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Information Content when Mutual Funds Deviate from Benchmarks Hao Jiang, Marno Verbeek and Yu Wang Rotterdam School of Management Erasmus University www.rsm.nl/mverbeek.

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Presentation on theme: "Information Content when Mutual Funds Deviate from Benchmarks Hao Jiang, Marno Verbeek and Yu Wang Rotterdam School of Management Erasmus University www.rsm.nl/mverbeek."— Presentation transcript:

1 Information Content when Mutual Funds Deviate from Benchmarks Hao Jiang, Marno Verbeek and Yu Wang Rotterdam School of Management Erasmus University Inquire Europe-UK, 26 March 2012 Marno Verbeek, Rotterdam School of Management 1/33

2 Are mutual funds informed investors?Do they help impounding information intoprices?  We evaluate the role that active managed (equity) mutual funds play in determining security prices.  Do deviations from benchmarks by active mutual funds contain information about future stock returns?  If so, what types of funds and stocks are most likely to exhibit such informed mutual fund portfolio tilting?  What are the implications for mutual fund performance and asset pricing? Marno Verbeek, Rotterdam School of Management 2/33

3 Measure of Deviations from Benchmarks : the portfolio weight of stock i in fund j during quarter t; : the portfolio weight of stock i in fund j’s benchmark index in quarter t; : the number of funds whose investment universe includes stock i. (The sto ck is either held by the fund or in the benchmark index of the fund). Thus, DFB is a stock-level measure that reflects the average amount of over/underweighting of the stock relative to the benchmark(s). 3

4 Selecting Mutual Funds’ Performance Benchmarks For each fund, select one index from the following 19 indexes: S&P 500, S&P 400, S&P 600, S&P 500/Barra Value and Growth, Russell 1000, Russell 2000, Russell 3000, Russell Midcap, Value & Growth Wilshire 5000, and Wilshire (Cremers and Petajisto, 2009) We also experiment with alternative ways of constructing benchmarks, with very similar results. 4

5 Matching funds and benchmarks We start from a universe of 19 benchmarks. For each fund in each quarter, we select the benchmark that minimizes the distance in portfolio weights between the fund and the index. Distance is measured by “Active Share” (Cremers and Petajisto, RFS, 2009): Thus: benchmarks are not self-reported and a fund can have different benchmarks over time. Marno Verbeek, Rotterdam School of Management 5/33

6 Marno Verbeek, Rotterdam School of Management 6/33 Data and Sample Selection Merging five major sources of financial data: –CRSP Survivor-Bias-Free US MFDB (fund returns, fees, …) –Thomson Financial CDA/Spectrum S12 holdings database (voluntary & mandatory) –CRSP stock files (stock prices, returns, shares outstanding & exchange codes) –I/B/E/S for analyst forecasts –Accounting information from Compustat Final sample contains 2,750 US domestic equity mutual funds spanning MFLINKSNCUSIP HoldingsMFDB Stocks Analyst forecasts Accounting info

7 Marno Verbeek, Rotterdam School of Management 7/33 Data and Sample Selection Using the investment objective codes provided by CRSP and TFN, we eliminate bond funds, balanced funds, asset allocation funds, international funds, precious metal funds, and sector funds. We also require that funds hold on average 80%- 105% in common equity. We (manually) exclude index funds by dropping fund names with “INDEX”, “IND”, etc. Multiple share classes in CRSP MFDB confer ownership in the same underlying pool of assets. We select the share class with the longest history of data.

8 Marno Verbeek, Rotterdam School of Management 8/33 Does DFB predict returns?I: Equally-weighted and value-weightedportfolio sorts. Portfolio performance measured by: Raw returns CAPM alphas 3, 4 and 5-factor alphas, controlling for size, value, momentum and the Pastor and Stambaugh (2003) liquidity factor. DGTW-adjusted: performance relative to a characteristics-based benchmark (consisting of stocks matched on size, book-to-market and momentum). [Daniel et al., 1993]

