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  • Maybe It Really Is Different This Time

    Presentation to: Nomura Quantitative Investment Strategies ConferenceJune 16, 2009

    Robert C. JonesBob.Jones@GS.com

  • 1

    Agenda

    A Review of August, 2007

    B The Stickers versus the Adapters

    C The Stickers case:

    Long-term perspective: HML and WML

    Normal ebb and flow

    D The Adapters case:

    Quant crowding

    Adjusting for trading and liquidity

    E The future of quantitative equity management

  • 2

    Sudden and dramatic underperformance of previously uncorrelated common quant factorsFactor performance in August 2007 scaled to 1% daily volatility1

    US:

    Revisions

    MomentumValueAccruals

    -60%-50%-40%-30%-20%-10%

    0%10%20%

    1 3 7 9 13 15 17 21 23 27 29 31

    August 2007

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Source: QIS.

  • 3

    Sudden and dramatic underperformance of previously uncorrelated common quant factorsFactor performance in August 2007 scaled to 1% daily volatility1

    Japan:

    Revisions

    Momentum

    Value

    Accruals

    -40%

    -30%

    -20%

    -10%

    0%

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    20%

    1 3 7 9 13 15 17 21 23 27 29 31

    August 2007

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Source: QIS.

  • 4

    Sudden and dramatic underperformance of previously uncorrelated common quant factorsFactor performance in August 2007 scaled to 1% daily volatility1

    UK:

    RevisionsMomentum

    ValueAccruals

    -20%

    -15%

    -10%

    -5%

    0%

    5%

    1 3 7 9 13 15 17 21 23 28 30

    August 2007

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Source: QIS.

  • 5

    Sudden and dramatic underperformance of previously uncorrelated common quant factorsFactor performance in August 2007 scaled to 1% daily volatility1

    Continental Europe:

    RevisionsMomentum

    ValueAccruals

    -25%

    -20%

    -15%

    -10%

    -5%

    0%

    5%

    1 3 7 9 13 15 17 21 23 27 29 31

    August 2007

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Source: QIS.

  • 6

    The Stickers vs. the Adapters

    The Stickers believe this is part of the normal volatility of such strategies

    Long-term perspective: results for HML and WML not outside historicalexperience.

    Investors who stick to their process will end up amply rewarded

    The Adapters believe that quant crowding has fundamentally changed the nature of these factors

    Likely to be more volatile and offer lower returns going forward

    Need to adapt your process if you want to add value consistently in the future

    Who is right?

    Source: Goldman Sachs Asset Management.

  • 7

    Historical perspective HML1Recent US results for HML do not seem extraordinary relative to historical trends

    Cumulative HML daily return, July 1926 February 2009

    Source: Kenneth French Data Library. 1 HML stands for high book-to-price minus low book-to-price as defined by the Kenneth French Data Library. The Fama/French factors are constructed using the 6 value-weight portfolios formed on size and book-to-market. HML is the average return on the two value portfolios minus the average return on the two growth portfolios. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. The time period shown above (1926 2009) was selected due to data availability.

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    Historical perspective WML1Recent US results for WML are also consistent with historical trends

    Cumulative WML daily return, January 1927 February 2009

    Source: Kenneth French Data Library. 1 WML stands for winners minus losers as defined by the Kenneth French Data Library. The Fama/French factors are constructed using the 6 value-weight portfolios formed on size and prior (2 -12) returns to construct Momentum. WML is the average return on the two high prior return portfolios minus the average return on the two low prior return portfolios. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. The time period shown above (1927 2009) was selected due to data availability.

  • 9

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    Quant crowding

    Leading up to 2007, quantitative investing had become an increasing percentage of the overall market.

    When quantitative investors were forced to unwind their positions simultaneously in August 2007, it created the dramatic moves that were synchronized across factors and markets.

    There is always the potential for more risk and higher correlations for quant factors whenever quant crowding is evident.

