benchmarking money manager performance: issues & evidence louis k. c. chan university of...
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Benchmarking money manager performance:
Issues & evidence
Louis K. C. ChanUniversity of Illinois Urbana-
Champaign
March 2006
Objectives
• The evaluation and attribution of investment performance is crucial for investment research and practice– Money manager performance
– Results of investment strategies & trading rules
– Effects of managerial decisions on shareholder wealth
• Academic and practitioner research has produced a large array of methods for evaluating and attributing investment performance
Objectives
• Question: are conclusions sensitive to the choice of evaluation and attribution methods? why?
• We compare the results from various methods applied to common samples– Set of active institutional money managers– Passive indexes
Evaluating method performance• Many widely-used methods draw on evidence
from asset pricing studies that size, value/growth describe much of the variation in returns (notably Fama and French (1992), Fama and French (1993))
• We concentrate on benchmarking methods based on size, value/growth– Characteristic-matched control portfolios– Time-series factor model regressions– Effective asset mix regressions– Cross-sectional regressions on characteristics
• 1998 – 2000 market boom as stress test of benchmarking methods
Evaluating manager performance
• Much previous work on evaluating performance of mutual and closed-end funds (e.g. Jensen (1968), Elton et al. (1993), Malkiel (1995), Gruber (1995), Carhart (1997), Daniel et al. (1997), Kothari and Warner (2001), etc.)
• Managers of pension plan equity assets are just as important, but much less previous research (see LSV 1992, Coggin et al. 1993)
A first look: characteristic-matched portfolios
vs. 3 factor model
Benchmark details
• Benchmarks vary according to– Characteristics or loadings– Measuring size, value/growth style– Treating size, value/growth effects
separately– Portfolio weighting scheme– Frequency of benchmark reconstitution
Benchmark details
• Characteristics versus loadings– Predict benchmark return using portfolio’s
attributes (size, book-to-market …) or predict benchmark return using portfolio’s loadings on factors
– Some evidence that attributes predict returns better than loadings (Daniel and Titman 1997)
– Data on holdings not generally accessible
Building performance benchmarks
• Measuring size, value/growth style– Size: market capitalization (float?)– Value/growth orientation usually measured by
book-to-market ratio (book value of equity divided by market value of equity)
– Book value of equity does not record value of intangible assets; includes goodwill from acquisitions
Building performance benchmarks
• Treating size, value/growth effects separately– E.g. independent 2-way sorts by size, BM– In one-way sorts by book-to-market equity
large stocks typically are classified as growth – Under an independent size/BM sort procedure
large-cap managers, regardless of large value/large growth style, will tend to be compared against a growth benchmark
Building performance benchmarks
• Weighting scheme for stocks in benchmark– Equal-weighting– Value-weighting
• Benchmark reconstitution frequency– Over time benchmark becomes more
heterogeneous and may no longer correspond to managed portfolio’s features
Data
• Holdings and returns every quarter for 199 portfolios offered by money managers to clients, 1989Q1 - 2001Q4
• Domestic U.S. equity portfolios only• Different styles (large/mid/small,
value/blend/growth)• Some selection bias
Results outline• Performance relative to benchmarks based
on characteristics– Overall active manager sample– Classified by investment style– Diagnostics
• Performance relative to benchmarks based on loadings– Overall active manager sample– Classified by investment style– Diagnostics
Performance measures
• Abnormal return = portfolio’s return minus return on benchmark portfolio
• Tracking error volatility = standard deviation of quarterly difference between portfolio’s return and benchmark’s return
Benchmark performance
Benchmark performance
Benchmark comparisons
Performance based on regression benchmarks
• Three factor model excess return is ( rpt – rft ) – benchmark return
• benchmark return is from fitted regression β(rmt – rft ) + s SMBt + h HMLt
Regression-based benchmark details
• Exposures estimated– over full period (including the quarter when
we measure performance)– or leaving out the quarter when we measure
performance
• Measuring size, value/growth factors– High versus low book-to-market– Other indicators of value/growth orientation
Building regression-based benchmarks
• 3 factor model accounts for size, value/growth separately
• E.g. benchmark return for small value manager = return for market exposure
plus return for smallness plus return for value
• Benchmark credits manager for smallness even though small stocks’ performance is because small growth does better than small value
Regression-based benchmarks• Alternative: compare manager to a
selection of passive benchmarks (effective asset mix regressions)
rpt = α + w1*LGt + w2*LVt
+ w3*MCGt + w4*MCVt
+ w5*SGt + w6*SVt + υpt
w1, … ,w6 portfolio weights (between 0 and 1, add up to 1)
Building regression-based benchmarks
• Another widely-used alternative: each stock’s predicted return is from a cross-sectional regression using stock characteristics, industry dummy variables rit = α + β1*X1i + β2*X2i + …
Regression-based benchmark comparisons
Regression-based benchmark comparisons
Conclusions
• Benchmarking methods that appear similar on the surface can lead to very different conclusions about investment performance
• Popular methods (characteristic-matched reference portfolios, 3 factor time series regression models, cross-sectional regression) have disappointing ability to track managed active portfolios and passive benchmarks
Conclusions
• Methods based on within-size classifications, use multiple measures of value-growth orientation, improve ability to track managed and passive portfolios
• Given the fragility in reliably separating skill from style, detailed decomposition and attribution of performance should be treated with caution