Online Appendix. Do Funds Make More When They Trade More?

Size: px
Start display at page:

Download "Online Appendix. Do Funds Make More When They Trade More?"

Transcription

1 Online Appendix to accompany Do Funds Make More When They Trade More? Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor April 4, 2016

2 This Online Appendix presents additional empirical results, mostly robustness results, complementing the results presented in the paper. Most of the results presented here are summarized in the paper. Contents Section 1: Results from the Subperiod Table A1: Turnover-Performance Relation in the Cross Section and Time Series Table A2: Heterogeneity in the Turnover-Performance Relation Table A3: Properties of Fund Turnover and Performance Across Fund Categories Table A4: Correlations of Average Turnover Across Fund Categories Table A5: Commonality in Fund Turnover Table A6: What Explains Turnover? Table A7: Relation Between Fund Performance and Average Turnover Section 2: Alternative Fixed Effects Table A8: Counterpart of Paper s Table 1 with Fund and Benchmark-Month Fixed Effects Table A9: Counterpart of Paper s Table 1 with Fund Manager Fixed Effects Section 3: Placebo Test with Passive Funds Table A10: Counterpart of Paper s Table 1 with Passive Index Funds, Conservative Filter Table A11: Counterpart of Paper s Table 1 with Passive Index Funds, No Expense- Ratio Filter Table A12: Counterpart of Paper s Table 1 with Passive Index Funds, Higher Turnover Filter 2

3 Section 4: Finite-Sample Bias? Table A13: Does Performance Predict Turnover? Figure A1: Simulation Analysis To Assess Finite-Sample Bias Table A14: Turnover-Performance Relation Controlling for Lagged Performance Section 5: Alternative Clustering Table A15: Counterpart of Paper s Table 1, Clustering by Month Table A16: Counterpart of Paper s Table 1, Clustering by Fund and Month Table A17: Counterpart of Paper s Table 1, Clustering by Year Table A18: Counterpart of Paper s Table 2, Clustering by Month Table A19: Counterpart of Paper s Table 2, Clustering by Fund and Month Table A20: Counterpart of Paper s Table 7, Clustering by Month Table A21: Counterpart of Paper s Table 7, Clustering by Fund and Month Section 6: Measuring Turnover Table A22: Robustness to Flow-Induced Turnover Table A23: Counterpart of Paper s Table 1 with Benchmark-adjusted Turnover Table A24: Turnover-Performance Relation with Rescaled Turnover Measure Table A25: Turnover-Performance Relation Controlling for Stock Holdings Section 7: Alternative Benchmark Models Table A26: Counterpart of Paper s Table 1 with Estimated Morningstar Betas Table A27: Version of Table A26 with Betas Conditional on FundTurn Table A28: Counterpart of Paper s Table 1 with Three-Factor Fama-French Benchmark Table A29: Version of Table A28 with Betas Conditional on FundTurn Table A30: Counterpart of Paper s Table 1 with Four-Factor Fama-French-Carhart Benchmark 3

4 Table A31: Version of Table A30 with Betas Conditional on FundTurn Table A32: Counterpart of Paper s Table 1 with Five-Factor Fama-French Benchmark Table A33: Version of Table A32 with Betas Conditional on FundTurn Table A34: Counterpart of Paper s Table 1 with Cremers-Petajisto-Zitzewitz Benchmark Table A35: Version of Table A34 with Betas Conditional on FundTurn Section 8: Alternative Skill Proxies Table A36: Counterpart of Paper s Table 2 with Unadjusted Gross Alpha Table A37: Counterpart of Paper s Table 2 with Adjusted Gross Alpha Table A38: Manager Age, Tenure, and the Turnover-Performance Relation Table A39: Counterpart of Paper s Table 2 with fund-size and expense-ratio terciles computed within style-months Table A40: Comparing Direct-Sold and Broker-Sold Funds Section 9: Additional Results Table A41: Summary Statistics Table A42: Counterpart of Paper s Table 1 with Benchmark-Adjusted Net Returns Table A43: Counterpart of Paper s Table 1 with Annual Data Table A44: Interacting Turnover with Time Since Turnover Table A45: Additional Lags of Turnover Table A46: Counterpart of Paper s Table 1 in Cold-IPO-Market Subperiod ( ) Table A47: Economic Significance Figure A2: Nonlinearities in the Turnover-Performance Relation? 4