9 Return forecasting power of DFB: Decile Portfolios Marno Verbeek, Rotterdam School of Management 9/33

10 Return forecasting power of DFB: EW Decile Portfolios Panel A: Equal-Weight Post-Ranking Portfolio Return (%/month) DecileAverage ReturnCAPM AlphaFF AlphaCarhart Alpha5-Factor Alpha DGTW-Adj Return (2.5)(-2.88)(-5.45)(-4.72)(-4.87)(-3.62) (2.32)(-1.57)(-5.6)(-3.23)(-3.14)(-2.78) (2.35)(-1.11)(-4.51)(-2.08)(-1.87)(-1.4) (2.6)(-0.45)(-2.21)(-0.92)(-0.74)(-1.08) (2.81)(-0.05)(-2.29)(-0.72)(-0.53)(-0.79) (3.08)(0.45)(-1.49)(0.04)(0.35)(1.12) (3.4)(1.16)(0.06)(1.21)(1.79)(2.12) (3.12)(0.6)(-0.25)(-0.41)(0.12)(0.8) (3.6)(1.74)(3.13)(2.05)(2.33)(3.01) (3.93)(2.48)(4.39)(3.28)(3.49)(5.58) D10-D10.74***0.66***0.72***0.58***0.61***0.56*** (4.38)(4.07)(6.44)(5.28)(5.53)(5.99) D9-D20.51***0.48***0.61***0.36***0.38***0.34*** (4.31)(4.02)(6.04)(3.77)(3.8)(4.13) Marno Verbeek, Rotterdam School of Management 10/33

11 Return forecasting power of DFB: VW Decile Portfolios Panel B: Value-Weight Post-Ranking Portfolio Return (%/month) DecileAverage ReturnCAPM AlphaFF AlphaCarhart Alpha5-Factor Alpha DGTW-Adj Return (3.25)(-1.15)(0.02)(-0.24)(-0.2)(-3.16) (3.44)(0.66)(-1.26)(0.08)(0.11)(0.61) (3.36)(0.47)(-0.9)(0.17)(0.41)(1.19) (3.19)(0.22)(-1.16)(0.24)(0.37)(-0.53) (3.21)(0.19)(-1.09)(0.43)(0.36)(0.29) (3.28)(0.18)(-1.45)(-0.33)(-0.13)(0.36) (3.58)(1.27)(0.4)(1.16)(1.67)(1.16) (3.1)(-0.13)(-0.38)(-0.86)(-0.78)(-0.02) (3.84)(2.17)(3.38)(1.83)(1.88)(2.92) (3.73)(2.1)(3.54)(2.11)(2.29)(2.2) D10-D10.56**0.44**0.57***0.29*0.31**0.41*** (2.48)(2.2)(3.26)(1.94)(2.11)(2.73) D9-D20.35* *** ** (1.93)(1.5)(3.31)(1.51)(1.54)(2.01) Marno Verbeek, Rotterdam School of Management 11/33

12 What are the characteristics of stocks with extreme DFB? What are the stocks with heavy mutual fund bets? At end of each quarter, we sort stocks by their DFB, calculate the cross-sectional averages of the characteristics, and then average over time. Per DFB decile we report: Average portfolio weights in benchmark % stocks outside the benchmarks Scores (from 1-10) on size, book-to-market, previous one-year return (average = 5.5) Residual volatility (standard deviation after controlling for Fama-French factors).

13 Characteristics of Stocks Over- and Under- Weighted by MF DecileDFB (%) Benchmark Weights (%) Proportion of Stocks Outside of Benchmarks (%) No. of Funds in the Stock- Fund Cohort Market Cap Score (1- 10) BM Score (1-10) Pr1Yr Score (1-10) Residual Volatility (%) D10 - D Marno Verbeek, Rotterdam School of Management 13/33

14 Does DFB predict returns?II: Cross-sectional regressions Allow us to control for other factors that may predict the cross-section of future stock returns. DFB decile (D1, D10) Market cap, book-to-market ratio Previous one-year return, residual volatility Turnover ∆MFO (changes in fraction of shares owned by MFs) Δbreadth (changes in #MFs that hold the stock) See Table IV in the paper for full results. Marno Verbeek, Rotterdam School of Management 14/33

