Quantitative Investment Strategies: The Unintended Consequences

Quantitative Investment Strategies:The Unintended Consequences

Vadim Zlotnikov

Chief Investment Strategist andDirector of Quantitative ResearchSanford C. Bernstein & Co. LLC.

January 8, 2008

See Disclosure Appendix of this report for important disclosures and analyst certifications.

Bernstein Research2

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

Bernstein Research3

Growth in Quantitative Research—Why Now?

Bernstein Research4

Acceptance of Financial Engineering Concepts Is Reshaping Our Industry

Technology/databases

Markets/liquidity

Education by FoF

Academic validation

Supply-Side

Disciplined

Scalable

Customizable

Easier to monitor?

Benefits

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

Sources of Growth

Bernstein Research5

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:

0

5

10

15

20

25

30

35

ExtensivelyUsed by atLeast 50%

Small, butEnthusiastic

Minority

Experimental Frequent, butRarely

Effective

Non-Existentor Not

Relevant

Last Year This Year

(%)

Source: Bernstein Survey of Analysts and Portfolio Managers 2007

Bernstein Research6

Factor Characteristics – The Building Blocks

Bernstein Research7

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

Bernstein Research8

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

Valuation Enterprise Value/EBIT 8.9% 0.5 8.3% 1.6Price/Book Value 3.3 0.2 1.5 0.3

Capital Use Net Accruals 7.0% 1.0 0.5% 0.1YoY 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% 0.5Beta (0.5) 0.0 (1.5) (0.1)

*Since 1979Source: Bernstein analysis May, 2007

1978–2007

AnnualizedReturn

AnnualizedReturn

InformationRatio

InformationRatio

2005–2007

Bernstein Research9

Even the Best Individual Stock Screens Are Right Only Half the Time

Source: Bernstein analysis May, 2007

Share of Companies Outperforming During Next YearCompanies Ranked Based on Enterprise Value-to-EBITDA

35

40

45

50

55

1 3 12 24 36

(%)

Highest 20%

Lowest 20%

Months

Bernstein Research10

Cumulative Returns to Long/Short Strategy Using Price Momentum to Pick Stocks

1965–2007

Some Screens Work Very Well In the Short Term but Generate Perverse Returns Longer Term

(1)

0

1

2

3

4

5

6

0 4 7 10 13 16 19 22 25 28

Months Since the Trade

(%)

Source: Bernstein analysis May, 2007


Factor Efficacy Varies Significantly By Sector

Excess Returns from Buying 20% of Top-Ranked Stocksand Shorting 20% of the Worst-Ranked Stocks

1980–Late May 2007

Source: Bernstein analysis, May, 2007

0.0% 1.3%

5.8%

(3.1)%(2.2)%

5.9%6.7%

(1.7)%

15.7%

6.1%

(4.8)%(4.5)%

Transport Consumer Staples Technology Telecom

Price/Book

Asset Turnover

9-Month Price Momentum


(8)

(6)

(4)

(2)

0

2

4

6

8

77% 60% 48% 39% 31% 24% 18% 11% 5% (3)% (12)% (25)%

Price Momentum (%)

Returns to Price Momentum are Fairly Linear

1979–June YTDRelative Annual

Returns (%)

Source: Bernstein Analysis.


(10)

(6)

(2)

2

6

10

(6.9)% (3.2)% (1.5)% (0.5)% 0.3% 1.1% 2.0% 3.3% 5.4% 9.7%

Year-Over-Year Change in Net Current Assets (%)

Returns to Changes in Current Accruals are Non-linear

1979–June YTDRelative Annual

Returns (%)



Constructing Models – Critical Considerations


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


Analysis of Quant Models: Factors Are Similar, but...

Source: Bernstein survey of 25 buy-side quantitative models, where rankings for S&P 500 stocks were provided; January, 2007

Survey ResultsFactor Exposure of Quantitative Models

0.0 0.5 1.0 1.5 2.0 2.5

Price-to-Normalized Earnings Ratio

LBO Value

Total Cash-to-Market Capitalization

P/E-to-Long-Term Growth Forecast

Average 9-Month Price Trend

Total Yield

Enterprise Value-to-EBIT

Price-to-Gross Cash Flow

Deviation from Average

TraditionalFactors

LessTraditional

Factors


…Completely Different Buy/Sell Recommendations - Model Construction Is Key

Survey ResultsDegree of Overlap in the Rankings of S&P 500 Stocks

First Quintile and Fifth Quintile Based on Quantitative Models

Degree of Overlap in Rankings

# of Stocks Ranked

369

108

23

368

102

30

0

100

200

300

400

500

Low <20% Medium 21-40% High >40%

Q1 Q5

Source: Bernstein survey of 25 buy-side quantitative models, where rankings for S&P 500 stocks were provided; January, 2007


“Controversy” Stocks Illustrate the Problem

"Controversy" Stocks: Simultaneously Rankedas Q1 and Q5 by More than 25% of Models

Ranked as of 6/30/06

DHI D R Horton Inc. 38.1% 38.1%KBH KB Home 33.3 33.3WPI Watson Pharmaceuticals Inc. 23.8 23.8XTO Xto Energy Inc. 23.8 23.8MWV Meadwestvaco Corp. 23.8 23.8WHR Whirlpool Corp. 33.3 23.8JBL Jabil Circuit Inc. 23.8 23.8AIG American International Group 23.8 23.8DELL Dell Inc. 28.6 23.8CZN Citizens Communications Co. 28.6 23.8AAPL Apple Computer Inc. 23.8 28.6PFG Principal Financial Group Inc. 33.3 23.8NVLS Novellus Systems Inc. 33.3 23.8PHM Pulte Homes Inc. 23.8 33.3Average 28.0% 27.1%

Ticker Company NameQ1 by %of Models

Q5 by %of Models

Source: Bernstein analysis June 30, 2006The 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.