5 1. Results for the Recent Subsample Table A1 Turnover-Performance Relation in the Cross Section and Time Series This table is the same as Table 1 in the main paper, but uses data from Month Fixed Effects Fund Fixed Effects No Yes Yes (4.37) (3.74) No (-1.00) (-1.71) 5

6 Table A2 Heterogeneity in the Turnover-Performance Relation This table is the same as Table 2 in the main paper, but uses data from Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (4.23) (1.57) (3.63) (2.25) Yes (2.16) (0.47) (0.85) (1.57) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (3.06) (4.52) (3.88) (-0.07) Yes (0.14) (0.47) (0.12) (0.03) Panel C: Fund Size Categories Small Medium Large Small Large Controls No (5.44) (1.66) (0.97) (2.54) Yes (2.06) (0.47) (0.50) (1.93) Panel D: Fund Expense Ratio Categories High Medium Low High Low Controls No (3.99) (3.34) (2.44) (1.96) Yes (1.35) (0.47) (0.16) (2.25) 6

7 Table A3 Properties of Fund Turnover and Performance Across Fund Categories This table is the same as Table 3 in the main paper, but uses data from Average benchmark- Funds Number Fund turnover (fraction/year) adjusted return (%/month) included of funds Average Volatility Autocorr. Gross Net Panel A: Full Sample All Panel B: Stock Size Categories Small-Cap Mid-Cap Large-Cap Small Large (t-statistic) (4.43) (-0.29) (-0.89) (0.72) (0.58) Panel C: Stock Value-Growth Categories Growth Blend Value Growth Value (t-statistic) (13.37) (7.17) (2.93) (1.25) (1.19) Panel D: Fund Size Categories Small Medium Large Small Large (t-statistic) (5.98) (4.72) (-3.63) (0.57) (0.01) Panel E: Fund Expense Ratio Categories High Medium Low High Low (t-statistic) (6.50) (6.59) (1.13) (0.34) (-2.79) 7

8 Table A4 Correlations of Average Turnover Across Fund Categories This table is the same as Table 4 in the main paper, but uses data from Stock Size S M L Stock Value-Growth G B V Small 1.00 Growth 1.00 Mid Blend Large Value Fund Size S M L Fund Expense Ratio L M H Small 1.00 Low 1.00 Medium Medium Large High

9 Table A5 Commonality in Fund Turnover This is the same as Table 5 in the main paper, but uses data from (1) (2) (3) (4) (5) (6) AvgT urn (14.08) (1.10) (2.55) (2.81) (4.40) (7.63) AvgT urn Stock Size (5.60) AvgT urn Stock V G (2.66) AvgTurn Fund Size (4.97) AvgTurn Fund Exp (4.20) AvgT urnsim (6.10) Observations 217, , , , , ,058 Within-fund R 2 (%)

10 Table A6 What Explains Turnover? This table is the same as Table 6 in the main paper, but uses data from Dependent variable: FundTurn it Dependent variable: AvgTurn t (1) (2) (3) (4) (5) (6) (7) (8) Sentiment t (2.53) (2.02) (4.47) (3.62) V olatility t (5.19) (4.31) (3.93) (5.34) Liquidity t (-4.02) (-4.60) (-2.62) (-3.84) Business Cycle t (-2.96) (-5.31) Market Return t (-1.22) (-1.06) Time Trend t (-0.87) (-1.38) (-1.96) (-1.54) (-1.73) (-2.24) (-2.71) (-2.60) R R 2 R 2 (trend only) Observations

11 Table A7 Relation Between Fund Performance and Average Turnover This table is the same as Table 7 in the main paper, but uses data from (1) (2) (3) (4) (5) (6) AvgTurn i,t (3.29) (3.22) (2.27) AvgTurnSim i,t (3.89) (3.74) (2.06) FundTurn i,t (4.37) (3.43) (4.50) (4.09) Observations 221, , , , , ,058 11