    Total Quant AUM as a percentage of the Russell 3000 Market Cap, March 1999 December 2008

    For illustrative purposes only. The chart shows selected active quantitative manager assets as a percentage of the Russell 3000 Index. The universe of managers examined were those institutions filing holdings data with 13Fs which were picked up in the Thomson ownership database. We screened for quantitative managers who we consider our main competitors based on investment philosophy and other factors we deemed relevant.

  • 10

    Historical perspective Value5-day standardized returns (Dec-90 to Dec-08)

    Looking at intra-month data, results for US Value in August 2007 were well outside of historical norms.

    These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Please see additional disclosures. Source: Goldman Sachs Asset Management. The OTP is an optimized, unconstrained long-short portfolio which does not take transactions costs into account. It is not meant to reflect performance achieved in any realized accounts, but rather to measure the success of the model's predictions. The returns presented herein are gross and do not reflect the deduction of investment advisory fees, which will reduce returns. These performance results for optimal tilt portfolios are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. With regard to our simulation methodology, we generate risk and return estimates for all assets in our research database using our proprietary model. Simulated performance results do not reflect actual trading and have inherent limitations. Please see additional disclosures. We standardize the 5-day OTP returns by dividing by the rolling 90 day volatility. The time period shown above (1990 2008) was selected due to data availability.

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    Historical perspective Momentum5-day standardized returns (Dec-90 to Dec-08)

    Daily US data for Momentum also shows extreme results

    These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. Please see additional disclosures. Source: Goldman Sachs Asset Management. The OTP is an optimized, unconstrained long-short portfolio which does not take transactions costs into account. It is not meant to reflect performance achieved in any realized accounts, but rather to measure the success of the model's predictions. The returns presented herein are gross and do not reflect the deduction of investment advisory fees, which will reduce returns. These returns are lagged by 20 days for illustrative purposes and are generated by creating an optimal single-factor momentum portfolio and looking at the subsequent returns to the Momentum portfolio 20 days later. We have used returns for the 20 day lagged Momentum portfolio in the above chart to account for short term price reversals. These performance results for optimal tilt portfolios are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. With regard to our simulation methodology, we generate risk and return estimates for all assets in our research database using our proprietary model. Simulated performance results do not reflect actual trading and have inherent limitations. Please see additional disclosures. We standardize the 5-day OTP returns by dividing by the rolling 90 day volatility. The time period shown above (1990 2008) was selected due to data availability.

  • 12

    A closer look ValueWhen we look only at liquid stocks, the value effect is more muted and exhibits a flat trend for 20 years

    Cumulative daily return of Value; largest US stocks by market capitalization versus full universe, July 1926 December 2008

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    Large cap universe

    Source: Kenneth French Data Library. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.htmlThe large cap universe portfolio shown above is created monthly, where each month the investment universe is reset to the current largest 20% of stocks by market capitalization. The large cap universe portfolio is compared to the full HML universe portfolio, as defined on page 7. The time period shown above (1926 2008) was selected due to data availability.

  • 13

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    A closer look- MomentumWhen we look only at liquid stocks, the momentum effect is more muted for 20 years

    Cumulative daily return of Momentum; largest US stocks by market capitalization versus full universe, July 1926 December 2008

    Full universe

    Large cap universe

    Source: Kenneth French Data Library. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.htmlThe large cap universe portfolio shown above is created monthly, where each month the investment universe is reset to the current largest 20% of stocks by market capitalization. The large cap universe portfolio is compared to the full WML universe portfolio, as defined on page 8. The time period shown above (1926 2008) was selected due to data availability.

  • 14

    The impact of transaction costs - Value

    When we adjust our Value portfolio to deduct (assumed) trading costs, results look much worse, especially at higher levels of AUM

    Cumulative daily returns to optimal Value portfolio, February, 1987 December, 2008

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    Value portfolio, high AUM, net of t-costs

    Please refer to page 10 for the backtesting methodology used in the Value (B / P) portfolios shown above. We define low assets under management (AUM) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 - 2008) was selected due to data availability.