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


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


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


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)


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


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


0

10

20

30

40

Signals Basedon AlternativeAsset Classes

StochasticFactor Weights:

LearningModels

Varying FactorWeights:

DeterministicModels

PortfolioConstruction

Other

This Year Last Year

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:

(%)



Models Are Being Actively Modified to Incorporate Findings

During the Next 12 Months, the Most Significant, Revolutionary Change to Our Quantitative Models Will Include:

0

10

20

30

No MajorChangesPlanned

Risk Models,Portfolio

Construction

Dynamic FactorWeight

Allocation

More GranularModels

Other

This Year

Last Year

(%)



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


Quantitative Approaches: What Went Wrong?


Margin Sustainability is Key to Investment Outlook

S&P 500: Price-to-Sales v s. FCF Yield Minus 10-Year Treasury1965 Through Early-November 2007



Collective Extrapolation of Historically Lowest Volatility Drove Turmoil and Failure of Quantitative Strategies

S&P 500: Market Volatility*1874 Through End-October 2007

Source: Bloomberg, Ibbotson, Robert Shiller, Bernstein Analysis.

* Standard deviation of trailing-six-months of S&P500 monthly total returns; data smoothed over trailing-12-months.


Volatility Shock Drove “Anti-value” Market

Large-Cap Core Universe Discriminate Analysis of Top/Bottom 10% of Stocks,

Past 3 Months


Past Three MonthsDiscriminant Q1-Q5

Function Partial Avg. MonthlyFactor Coefficient* R-Square SpreadBook-to-Price - 17.3% -4.2%Market Cap (Log) + 7.6% 3.1%EBIT-to-Enterprise Value - 5.9% -3.9%Monthly Earnings Revisions + 2.4% 2.7%Five-Year Sales Stability + 2.6% 3.0%YoY Sales Growth + 1.5% 3.5%ROE Volatility** + 1.4% -0.5%* "+" sign represent that factor correlates positively with performance; "-" sign represents that factor correlates negative with performance** top quintile is lowest roe volatility


Only Modest Misvaluations Emerged Among Large Caps

Large-Cap UniverseDispersion of Book-to-Price vs. Free Cash Flow Yield*

Through Late-October 2007


* Data smoothed over three-months.


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



Low Multiple Stocks with High Turnover Underperformed – Capitulation and De-leveraging are the Culprits

Annualized Monthly ReturnsCrosstabs of Abnormal Turnover

December 2006 through October 2007


Enterprise ValueEnterprise Value-to-EBIT -to-EBIT as of

Lowest Q2 Q3 Q4 Highest All Q1:07 Q3:07

Lowest -4.5% -10.2% 8.1% 8.5% 10.4% 3.4% 13.3 13.1 Abnormal Q2 6.2% 1.0% 2.2% 13.0% 22.3% 8.9% 12.2 13.2 Turnover Q3 -1.0% -2.6% -2.8% -0.8% 6.5% -0.6% 11.7 11.7

Q4 -7.3% -10.9% -3.7% -0.1% 2.9% -4.5% 11.9 11.9

Highest -13.2% -0.1% -6.4% 1.9% -0.1% -4.2% 11.7 11.3 All -4.7% -4.8% -1.0% 4.4% 8.7% 0.4% 12.1 12.1


Value Opportunities Emerged Among Early-Cyclicals

Dispersion of Book-to-PriceFinancials Vs Consumer Cyclicals vs. Technology

Through Early-November 2007



Factors worth monitoring to determine persistence of stability

Key Risks to the Persistence of Stability

Risk Current Situation Variables to Monitor Potential Path to Instability1. Contagion from subprime Delinquencies rising Housing inventory rise Drop in housing wealth leads

Credit tightening Contagion to prime to accelerating defaults andAdverse impact on housing credit; state-level data lower consumer spending

2. Slower foreign inflows into US/Euro rate narrowed and Strengthening in Japanese Higher long-term ratesUS fixed income instruments contributed to declining economy and yields exacerbate housing problemsas dollar weakens dollar on long-bond and reduce Fed's flexibility

3. Wage inflation in Asia and Chinese wages rising 15-20% Size of margin pressureRaises doubt as to sustainability

weaker dollar Strength in Euro, Rupee on key outsourcers and of historically peak margins;Rise in prices on goods importers reignites inflation concerns

4. Dysfunctional policy for Every Democratic Signs of protectionist Market sell-off as investorsincome disparity candidate favors higher sentiment revival; attempt to lock-in capital gains

income, capital gains and Congressional support for at lower rates; negative fordividend tax rate tax rate increases high dividend payers


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

Documents

Quantitative Investment Strategies: The Unintended Consequences