12 2. Alternative Fixed Effects Table A8 Counterpart of Paper s Table 1 with Fund and Benchmark-Month Fixed Effects This table is the same as Table 1 in the main paper, except we replace month fixed effects with benchmark-month fixed effects. Benchmark-Month Fixed Effects Fund Fixed Effects No Yes Yes (6.63) (7.96) No (1.92) (1.48) 12

13 Table A9 Counterpart of Paper Table 1 with Fund Manager Fixed Effects This table is the same as Table 1 in the paper, except it replaces fund fixed effects with fund manager fixed effects. Data on manager identities are from Morningstar. If a manager manages multiple funds in a given month, the same manager s fixed effect appears in multiple fund-month observations in that month. If a fund has multiple managers in a given month, we follow a simple seniority-based approach to assign the manager fixed effects. When there are multiple managers, we define the fund s manager to be the person who arrived at the fund first. If there is a tie, we take the person who stays at the fund longest. Month Fixed Effects Fund Manager Fixed Effects No Yes Yes (6.30) (6.38) No (1.92) (1.61) 13

14 3. Placebo Test with Passive Funds Table A10 Counterpart of Paper s Table 1 with Passive Index Funds, Conservative Filter This table is the same as Table 1 in the main paper, except we use data on mutual funds that Morningstar classifies as index funds. To remove active funds that may be accidentally classified as passive funds, we exclude funds with turnover greater than 100% per year and funds with expense ratio greater than 1% per year. There are 12,520 observations in each regression. Month Fixed Effects Fund Fixed Effects No Yes Yes (-0.51) (-1.07) No (0.05) (0.01) 14

15 Table A11 Counterpart of Paper s Table 1 with Passive Index Funds, No Expense-Ratio Filter This table is the same as the previous table, except we remove the expense-ratio filter to create the sample of passive funds. There are 13,590 observation in each regression. Month Fixed Effects Fund Fixed Effects No Yes Yes (-0.44) (-1.15) No (0.34) (0.40) 15

16 Table A12 Counterpart of Paper s Table 1 with Passive Index Funds, Higher Turnover Filter This table is the same as Table A10, except we apply a 500% turnover filter instead of a 100% filter to create the sample of passive funds. There are 12,669 observations in each regression. Month Fixed Effects Fund Fixed Effects No Yes Yes (-0.61) (-1.38) No (0.27) (0.16) 16

17 4. Finite-Sample Bias? Table A13: Does Performance Predict Turnover? This table shows results from regressing fiscal-year fund turnover on contemporaneous and lagged fund performance. We work at the fiscal-year frequency so the timing of turnover and performance overlaps. The dependent variable is F undt urn(i, t) [FYR], defined here as fund i s turnover in fiscal year t. GrossR(i, t) [FYR] is the fund s benchmark-adjusted gross return during fiscal year t. GrossR(i, t 1) [FYR] is the fund s benchmark-adjusted gross return during the 12-month period prior to GrossR(i, t) [FYR]. All regressions include fund fixed effects and cluster by fund to account for persistence in turnover. (1) (2) (3) GrossR(i,t) [FYR] (0.55) (-0.19) GrossR(i,t-1) [FYR] (-1.88) (-1.80) Observations

18 9 x R(t+1) on v(t) R(t+1) on v(t), R(t), and R(t 1) 7 Mean slope coefficient on v(t) Simulated sample length (years per fund) Fig. A1. Simulation to Assess Finite-Sample Bias. This figure shows results from simulations used to quantify potential finite-sample bias in our main turnover-performance regressions. The figure also shows how controlling for lagged performance eliminates the bias. We simulate many samples, each of which has τ yearly observations. We plot τ on the horizontal axis. Simulated returns R are i.i.d. normal with mean zero and standard deviation 10.3% per year, which matches the empirical volatility. Simulated turnover follows v(t) = R(t 1) + u(t), where u(t) is i.i.d. normally distributed with mean zero and standard deviation The numbers and 0.44 are the slope and residual volatility, respectively, from an actual regression of annual FundTurn it on lagged GrossR i,t 1, both measured at the fiscal-year frequency, and fund fixed effects. The dashed red line shows the slope from a regression of R(t + 1) on v(t), averaged across simulated samples. The solid black line shows the slope from a regression of R(t +1) on v(t), R(t), and R(t 1), averaged across simulated samples. 18