  • 15

    The impact of transactions costs - Momentum

    When we adjust our Momentum portfolio to deduct (assumed) trading costs, results look much worse, especially at higher levels of AUM

    Cumulative daily returns to optimal Momentum portfolio, February, 1987 December, 2008

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    Momentum portfolio, high AUM, net of t-costs

    Please refer to page 10 for the backtesting methodology used in the Momentum (Price Momentum: 2 12 month return) portfolios shown above. We define low AUM (assets under management) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 - 2008) was selected due to data availability.

  • 16

    Considering t-costs during optimization - Value

    When we add t-costs to the objective function, turnover goes down and net returns improve, but gross returns are much weaker than in the no t-cost case

    Cumulative daily return to t-cost optimized Value portfolios, February 1987 December 2008

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    High AUM, net of t-costs

    Information RatiosFeb 1987 Dec 2008

    Please refer to page 10 for the backtesting methodology used in the Value (B / P) portfolios shown above. We define low assets under management (AUM) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 - 2008) was selected due to data availability.

    Gross of T-cost

    Net of T-cost

    High AUM 0.27 0.08Low AUM 0.70 0.51

  • 17

    Considering t-costs during optimization - Momentum

    When we add t-costs to the objective function, turnover goes down and net returns improve, but gross returns are much weaker than in the no t-cost case

    Cumulative daily return to t-cost optimized Momentum portfolio, February 1987 December 2008

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    High AUM, net of t-costs

    Information RatiosFeb 1987 Dec 2008

    Please refer to page 10 for the backtesting methodology used in the Momentum (Price Momentum: 2 12 month return) portfolios shown above. We define low AUM (assets under management) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 - 2008) was selected due to data availability.

    Gross of T-cost

    Net of T-cost

    High AUM 0.73 0.42Low AUM 1.63 1.34

  • 18

    Exposure to current and legacy factors - Value

    When we add t-costs to the optimizer, our Value portfolio has exposures to both current and legacy (lagged) factor values

    Source: Goldman Sachs Asset Management. Partial correlation measures the degree of association between the lagged single-factor Value portfolio and the Value optimal tilt portfolio, with the effect of a set of controlling random variables removed.Please refer to page 10 for the backtesting methodology used in the single-factor Value (B / P) portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. We define low assets under management (AUM) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 - 2008) was selected due to data availability.

    Partial correlations of US optimal and lagged Value portfolios, February 1987 December 2008

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    Optimal portfolio net of t-costs at $2 billion AUM

  • 19

    Exposure to current and legacy factors - Momentum

    When we add t-costs to the optimizer, our Momentum portfolio has exposures to both current and legacy (lagged) factor values

    Source: Goldman Sachs Asset Management. Partial correlation measures the degree of association between the lagged single-factor Momentum portfolio and the Momentum optimal tilt portfolio, with the effect of a set of controlling random variables removed.Please refer to page 10 for the backtesting methodology used in the single-factor Momentum (Price Momentum: 2 12 month return) portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. We define low assets under management (AUM) as an initial portfolio size of $10mm and high AUM as an initial portfolio size of $2bn. These results are gross and net of transactions costs, as defined by our proprietary transaction cost model. The time period shown above (1987 -2008) was selected due to data availability.

    Partial correlations of US optimal and lagged Momentum portfolios, February 1987 December 2008

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    Optimal portfolio net of t-costs at $2 billion AUM

  • 20

    Factor decay - Value

    The factor decay graphs show how the returns to legacy portfolios decline relative to the returns of current portfolios

    Recent results show lower means and faster decays than older results, a sign of quant crowding (i.e., faster response to new information)

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    Source: Goldman Sachs Asset Management. Please refer to page 10 for the backtesting methodology used in the single-factor Value (B / P) portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. These results are gross of transaction costs. The time series has been split into two equal time periods to show how factor decay has changed for the more recent time period relative to the historical time period. The time period shown above (1985 - 2008) was selected due to data availability.

