Quantitative Investment Strategies: The Unintended Consequences

1. Quantitative Investment Strategies:The Unintended Consequences Vadim Zlotnikov Chief Investment Strategist and Director of Quantitative Research Sanford C. Bernstein & Co. LLC. January 8, 2008 See Disclosure Appendix of this report for important disclosures and analyst certifications.

2. Agenda • Quantitative Equity Investment • Growth in Quantitative Research—Why Now? • Factor Characteristics – The Building Blocks • Constructing Models – Critical Considerations • Quantitative Approaches – What Went Wrong? • Stability Bubble • Factor Failure • Emerging Opportunities

3. Growth in Quantitative Research—Why Now?

4. Supply-Side • Technology/databases • Markets/liquidity • Education by FoF • Academic validation Sources of Growth • Asset-liability matching to reduce volatility • Retail acceptance of pre-packaged advice(e.g., lifecycle) • Separation of alpha and beta—search for skill • Search for uncorrelated alphas • Demand for LIBOR + 300 bps returns Benefits • Disciplined • Scalable • Customizable • Easier to monitor? Acceptance of Financial Engineering Concepts Is Reshaping Our Industry

5. Broad Penetration of Quant Tools Among Money Managers is Evident; Growth Continues Effective Use of Quantitative Tools (Stock Selection Only) by Fundamental PMs and Analysts in My Company Is: (%) Source: Bernstein Survey of Analysts and Portfolio Managers 2007

6. Factor Characteristics – The Building Blocks

7. Our Approach to Identifying Quantitative Stock Selection Signals • Sources of Excess Returns: Melding of Behavioral Finance and Microeconomics • Fundamental analysis is part of signal evaluation (controversial) • Test statistical validity: data mining, spurious correlations, data errors • Time horizons, turnover, linearity, volatility shape notion of efficacy • Each factor should capture meaningful incremental information • Principal components analysis • Regressions, correlations with other factors • “Value-added” differences by stock universe

8. Stock Characteristics That Lead to Excess Returns Are Generally Well Understood, but Efficacy Is Episodic Excess Returns from Buying 20% of Top Ranked Stocks and Shorting 20% of the Worst Ranked Large-Capitalization Stocks; Monthly Rebalancing 1978–2007 2005–2007 Annualized Return Information Ratio Annualized Return Information Ratio Valuation Enterprise Value/EBIT 8.9% 0.5 8.3% 1.6 Price/Book Value 3.3 0.2 1.5 0.3 Capital Use Net Accruals 7.0% 1.0 0.5% 0.1 YoY Change in Shares 6.3 0.6 1.3 0.2 Growth Dynamics Price Momentum 7.9% 0.4 (0.7)% (0.1) Investor Sentiment Institutional Ownership Level 4.5%* 0.6* 1.6% 0.5 Other Return on Invested Capital 1.0% 0.1 2.5% 0.5 Beta (0.5) 0.0 (1.5) (0.1) *Since 1979 Source: Bernstein analysis May, 2007

9. Even the Best Individual Stock Screens Are Right Only Half the Time Share of Companies Outperforming During Next Year Companies Ranked Based on Enterprise Value-to-EBITDA (%) Highest 20% Lowest 20% Months Source: Bernstein analysis May, 2007

10. Some Screens Work Very Well In the Short Term but Generate Perverse Returns Longer Term Cumulative Returns to Long/Short Strategy Using Price Momentum to Pick Stocks 1965–2007 (%) Months Since the Trade Source: Bernstein analysis May, 2007

11. Factor Efficacy Varies Significantly By Sector Excess Returns from Buying 20% of Top-Ranked Stocks and Shorting 20% of the Worst-Ranked Stocks 1980–Late May 2007 Source: Bernstein analysis, May, 2007

12. Returns to Price Momentum are Fairly Linear 1979–June YTD Relative Annual Returns (%) Source: Bernstein Analysis.

13. Returns to Changes in Current Accruals are Non-linear 1979–June YTD Relative Annual Returns (%) Source: Bernstein Analysis.

14. Constructing Models – Critical Considerations

15. Critical Aspects of Stock-Selection Model Construction • Investment Management’s Issues • Universe/style • Time horizon/turnover/liquidity • Hit rates, persistence, IR • Fundamental analysis/transparency • Analytical Issues • Negatively correlated factors • Simplicity (“good enough”) • Avoidance of data mining/overfitting • Updating of factors/weights

16. Analysis of Quant Models: Factors Are Similar, but... Survey Results Factor Exposure of Quantitative Models TraditionalFactors Less Traditional Factors Deviation from Average Source: Bernstein survey of 25 buy-side quantitative models, where rankings for S&P 500 stocks were provided; January, 2007

17. …Completely Different Buy/Sell Recommendations - Model Construction Is Key Survey Results Degree of Overlap in the Rankings of S&P 500 StocksFirst Quintile and Fifth Quintile Based on Quantitative Models # of Stocks Ranked Degree of Overlap in Rankings Source: Bernstein survey of 25 buy-side quantitative models, where rankings for S&P 500 stocks were provided; January, 2007