19 Table A14: Turnover-Performance Relation Controlling for Lagged Performance The dependent variable is GrossR it, the fund s benchmark-adjusted gross return in month t. The first column matches the top-left cell of our paper s Table 1. The second column is the same but only uses observations with non-missing values of GrossRFY Ri, t 1 and GrossRFY Ri, t 2. Abusing notation slightly, GrossRFY R i,t 1 is the fund s benchmarkadjusted gross return during the fiscal year that coincides with the timing of FundTurn i,t 1. GrossRFY R i,t 2 is the fund s benchmark-adjusted gross return during the 12-month period prior to GrossRFY R i,t 1. Column 2 shows that we lose some observations when we require the additional regressors, and the smaller subsample has a slightly weaker, but still strong, turnover-performance relation. All regressions include fund fixed effects and cluster by Sector month. (1) (2) (3) FundTurn i,t (6.63) (5.56) (5.47) GrossRFY R i,t (0.99) GrossRFY R i,t (-4.33) Observations

20 5. Alternative Clustering Table A15: Counterpart of Paper Table 1, Clustering by Month This table is the same as the paper s Table 1, except we compute t-statistics differently. Whereas Table 1 in the paper clusters by Sector month, this table clusters by month. Month Fixed Effects Fund Fixed Effects No Yes Yes (4.32) (4.66) No (1.10) (0.86) 20

21 Table A16: Counterpart of Paper Table 1, Clustering by Fund and Month This table is the same as the paper s Table 1, except we compute t-statistics differently. Whereas Table 1 in the paper clusters by Sector month, this table clusters by fund and month. Month Fixed Effects Fund Fixed Effects No Yes Yes (3.96) (4.22) No (1.08) (0.84) 21

22 Table A17: Counterpart of Paper Table 1, Clustering by Year This table is the same as the paper s Table 1, except we compute t-statistics differently. Whereas Table 1 in the paper clusters by Sector month, this table clusters by calendar year. Month Fixed Effects Fund Fixed Effects No Yes Yes (3.65) (3.36) No (0.81) (0.61) 22

23 Table A18 Counterpart of Paper s Table 2, Clustering by Month This table is the same as Table 2 in the paper, except we compute t-statistics clustering by month instead of by sector month. Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (4.85) (2.70) (4.17) (3.79) Yes (3.14) (1.20) (0.98) (2.88) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (3.58) (4.83) (4.39) (0.44) Yes (1.25) (1.20) (1.55) (0.21) Panel C: Fund Size Categories Small Medium Large Small Large Controls No (5.67) (2.83) (1.04) (3.36) Yes (2.67) (1.20) (0.50) (2.20) Panel D: Fund Expense Ratio Categories High Medium Low High Low Controls No (4.74) (3.29) (2.08) (3.93) Yes (2.43) (1.20) (0.72) (3.29) 23

24 Table A19 Counterpart of Paper s Table 2, Clustering by Fund and Month This table is the same as Table 2 in the paper, except we compute t-statistics clustering by fund and month instead of by sector month. Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (3.81) (2.64) (3.73) (2.84) Yes (2.66) (1.11) (0.85) (2.24) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (3.30) (4.23) (4.10) (0.41) Yes (1.22) (1.11) (1.40) (0.19) Panel C: Fund Size Categories Small Medium Large Small Large Controls No (4.96) (2.49) (1.00) (2.91) Yes (2.32) (1.11) (0.46) (1.93) Panel D: Fund Expense Ratio Categories High Medium Low High Low Controls No (3.99) (3.15) (1.97) (3.08) Yes (2.21) (1.11) (0.63) (2.79) 24

