Quantitative Stock Selection

1. Quantitative Stock Selection James F. Page III, CFA May 2005

2. Project Summary • Why Quant Selection is Attractive • Methodology • Historical Back Testing • Model Results • Dynamic Weights / Regime Change • Benchmarks • Next Generation Models • Concluding Thoughts

3. I. Quantitative Stock Selection

4. Quant Stock Selection Premise • In aggregate, certain fundamental, expectational, and macro variables may contain valuable information in predicting stock returns • Not unlike traditional fundamental analysis, just more systematic

5. Quant Stock Selection Pros: • Anecdotal evidence suggests ~80% of stock picking is done ‘by hand’ (individuals making calls on fundamentals) • Relies heavily on talent (or luck) of individual analyst • Individuals can only process so much information (sector focus) • Human nature suggests cognitive biases likely • Market structure may perpetuate mis-pricings (Street incentives, value weighted benchmarks, short sale restrictions) • Little academic research on subject (trade rather than publish) • Evidence suggests that investors systematically over pay for ‘growth’ • Quantitative selection is scaleable

6. Quant Stock Selection Cons: • Black box nature of model • Explain approach without revealing too much information • Attribution analysis – must be able to explain performance • Protecting against common modeling errors • Credibility of simulated results • Adapting to individual client restraints

7. Quant Stock Selection Market Neutral • Generate returns from both undervalued and overvalued stocks • At present, high market valuation = low future returns • Market exposure is commodity but good stock selection is valued (higher management fees) • Low return expectations combined with geo-political environment suggests absolute return approach prudent

8. II. Methodology

9. Methodology • Hypothesize • Develop candidate list of potential factors that may assist in predicting stock returns (valuation, growth, etc.) • ‘Priors’ reduce data mining • Back Test • Decide on “universe” for testing (capitalization, index, sector, etc) • Use sorting or regressions to test individual candidate variables • FactSet’s AlphaTester currently available to Duke students • Rebalance • Periodically rebalance portfolios (monthly, annually, etc.)

10. Methodology • Analyze Results • Consider factor performance and consistency (both long and short candidates) in predicting returns balanced against turnover • Select most promising factors for inclusion in the model • Weight • Once individual factors selected must decide on weights for final model by either: • ‘Eye balling’ best factors and assigning weights for a scoring model • Pushing individual factor portfolios into a mean-variance optimizer

11. III. Historical Back Testing

12. Historical Back Testing • Access to reasonably accurate historical data is costly • FactSet’s AlphaTester is currently available to Duke students • Two approaches common in practice • Regression of factors on security returns (Panel, etc.) • Sorting universe into fractiles based on factor characteristics (AlphaTester) • Must protect against common modeling errors • Survivorship bias • Information / reporting lags • Data mining • Inaccuracies in data • Credibility of simulated returns is critical

13. Historical Back Testing Term 3 Model Discredited • Errors in Historical Returns • Scrub Example.xls • Survivorship Bias • Difficult to rule out unless you spend a lot of time examining results • Fractile Misspecification • MSFT grouped in F1 Div Yield for 85-04 because of Special Dividend • Betas not believable • Subject to similar errors as returns information • Makes market neutral simulation difficult • Combing factors into comprehensive model increases complexity

14. Historical Back Testing To Mitigate Potential Errors: • Universe Selection is critical component • Market Cap weighted • Adds to turnover (98-00) • Unstable sector allocations • Less undervalued firms to buy • Revenue weighted • Sector bias • Less overvalued firms to sell • Actual Indices (Preferred method) • Limit universe to actual benchmark • Limit survivorship bias • Historical indices available (but option not turned on for Duke) • Greatly enhance credibility – look to acquire for next year’s class

15. Historical Back Testing To Mitigate Potential Errors: • Factor Syntax • If you do not get this right – data is worthless (lots of opportunities to get it wrong) • Consider consolidating our “approved” syntax for future students as starting point • Expectational (instead of accounting/fundamental) produced significantly fewer errors • Survivorship Bias • Selecting “Research Companies” does not protect without: • Appropriate Syntax on Factors • Correct specification of Universe • Sanity checks on early period companies • # of NA companies can be signal • Errors • You must clean historical data • Consider median returns as back of envelope option

16. Historical Back Testing Recommendations: • Use historical indices as universe • S&P 500 • Barra 1000 • Start with “approved” list of factor syntax • Clean historical results (particularly returns) • Do not rely on betas to construct market neutral portfolio • Research ways to limit reliance on AlphaTester • Look for other data providers – ask managers what they use • Interface with CompuStat/IBES directly? • Once comfortable with model, begin sorting real time ASAP