  • 21

    Factor decay - Momentum

    The factor decay graphs show how the returns to legacy portfolios decline relative to the returns of current portfolios

    Recent results show lower means and factor decays than older results, a sign of quant crowding (i.e., faster response to new information)

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    Source: Goldman Sachs Asset Management. Please refer to page 10 for the backtesting methodology used in the single-factor Momentum (Price Momentum: 2 12 month return) portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. These results are gross of transaction costs. The time series has been split into two equal time periods to show how factor decay has changed for the more recent time period relative to the historical time period. The time period shown above (1985 - 2008) was selected due to data availability.

  • 22

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    Decay rates for other popular factors

    Other well-known and popular quant factors have also seen their returns decline and decay rates speed up in recent yearsEarnings Surprise: Revisions:

    Accruals: Reversal:

    Source: Goldman Sachs Asset Management. Please refer to page 10 for the backtesting methodology used in the single-factor portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. These results are gross of transaction costs. The time series has been split into two equal time periods to show how factor decay has changed for the more recent time period relative to the historical time period. The time period shown above (1985 - 2008) was selected due to data availability. We define Earnings Surprise as abnormal returns around earnings announcements; Revisions as analystsearnings estimate revisions; Accruals as balance sheet accounting accruals; Reversal as one month change in stock price.

  • 23

    So now what?

    When properly adjusted for liquidity and trading costs, popular quant factors have indeed become less effective recently.

    The Stickers solution:

    Live with it

    Sharpe ratios still attractive

    The Adapters solution:

    Develop proprietary factors

    Dynamic allocation to popular factors

    Incorporate human judgment, but chastened

    Source: Goldman Sachs Asset Management.

  • 24

    Sources of alpha

    Alpha can only be derived when prices dont accurately reflect all public information (i.e., price fair value).

    This can only happen when investors over- or under-react to information.

    Thus, the Value effect is really an over-reaction effect; Momentum is an under-reaction effect.

    Investors tend over-react to information that confirms their prior beliefs and under-react to information that contradicts those beliefs.

    We can develop proprietary factors by looking for other situations where investors systematically over- or under-react to information.

    Source: Goldman Sachs Asset Management.

  • 25

    Factor results in August 2007Scaled to 1% daily volatility1

    More well-known factorsMostly proprietary factors

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    Proprietary factors

    US:

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. The factor categorizations shown above were determined based on what QIS perceives to be the level of crowding and popularity of quantitative factors. The most common factors are included in the more well-known category, while the most unique factors included in the mostly proprietary factors category. Source: QIS

  • 26

    Factor results in August 2007Scaled to 1% daily volatility1

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    Japan:

    More well-known factorsMostly proprietary factors Proprietary factors

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. The factor categorizations shown above were determined based on what QIS perceives to be the level of crowding and popularity of quantitative factors. The most common factors are included in the more well-known category, while the most unique factors included in the mostly proprietary factors category. Source: QIS

  • 27

    Factor results in August 2007Scaled to 1% daily volatility1

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    UK:

    More well-known factorsMostly proprietary factors Proprietary factors

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. The factor categorizations shown above were determined based on what QIS perceives to be the level of crowding and popularity of quantitative factors. The most common factors are included in the more well-known category, while the most unique factors included in the mostly proprietary factors category. Source: QIS

  • 28

    Factor results in August 2007Scaled to 1% daily volatility1

    -35%-30%-25%

    -20%-15%-10%-5%

    0%5%

    10%

    1 3 7 9 13 15 17 21 23 27 29 31

    August 2007

    Continental Europe:

    More well-known factorsMostly proprietary factors Proprietary factors

    1 Equivalent to 16% annualized volatility. Scaled to normalize different return series.The graphs above illustrate backtested average daily returns to our target investment portfolios without taking into account transactions costs. These returns are reported for informational purposes only and do not reflect the performance of any GSAM product. These performance results are backtested based on an analysis of past market data with the benefit of hindsight, do not reflect the performance of any GSAM product and are being shown for informational purposes only. The factor categorizations shown above were determined based on what QIS perceives to be the level of crowding and popularity of quantitative factors. The most common factors are included in the more well-known category, while the most unique factors included in the mostly proprietary factors category. Source: QIS