25 Table A20 Counterpart of Paper s Table 7, Clustering by Month This table is the same as Table 2 in the paper, except we compute t-statistics clustering by month instead of by sector month. (1) (2) (3) (4) (5) (6) AvgTurn i,t (1.37) (1.36) (1.03) AvgTurnSim i,t (2.47) (2.17) (2.70) FundTurn i,t (4.32) (4.69) (4.61) (4.83) Observations

26 Table A21 Counterpart of Paper s Table 7, Clustering by Fund and Month This table is the same as Table 2 in the paper, except we compute t-statistics clustering by fund and month instead of by sector month. (1) (2) (3) (4) (5) (6) AvgTurn i,t (1.37) (1.36) (1.02) AvgTurnSim i,t (2.46) (2.15) (2.60) FundTurn i,t (3.96) (4.08) (4.27) (4.38) Observations

27 6. Measuring Turnover 27

28 Table A22 Robustness to Flow-Induced Turnover The dependent variable in each regression is GrossR i,t, fund i s benchmark-adjusted gross return in month t. Column 1 reproduces the results with fund fixed effects from the paper s Table 1. We consider two measures of lagged flow-induced turnover, denoted FlowTurn1 i,t 1 and FlowTurn2 i,t 1. Both measures are computed from the monthly series of fund size and fund returns, and both are measured over the same 12-month period as FundTurn i,t 1. FlowTurn1 i,t 1 is the sum of the absolute values of the 12 monthly dollar flows, divided by the average fund size during the 12-month period. FlowTurn2 i,t 1 is the smaller of two sums, one of all positive dollar flows and one of all negative flows during the 12-month period, divided by average fund size. FlowV ol i,t 1 is the standard deviation of the 12 monthly flows, measured as fractions during the same time window as FundTurn i,t 1. We winsorize all flow measures at the 1st and 99th percentiles. All regressions include fund fixed effects. t-statistics are computed as in Table 1. Data are from (1) (2) (3) (4) (5) (6) (7) (8) FundTurn i,t (6.63) (5.08) (5.03) (5.96) FlowTurn1 i,t (1.11) (0.83) FlowTurn2 i,t (1.35) (0.72) FundTurn i,t 1 FlowTurn2 i,t (4.96) FlowV ol i,t (0.53) (0.16) Observations

29 Table A23: Counterpart of Paper Table 1 with Benchmark-Adjusted Turnover This table is the same as the paper s Table 1, except we replace FundTurn i,t 1 with benchmark-adjusted turnover, computed as FundTurn i,t 1 minus the median turnover computed across all index funds in the same Morningstar Category (e.g. Large Blend) as fund i and whose turnover is measured in the same time window as FundTurn i,t 1. Month Fixed Effects Fund Fixed Effects No Yes Yes (4.01) (3.88) No (1.06) (0.90) 29

30 Table A24: Turnover-Performance Relation with Rescaled Turnover Measure Column 1 reproduces the results with fund fixed effects from the paper s Table 1. Column 2 is the same but uses a re-scaled version of FundTurn i,t 1. Whereas FundTurn i,t 1 divides min(buys, Sells) by the fund s average size during the previous fiscal year, the re-scaled version divides by the fund s size at the beginning of the previous fiscal year. FundTurn i,t 1 and its re-scaled versions are both winsorized at the 1st and 99th percentiles. We lose some observations in the second column because of missing fund size. Both regressions include fund FEs. (1) (2) FundTurn i,t (6.63) Re-scaled FundTurn i,t (11.68) Observations 285, ,071 30

31 Table A25: Turnover-Performance Relation Controlling for Stock Holdings Column 1 reproduces the results with fund fixed effects from the paper s Table 1. Column 2 controls for Percent Stock, the percent of the fund s assets held in long stock positions. To remove extreme outliers, we set Percent Stock equal to 100% when it exceeds 100%; this change affects fewer than 1% of observations. Percent Stock is from Morningstar and is typically reported quarterly. We use the most recent portfolio report date before month t, but we do not look back more than 12 months. All regressions include fund FEs. (1) (2) FundTurn i,t (6.63) (6.83) Percent Stock (2.67) Observations 285, ,897 31

