key driver of relative price discovery for equities
Generic models from academic working papers experience decay out-of-sample, but still provide valuable starting points for investors who can combine investment expertise with sound empirical modeling
Our skill modeling framework allows for significant improvements in performance by conditioning forecasts on important characteristics
Company relationship databases open up new possibilities for better models of relative
fundamental trends
Relative to machine learning approaches, structured fundamental analysis is far less likely to result in over-fitting
The added complexity is motivated by fundamental insights Attribution can easily separate generic factor performance from the conditional skill
components
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Summary
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