15 Return forecasting power of DFB: Fama and MacBeth (1973) Regressions R t+1 Model 1Model 2Model 3Model 4 D * *** ** ** (-2.13)(-2.46)(-2.15)(-3.10) D ***0.0159***0.0194***0.0158*** (5.28)(5.35)(5.25)(5.38) Market Cap * * (-2.21) (-2.41) BM Ratio * * (1.68) (1.67) Pr1Yr *** *** (5.83) (5.98) Residual Vol *** *** (-.268) (-2.67) Turnover * (-1.72) (-1.54) Pr1Mt (-1.26) (-1.42) ∆MFO (1.02)(-0.12) ∆Breadth * (1.91)(1.60) Intercept ** ** (0.53)(2.31)(0.46)(2.41) Avg Adj-R %6.87%0.94%6.98% Marno Verbeek, Rotterdam School of Management 15/33

16 So… DFB strongly predict returns A portfolio that buys stocks in Decile 10 (high DFB) and sells stocks in Decile 1 generates an average return of 0.74% (EW) or 0.56% (VW) per month. These differences are statistically significant. Risk adjustment has only limited impact. All five versions of alpha are highly significant. Cross-sectional regressions confirm this. Controlling for other predictive factors has little impact. We argue that this is (partly) an information-based story. Marno Verbeek, Rotterdam School of Management 16/33

17 Some evidence that is consistent withour information-based story. Stock characteristics DFB is more profitable for midcap stocks, stocks with high idiosyncratic volatility, and for stocks that have relatively low numbers of mutual funds investing in it. Fund characteristics DFB is more profitable for funds with high past performing funds (skilled funds), growth funds Marno Verbeek, Rotterdam School of Management 17/33

18 Further evidence supporting the information story(1) Stock characteristicsDFB strategy per size-quartile Marno Verbeek, Rotterdam School of Management 18/33

19 Further evidence supporting the information story(1) Stock characteristics DFB strategy per volatility-quartile Idiosyncratic volatility: firm-specific information Marno Verbeek, Rotterdam School of Management 19/33

20 Further evidence supporting the information story(1) Stock characteristics Number of funds: competition for private information Marno Verbeek, Rotterdam School of Management 20/33

21 (2) Fund characteristics Past two-year alphas: high vs low Marno Verbeek, Rotterdam School of Management 21/33

22 (2) Fund characteristics Investment objectives: Growth vs Income Marno Verbeek, Rotterdam School of Management 22/33

23 Information If mutual funds have superior information, what kind of information do we expect them to have? In stock markets, one of the most important pieces of information is about corporate earnings. Can DFB predict earnings surprises? Marno Verbeek, Rotterdam School of Management 23/33

24 (3) DFB and earnings news Quarters t+1t+2t+3t+4 Earnings Surprise Scaled by Actual Earnings (%) Q (0.32)(1.05)(1.26)(1.19) Q (5.62)(4.35)(2.81)(1.85) Q5-Q ***1.447***0.800***0.405** (6.03)(9.05)(5.69)(2.22) Q5-Q1 (Momentum-Adj) 1.384*** (5.31)(0.93)(1.38)(1.12) Earnings Surprise Scaled by Price (%) Q (-0.39)(0.30)(0.44)(0.42) Q (5.60)(4.27)(2.44)(1.46) Q5-Q ***0.023***0.012***0.007** (4.06)(6.93)(6.36)(2.10) Q5-Q1 (Momentum-Adj) 0.024*** *0.062 (4.23)(-0.74)(1.70)(1.02) Marno Verbeek, Rotterdam School of Management 24/33

25 (3) DFB and earnings news Quarters t+1t+2t+3t+4 CARs around Earnings Announcement (%) Q (1.20)(3.13)(3.01)(2.47) Q (5.06)(3.25)(3.18)(2.88) Q5-Q10.260*** *0.076* (4.32)(1.46)(1.97)(1.93) Q5-Q1 (Momentum-Adj)0.243*** (3.13)(-0.15)(0.35)(1.18) Marno Verbeek, Rotterdam School of Management 25/33

26 2626 Competing explanation: mutual funds’ demand pressure  Mutual fund managers tend to herd. –Wermers (1999), Sias (2004) –If mutual funds continue to buy the stocks they have overweighted, their demand pressure could drive up in-sample returns of the stocks.  Hypothesis 1: Mutual fund herding story implies positive correlation between consecutive changes in DFB; whereas informed/smart trader story implies negative correlation between consecutive changes in DFB.