  • 29

    Decay rates for popular factors

    An equal-weighted portfolio of Value and Momentum has experienced lower means and accelerated decay rates in recent times

    0.00

    0.50

    1.00

    1.50

    2.00

    2.50

    3.00

    3.50

    4.00

    Lag 1 Lag 5 Lag 20 Lag 60 Lag 120

    Info

    rmat

    ion

    Rat

    io

    1985-19961997-2008

    Source: Goldman Sachs Asset Management. Please refer to page 10 for the backtesting methodology used in the equal-weighted Value and Momentum factor (Value =B / P, Momentum = 2 12 month return) portfolios shown above. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. These results are gross of transaction costs. The time series has been split into two equal time periods to show how factor decay has changed for the more recent time period relative to the historical time period. The time period shown above (1985 - 2008) was selected due to data availability.

  • 30

    Decay rates for proprietary factors

    However, our proprietary factors have not seen the same decline in mean and/or acceleration in decay rates in recent times

    0.00

    0.50

    1.00

    1.50

    2.00

    2.50

    3.00

    3.50

    4.00

    Lag 1 Lag 5 Lag 20 Lag 60 Lag 120

    Info

    rmat

    ion

    Rat

    io

    1985-19961997-2008

    Source: Goldman Sachs Asset Management. Please refer to page 10 for the backtesting methodology used in the composite weighted proprietary factor portfolios shown above. The proprietary factor composite was determined based on what QIS perceives to be the level of crowding and popularity of quantitative factors. We have shown the above portfolios at various lags (days) to illustrate the effect of factor decay. These results are gross of transaction costs. The time series has been split into two equal time periods to show how factor decay has changed for the more recent time period relative to the historical time period. The time period shown above (1985 - 2008) was selected due to data availability.

  • 31

    Factor timing

    Factors become vulnerable (riskier) when they become too popular

    By monitoring factor popularity, we can adjust exposures to reflect changes in risk or expected return

    1 The QIS value spread is a metric formulated by the QIS team to capture the industry-adjusted dispersion in company over/under valuations.The above strategy was formed based on industry-adjusted B / P metrics. The value strategy is a portfolio that overweights those equities with high B / P (undervalued firms) and underweightsthose equities with low B / P. We then calculated the return to the portfolio and repeated the process on a monthly frequency. Simulated performance results do not reflect actual trading and have inherent limitations. Source of return data: Kenneth French Data Library. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. Source of firm book data (value spread): Kenneth French, Compustat. Due to data availability, we used Kenneth French Data Library for return to value and value spread data prior to 1978 (annual availability). Subsequently, we used Compustatfor value spread data (monthly availability) and Kenneth French Data Library for return data after 1978. Due to the different nature of the value spread data before and after 1978, we indexed data in the latter period to have the same starting point as in the former. This information discusses general market activity, industry or sector trends, or other broad-based economic, market or political conditions and should not be construed as research or investment advice. Please see additional disclosures.

    Cumulative performance of intra-industry Value strategy in the US, January 1928 February 2009

    Return to valueValue spread

    Above average: 5.9Below average: 3.6

    Subsequent 12-month return (%) to HML when Value spread is:

    -50

    0

    50

    100

    150

    200

    250

    300

    350

    400

    450

    1928

    1933

    1938

    1943

    1948

    1953

    1958

    1963

    1968

    1973

    1978

    1983

    1988

    1993

    1998

    2003

    2008

    Cum

    ulat

    ive

    retu

    rn (%

    )

    0.5

    0.6

    0.7

    0.8

    0.9

    1.0

    1.1

    1.2

    QIS

    val

    ue s

    prea

    d

  • 32

    Fix the underlying problem

    Quants have embraced objective models to forecast returns because theydo not trust biased human judgment

    An alternative solution is to train analysts and traders to recognize and control their biases

    Not strictly a quant approach, but uses insights gleamed from quantitative analysis

    Can also use quant/statistical models to improve analysts forecasts of the underlying fundamentals (EPS, ROE, growth, payout, etc.)

