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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