18. “Controversy” Stocks Illustrate the Problem "Controversy" Stocks: Simultaneously Ranked as Q1 and Q5 by More than 25% of Models Ranked as of 6/30/06 Q1 by % of Models Q5 by % of Models Ticker Company Name DHI D R Horton Inc. 38.1% 38.1% KBH KB Home 33.3 33.3 WPI Watson Pharmaceuticals Inc. 23.8 23.8 XTO Xto Energy Inc. 23.8 23.8 MWV Meadwestvaco Corp. 23.8 23.8 WHR Whirlpool Corp. 33.3 23.8 JBL Jabil Circuit Inc. 23.8 23.8 AIG American International Group 23.8 23.8 DELL Dell Inc. 28.6 23.8 CZN Citizens Communications Co. 28.6 23.8 AAPL Apple Computer Inc. 23.8 28.6 PFG Principal Financial Group Inc. 33.3 23.8 NVLS Novellus Systems Inc. 33.3 23.8 PHM Pulte Homes Inc. 23.833.3 Average 28.0% 27.1% Source: Bernstein analysis June 30, 2006 The companies discussed are for illustrative purposes only. Any fund managed by AllianceBernstein L.P. and distributed through its subsidiaries securities or investment interests in these companies at any given time.

19. Limitations and Challenges of Quantitative Approaches • Battling the Efficient Market Hypothesis • Data mining and spurious correlations • Risk factor vs. source of excess return • Rational agents with constraints vs. behavioralists • "Knowing" When a Strategy Failed, Is Failing or Will Fail • Can't always wait for statistical significance • Sometimes don't know why it worked • Easier if you have robust expectations • Underwriting volatility (or risk) for short-term profit

20. Issues in Quantitative Research • What are the new factors? • When should they work? • Where are they most effective? • Methods for integrating signals and constructing portfolios

21. What Are the New Factors?Moving Beyond the Compustat/FactSet • Nature of investor ownership; attention • Internet as source of fundamental data,e.g., Webcrawlers • Alternative asset classes as signals, e.g., options, futures, swaps… • Third-party market share, patent and other data

22. When They Should Work? Dynamic Factor Timing • Changes in macro-economic setting; risk regimes • Seasonality/cyclicality • Technical: serial correlation vs. mean reversion • Bayesian updating • Presence of an opportunity (e.g., dispersion)

23. Where Are They Most Likely to be Effective?Universe and Factor Conditioning • Static vs. dynamic universe definition • Level of granularity: style, sector, industry, stock • Tails of the returns distribution; shorts vs. longs

24. Integration and Updating of Factor Weights • Numerous approaches for determining initial factor weights: • In-sample regressions • Principal component analysis • Optimizer of factor weights • Likewise, several approaches for updating the factor weights: • Bayesian updating • Rebalancing of the conditional universes • Desirable to match target portfolio turnover and factor efficacy duration • Integration of investment and trading alphas – explicit trading costs

25. Portfolio Construction and Factor Timing Are Primary Areas for Future Research Most Promising Research Area to Deliver Future Outperformance in U.S. Equity Market Is: (%) Source: Bernstein Survey of Analysts and Portfolio Managers 2007

26. Models Are Being Actively Modified to Incorporate Findings During the Next 12 Months, the Most Significant, Revolutionary Change to Our Quantitative Models Will Include: (%) Source: Bernstein Survey of Analysts and Portfolio Managers 2007

27. Summary • Growth in deployment of quantitative tools is likely to persist • "Commoditization" of factors means a shift in the nature of value-added • Integration of quantitative and fundamental research is still suboptimum

28. Quantitative Approaches: What Went Wrong?

29. Margin Sustainability is Key to Investment Outlook S&P 500: Price-to-Sales v s. FCF Yield Minus 10-Year Treasury 1965 Through Early-November 2007 Source: Bernstein Analysis.

30. Collective Extrapolation of Historically Lowest Volatility Drove Turmoil and Failure of Quantitative Strategies S&P 500: Market Volatility* 1874 Through End-October 2007 * Standard deviation of trailing-six-months of S&P500 monthly total returns; data smoothed over trailing-12-months. Source: Bloomberg, Ibbotson, Robert Shiller, Bernstein Analysis.

31. Volatility Shock Drove “Anti-value” Market Large-Cap Core Universe Discriminate Analysis of Top/Bottom 10% of Stocks, Past 3 Months Source: Bernstein Analysis.

32. Only Modest Misvaluations Emerged Among Large Caps Large-Cap Universe Dispersion of Book-to-Price vs. Free Cash Flow Yield* Through Late-October 2007 * Data smoothed over three-months. Source: Bernstein Analysis.

33. However, Illiquidity Premium Up Sharply Dispersion Across Stocks in Book/Price and Free Cash Flow Yield vs. Past 7 Years Small-/Mid-Cap Universe 1968-Mid-October 2007 Source: Bernstein Analysis.

34. Low Multiple Stocks with High Turnover Underperformed – Capitulation and De-leveraging are the Culprits Annualized Monthly Returns Crosstabs of Abnormal Turnover December 2006 through October 2007 Source: Bernstein Analysis.

35. Value Opportunities Emerged Among Early-Cyclicals Dispersion of Book-to-Price Financials Vs Consumer Cyclicals vs. Technology Through Early-November 2007 Source: Bernstein Analysis.

36. Factors worth monitoring to determine persistence of stability Key Risks to the Persistence of Stability

37. What is Next? Adaptive Systems at Work • Re-emergence of exploitable illiquidity premium • Greater emphasis on: • Earnings quality, stability • Relative growth • Absolute, as opposed to relative, value • Avoidance of recent mistakes, pursuit of ones from log ago • Financial, consumer leverage stable-to-down • Increases risk aversion