32 7. Alternative Benchmark Models Table A26 Counterpart of Paper s Table 1 with Estimated Morningstar Betas This table is the same as Table 1 in the main paper, except it replaces GrossR with Morningstar-adjusted returns with estimated betas. Specifically, the dependent variable equals the fund s gross return minus the product of Morningstar s benchmark return and the fund s estimated beta against that benchmark. If the fund has fewer than 24 monthly observations, we use the average estimated beta across all funds in the same Morningstar category. Month Fixed Effects Fund Fixed Effects No Yes Yes (7.67) (7.62) No (1.48) (1.26) 32

33 Table A27 Version of Table A27 with Betas Conditional on FundTurn This table is the same as the previous table, except we allow estimated betas to vary over time with lagged fund turnover. For each fund we regress the fund s excess gross return on the benchmark return and its interaction with lagged F undt urn. The dependent variable in this table equals the estimated intercept plus residual from those regressions. If the fund has fewer than 24 monthly observations, we use the average estimated slopes across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (7.49) (7.39) No (1.21) (1.01) 33

34 Table A28 Counterpart of Paper s Table 1 with Three-Factor Fama-French Benchmark This table is the same as Table 1 in the main paper, except it replaces GrossR with threefactor Fama-French adjusted gross returns. Specifically, the dependent variable equals the fund s gross return minus the risk-free rate minus the three Fama-French excess factor returns (MktRf, HML, SMB) times their respective estimated betas. Beta estimates are fundspecific. If the fund has fewer than 24 monthly observations, we use the average estimated betas across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (7.09) (8.27) No (1.27) (1.41) 34

35 Table A29 Version of Table A28 with Betas Conditional on FundTurn This table is the same as the previous table, except we allow estimated betas to vary over time with lagged fund turnover. For each fund we regress the fund s excess gross return on the benchmark returns and their interaction with lagged F undt urn. The dependent variable in this table equals the estimated intercept plus residual from those regressions. If the fund has fewer than 24 monthly observations, we use the average estimated slopes across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (5.28) (6.32) No (0.44) (0.55) 35

36 Table A30 Counterpart of Paper s Table 1 with Four-Factor Fama-French-Carhart Benchmark This table is the same as Table 1 in the main paper, except it replaces GrossR with fourfactor-adjusted gross returns. Specifically, the dependent variable equals the fund s gross return minus the risk-free rate, minus the three Fama-French excess factor returns (MktRf, HML, SMB) times their respective estimated betas, minus the momentum excess return times the fund s momentum beta. Beta estimates are fund-specific. If the fund has fewer than 24 monthly observations, we use the average estimated betas across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (7.89) (8.87) No (-0.01) (-0.13) 36

37 Table A31 Version of Table A30 with Betas Conditional on FundTurn This table is the same as the previous table, except we allow estimated betas to vary over time with lagged fund turnover. For each fund we regress the fund s excess gross return on the benchmark returns and their interaction with lagged F undt urn. The dependent variable in this table equals the estimated intercept plus residual from those regressions. If the fund has fewer than 24 monthly observations, we use the average estimated slopes across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (5.18) (5.92) No (-1.36) (-1.60) 37

38 Table A32 Counterpart of Paper s Table 1 with Five-Factor Fama-French Benchmark This table is the same as Table 1 in the main paper, except it replaces GrossR with fivefactor Fama-French adjusted gross returns. Specifically, the dependent variable equals the fund s gross return minus the risk-free rate minus the five Fama-French excess factor returns (MktRf, HML, SMB, RMV, CMA) times their respective estimated betas. Beta estimates are fund-specific. If the fund has fewer than 24 monthly observations, we use the average estimated betas across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (5.93) (7.17) No (3.15) (3.71) 38

39 Table A33 Version of Table A32 with Betas Conditional on FundTurn This table is the same as the previous table, except we allow estimated betas to vary over time with lagged fund turnover. For each fund we regress the fund s excess gross return on the benchmark returns and their interaction with lagged F undt urn. The dependent variable in this table equals the estimated intercept plus residual from those regressions. If the fund has fewer than 24 monthly observations, we use the average estimated slopes across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (5.07) (6.06) No (2.65) (3.16) 39