27 t-1 t DFB=0 t+1 Arrival of good news DFB>0 Price increases; smart manager unwinds positions; DFB decreases t-1 t DFB=0 t+1 Managers buy DFB>0 Managers continue to buy DFB further increases 1: Informed/smart manager 2: Mutual fund demand pressure (herding) Marno Verbeek, Rotterdam School of Management 27/33

28 Smart money or mutual fund herding? Panel A: ΔDFB t Intercept **0.0011** (-0.97)(2.52)(2.61) ΔDFB t *** *** *** (-14.28)(-12.50)(-12.79) DFB t *** *** (-10.69)(-11.20) Market Cap t *** (-3.09) BM t (-0.30) Pr1Yr t (0.98) Residual Vol t (-1.13) Turnover t (1.24) Avg Adj-R %25.18%26.56%

29 Do returns subsequently reverse?  Hypothesis 2: High returns on stocks that funds overweight tend to subsequently reverse. R t-t+1 R t-t+2 R t-t+3 R t-t+4 D ** (-2.32)(-1.66)(-1.19)(-0.88) D ***0.0257***0.0322***0.0335*** (5.38)(4.67)(4.08)(3.31) Market Cap * ** * (-2.41)(-2.05)(-1.74)(-1.47) BM Ratio *0.0081*0.0120**0.0157** (1.67)(1.97)(2.08)(2.11) Pr1Yr ***0.0419***0.0466***0.0473*** (5.98)(5.89)(4.89)(4.09) Residual Vol *** *** ** ** (-2.67)(-2.21)(-1.98)(-1.85) Turnover ** ** * (-1.54)(-2.14)(-2.30)(-2.41) Pr1Mt **0.1002** (-1.42)(0.92)(1.86)(2.46) ∆MFO (-0.12)(-0.57)(0.42)(0.40) ∆Breadth *0.5117**0.5311** (1.60)(1.97)(2.60)(2.04) Adj R %6.82%6.66%6.47%

30 What about mutual fundperformance? Weight by amount of money invested. Still find alpha of about 6% per year. But funds invest less than 10% of their assets in these stocks. Results in basically zero alphas for mutual funds. Marno Verbeek, Rotterdam School of Management 30/33

31 DFB and mutual fund performance Decile % of Aggregate Fund InvestmentsAverage ReturnCAPM AlphaFF AlphaCarhart Alpha 5-Factor Alpha % (3.16)(-1.23)(0.09)(-0.4) 27.38% (3.81)(1.88)(0.54)(1.49)(1.43) 35.46% (3.49)(0.86)(-0.24)(0.55)(0.68) 43.45% (3.06)(0.45)(-0.56)(0.33)(0.42) 54.57% (3.29)(0.51)(-0.53)(0.84) 66.60% (3.31)(0.62)(-0.69)(0.44)(0.59) 78.66% (3.67)(1.77)(1.25)(1.83)(2.31) 89.74% (3.3)(0.68)(0.52)(0.16)(0.38) % (4.2)(2.95)(4.32)(2.95)(3.36) % (3.95)(2.61)(3.81)(2.98)(3.34) D10-D1 0.77***0.64***0.80***0.52***0.57*** (2.92)(2.68)(3.64)(2.82)(3.17) D9-D2 0.30* *** (1.81)(1.34)(3.2)(1.33)(1.62) Marno Verbeek, Rotterdam School of Management

32 Relation to previous literature  Micro-foundations of mutual funds’ information advantages –Coval and Moskowitz (2001): Geography and mutual funds –Cohen, Frazzini, and Malloy (2008): Shared educational background –It will be interesting to explore the link!  Best ideas of fund managers: Cohen, Polk, and Silli (2010)  Extracting beliefs from fund holdings: Shumway, Szefler, and Yuan (2009) Marno Verbeek, Rotterdam School of Management 32/33

33 Conclusions  A stock-level measure that seeks to aggregate various pieces of information scattered among fund managers, as revealed through their over- and under-weighting decisions, strongly forecasts cross-sectional variation in future returns.  Strong evidence in favor of an information-based interpretation of this return forecasting power.  Mutual funds tend to be “informed investors” whose investment activities help information transfer into security prices. Marno Verbeek, Rotterdam School of Management 33/33


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