    Source: Goldman Sachs Asset Management.

  • 33

    Conclusions

    Historical results for many popular quant factors look much weaker when we adjust for liquidity and trading costs

    Crowding has fundamentally changed the outlook for many of thesefactors: Likely to prove more volatile and provide lower Sharpe ratios in the

    future Stronger results still possible for smaller managers who focus in less

    liquid stocks or markets

    Some opportunities for quant managers Develop proprietary factors that capitalize on over- and under-

    reaction Dynamic allocation to popular factors based on perceived crowding Work with traditional analysts and traders to minimize over- and

    under-reaction

    Source: Goldman Sachs Asset Management.

  • 34

    Tracking Error (TE) is one possible measurement of the dispersion of a portfolios returns from its stated benchmark. More specifically, it is the standard deviation of such excess returns. TE figures are representations of statistical expectations falling within normal distributions of return patterns. Normal statistical distributions of returns suggests that approximately two thirds of the time the annual gross returns of the accounts will lie in a range equal to the benchmark return plus or minus the TE if the market behaves in a manner suggested by historical returns. Targeted TE therefore applies statistical probabilities (and the language of uncertainty) and so cannot be predictive of actual results. In addition, past tracking error is not indicative of future TE and there can be no assurance that the TE actually reflected in your accounts will be at levels either specified in the investment objectives or suggested by our forecasts.

    References to indices, benchmarks or other measures of relative market performance over a specified period of time are provided for your information only and do not imply that the portfolio will achieve similar results. The index composition may not reflect the manner in which a portfolio is constructed. While an adviser seeks to design a portfolio which reflects appropriate risk and return features, portfolio characteristics may deviate from those of the benchmark.

    The strategy may include the use of derivatives. Derivatives often involve a high degree of financial risk because a relatively small movement in the price of the underlying security or benchmark may result in a disproportionately large movement in the price of the derivative and are not suitable for all investors. No representation regarding the suitability of these instruments and strategies for a particular investor is made.

    Past performance is not indicative of future results, which may vary. The value of investments and the income derived from investments can go down as well as up. Future returns are not guaranteed, and a loss of principal may occur.

    Backtested PerformanceBacktested performance results do not represent the results of actual trading using client assets. They do not reflect the reinvestment of dividends, the deduction of any fees, commissions or any other expenses a client would have to pay. If GSAM had managed your account during the period, it is highly improbable that your account would have been managed in a similar fashion due to differences in economic and market conditions.

    Simulated PerformanceSimulated performance is hypothetical and may not take into account material economic and market factors that would impact the advisers decision-making. Simulated results are achieved by retroactively applying a model with the benefit of hindsight. The results reflect the reinvestment of dividends and other earnings, but do not reflect fees, transaction costs, and other expenses, which would reduce returns. Actual results will vary.

    Expected ReturnsExpected return models apply statistical methods and a series of fixed assumptions to derive estimates of hypothetical average asset class performance. Reasonable people may disagree about the appropriate statistical model and assumptions. These models have limitations, as the assumptions may not be consensus views, or the model may not be updated to reflect current economic or market conditions. These models should not be relied upon to make predictions of actual future account performance. GSAM has no obligation to provide updates or changes to such data.

    Opinions expressed are current opinions as of the date appearing in this material only. No part of this material may, without GSAMs prior written consent, be (i) copied, photocopied or duplicated in any form, by any means, or (ii) distributed to any person that is not an employee, officer, director, or authorised agent of the recipient.

    Copyright 2009, Goldman, Sachs & Co. All rights reserved. Rev#21110.OTHER.OTU

    General notes