40 Table A34 Counterpart of Paper s Table 1 with Cremers-Petajisto-Zitzewitz Benchmark This table is the same as Table 1 in the main paper, except it replaces GrossR with gross returns adjusted using the modified Fama-French factors of Cremers, Petajisto, and Zitzewitz (2013). Specifically, the dependent variable equals the fund s gross return minus the risk-free rate minus the three excess factor returns (S5RF, R3VR3G, and R2S5) times their respective estimated betas. Beta estimates are fund-specific. If the fund has fewer than 24 monthly observations, we use the average estimated betas across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (8.84) (9.34) No (2.05) (2.05) 40

41 Table A35 Version of Table A34 with Betas Conditional on FundTurn This table is the same as the previous table, except we allow estimated betas to vary over time with lagged fund turnover. For each fund we regress the fund s excess gross return on the benchmark returns and their interaction with lagged F undt urn. The dependent variable in this table equals the estimated intercept plus residual from those regressions. If the fund has fewer than 24 monthly observations, we use the average estimated slopes across all funds in the same Morningstar category. Month Fixed Effect Fund Fixed Effect No Yes Yes (7.51) (7.93) No (1.07) (0.97) 41

42 8. Alternative Skill Proxies Table A36 Counterpart of Paper s Table 2 with Unadjusted Gross Alpha This table is the same as Table 2 in the paper, except it replaces the fund s expense ratio with the fund s unadjusted gross alpha, defined as the fund s full-sample average GrossR. This change mainly affects Panel D, but it also affects the other panels via the change in control variables. Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (5.87) (2.88) (4.72) (3.92) Yes (3.65) (1.68) (1.39) (2.76) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (5.06) (5.45) (4.66) (0.54) Yes (1.55) (1.68) (1.75) (-0.15) Panel C: Fund Size Categories Small Medium Large Small Large Controls No (7.56) (3.74) (1.46) (4.11) Yes (3.49) (1.68) (0.81) (4.11) Panel D: Fund Unadjusted Gross Alpha Categories High Medium Low High Low Controls No (6.93) (4.35) (1.44) (4.78) Yes (2.77) (1.68) (1.00) (3.01) 42

43 Table A37 Counterpart of Paper s Table 2 with Adjusted Gross Alpha This table is the same as Table 2 in the paper, except it replaces the fund s expense ratio with the fund s adjusted gross alpha, the measure of fund skill from Pastor, Stambaugh, and Taylor (2015). This change mainly affects Panel D, but it also affects the other panels via the change in control variables. Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (5.87) (2.88) (4.72) (3.92) Yes (3.28) (1.73) (2.21) (1.97) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (5.06) (5.45) (4.66) (0.54) Yes (1.13) (1.73) (2.11) (-1.08) Panel C: Fund Size Categories Small Medium Large Small Large Controls No (7.56) (3.74) (1.46) (4.11) Yes (3.61) (1.73) (0.85) (3.50) Panel D: Fund Adjusted Gross Alpha Categories High Medium Low High Low Controls No (6.90) (5.03) (-0.29) (5.79) Yes (2.67) (1.73) (-0.52) (4.17) 43

44 Table A38 Manager Age, Tenure, and the Turnover-Performance Relation The dependent variable is GrossR it, fund i s benchmark-adjusted gross return in month t. Column 1 matches the top-left cell in Table 1 in the paper. Avg. Mgr. Tenure is the average number of years managing the fund, computed across the fund s managers in the given month. Avg. Mgr. Age is the average age across fund s managers in the given month. Manager tenure and age are highly correlated within funds, so we avoid including both in the same regression. All regressions include fund fixed effects and cluster by Sector*month. Data on fund managers are from Morningstar. (1) (2) (3) (4) (5) FundTurn(i,t-1) (6.63) (6.02) (7.43) (5.47) (4.82) Avg. Mgr. Tenure (-5.49) (-1.40) FundTurn(i,t-1)*Avg.Mgr.Tenure (-4.53) Avg. Mgr. Age (-2.72) (0.37) FundTurn(i,t-1)*Avg.Mgr.Age (-3.40) Observations