17. IV. Model Results

18. Model Results Desired Universe: S&P 500 Why: • ‘Considered’ to be highly efficient • Value weighted index suggests low hanging fruit • Historical data for testing is plentiful and reasonably accurate • Highly liquid (market impact costs and borrow) • Very scaleable because of market capitalizations Actual Universe: • First choose US Companies with highest sales (~ 500) • Had to switch to Market Cap because of data limitations

19. Model Results Universe Comments: • Unstable during bubble period (1998-2000) • Less undervalued firms to buy (but more overvalued firms to sell) • Sector allocations float with market sentiment Other: • Rebalanced “official” results annually due to time consuming nature of “cleaning returns” • Equal number of companies in each bucket • Equal weight returns • Did not impose sector constraints • Included two groups of Factors – Fundamental and Expectational • Actively looking for “Quality” factor to add to the model • Assume “beta” exposure is equal is both portfolios – probably conservative Results seem “too good” – further ‘cleaning’ necessary

20. Model Results Individual Factor Performance Monthly Statistics 1989 – 2004 Long Factors correlated with Value and visa versa View Portfolios

21. Model Results Fixed Weighting Scheme

22. Model Results Scoring Model Heat Map

23. Model Results Summary Statistics

24. V. Dynamic Weights / Regime Change

25. Dynamic Weights / Regime Change • A factor’s effectiveness may vary in different states of nature (PE ratios impacted by inflation) • Certain market / macro conditions may favor growth or value (value was dog in late 1990s) • Dynamic factor weights allow model to capitalize on conditional information • Few managers currently employ dynamic weighting schemes • This area “is the Holy Grail” of Quant Strategies

26. Dynamic Weights / Regime Change Forecasting Regime Change • Inflection point for style (growth or value) relative performance • Used S&P 500 Barra Value and Growth Indices as Proxies • Examined macro economic variables that might assist in forecasting these inflection points • Two variables demonstrated “promise” in forecasting style relative performances over the following year

27. Dynamic Weights / Regime Change Regime Change – Factor 1

28. Dynamic Weights / Regime Change Regime Change – Factor 2

29. Dynamic Weights / Regime Change The Same Can Be Applied to View Portfolios Expectational Factor #2 and Regime Change Factor #1: Prediction of Long outperforming Short

30. Dynamic Weights / Regime Change The Same Can Be Applied to View Portfolios Expectational Factor #2 and Regime Change Factor #2: Prediction of Long Outperforming Short

31. VI. Benchmarks

32. Benchmarks Value or Equal Weight? • Since 1990, EWI has outperformed by 177 basis points • Turnover for EWI is 6x which begs the question … • Can we separate turnover between model signals and weighting scheme?

33. Benchmarks Value or Equal Weight? • Significant Implications for Sector Weights / Tracking Error

34. Benchmarks Value or Equal Weight? • Correlations drift through time – implications for tracking error

35. Benchmarks Value or Equal Weight? • EWI had positive loading on the size premium • EWI has significant exposure to the value premium Fama-French Risk Factor Exposures Source: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

36. Benchmarks Value or Equal Weight? • EWI has 82% correlation with 500 / Barra Growth • EWI has 96% correlation with 500 / Barra Value • Further proof of value tilt

37. Benchmarks Value or Equal Weight? • Obvious Pros and Cons to both • EWI benchmark will make returns look less impressive, but help explain turnover • EWI may be a better match for style • Provide more stable weighting for sector allocations • Equal weight is newer idea – historical data is limited • If possible, choice should match weighting scheme of portfolio

38. VII. Next Generation Models

39. Next Generation Models • Refining Dynamic Factor Weights • Preferably done outside of FactSet • Migration Tracking • May contain information to enhance returns or limit turnover

40. Next Generation Models Modified Versions of S&P 500 Model • Separate Models for Sector and Stock Selection • More Conservative • More positions • Limited tracking error • More Aggressive • Directional • Less positions • Leverage Other Domestic Models • S&P Mid-Cap 400 / Russell 2000 International Models • Developed / Emerging markets

41. VIII. Concluding Thoughts

42. Concluding Thoughts Theoretical • How long will excess returns exist • How to stay ‘ahead of the curve’ Implementation • Cost of data • Credibility of simulation • Returns during first 12 – 24 months • Balance between turnover and model signals

43. Concluding Thoughts Overall • Quantitative Stock Selection Appealing • Outperformance Seems Possible • Long/Short Consistent with Absolute Return Approach

44. Bio James F. Page III Jimmy became interested in quantitative stock selection during Campbell Harvey’s Global Asset Allocation and Stock Selection class and a follow-up course dedicated to quantitative stock selection. He received his Bachelor of Science degree from the University of Florida and will receive his MBA from Duke University’s Fuqua School of Business in May 2005. Prior to enrolling at Duke, he spent four years in the Equity Research Department of Raymond James & Associates in St. Petersburg, FL. He is also a CFA charter holder.