45 Table A39 Counterpart of Paper s Table 2 with Fund-Size and Expense-Ratio Terciles Computed Within Style-Months This table is the same as Table 2 in the paper, except instead of computing fund size and expense ratio terciles within months, it computes them within style-months. Style here is Morningstar s Category, e.g., Large-Cap Growth. This change mainly affects Panels C and D, but it also affects Panels A and B via the change in control variables. Panel A: Stock Size Categories Small Cap Mid Cap Large Cap Small - Large Controls No (5.87) (2.88) (4.72) (3.92) Yes (4.00) (1.42) (0.69) (3.57) Panel B: Stock Value-Growth Categories Growth Blend Value Growth Value Controls No (5.06) (5.45) (4.66) (0.54) Yes (1.49) (1.42) (1.59) (0.11) Panel C: Fund Size Categories (Within Style-Month) Small Medium Large Small Large Controls No (7.10) (3.81) (2.04) (3.32) Yes (2.83) (1.42) (1.03) (2.64) Panel D: Fund Expense Ratio Categories (Within Style-Month) High Medium Low High Low Controls No (7.62) (3.58) (3.37) (3.48) Yes (2.67) (1.42) (1.29) (2.25) 45

46 Table A40: Comparing Broker-Sold and Direct-Sold Funds This table compares the turnover-performance slope across direct-sold and broker-sold funds in our sample. This table is the same as Table 2 Panel D in our paper, except we replace expense-ratio terciles with three distribution-channel categories: direct-sold, broker-sold, and not sure. Following Sun (2014), we say that a share class is broker-sold if it has a non-zero front load, a non-zero back load, or a 12b-1 fee exceeding 25 bps; otherwise, the share class is direct sold. Data on loads and 12b-1 fees are from Morningstar. Similar to Del Guercio and Reuter (2014), we classify a fund as broker-sold (direct-sold) if at least 75% of its assets are broker-sold (direct-sold) on average over time. If 25 75% of the fund s assets are broker-sold on average, we categorize the fund s distribution channel as not sure. In our full sample, 32% of fund-month observations are for broker-sold funds, 59% are for direct-sold funds, and 9% are not sure. Direct-Sold Not Sure Broker-Sold Direct - Broker Controls No (6.28) (2.83) (4.07) (1.43) Yes (1.63) (1.17) (1.80) (-0.35) 46

47 9. Additional Results Table A41 Summary Statistics Variable N Mean Stdev P1 P25 P50 P75 P99 Fund Return 314, Benchmark-adjusted return 314, Expense ratio 314, GrossR 314, FundTurn 285, Sentiment 296, Volatility 314, Liquidity 314, Business Cycle 314, Lagged Mkt. Return 314,

48 Table A42 Counterpart of Paper s Table 1 with Benchmark-Adjusted Net Returns This table is the same as Table 1 in the main paper, except we replace benchmark-adjusted gross fund returns (GrossR) with benchmark-adjusted net fund returns. Month Fixed Effects Fund Fixed Effects No Yes Yes (6.45) (6.56) No (1.48) (1.14) 48

49 Table A43 Counterpart of Paper s Table 1 with Annual Data This table is the same as Table 1 in the main paper, except for four changes: the unit of observation is the fund-fiscal year; GrossR is the fund s annual benchmark-adjusted gross return; we replace month FEs with fiscal year FEs; and we cluster by Morningstar sector fiscal year. There are 16,629 observations in each regression. Fiscal Year Fixed Effects Fund Fixed Effects No Yes Yes (5.56) (4.95) No (2.76) (2.53) 49

50 Table A44 Interacting Turnover with Time Since Turnover The dependent variable is GrossR i,t. Columns 1 and 3 match the bottom row of Table 1 in the main paper. NmonthsSince i,t is the number of months elapsed between the end of 12-month period used to measure FundTurn i,t 1 and the end of month t. Its value ranges from 1 to 12. (1) (2) (3) (4) FundTurn i,t (6.63) (5.03) (6.77) (5.71) FundTurn i,t 1 NmonthsSince i,t (-1.25) (-1.24) Observations Fund FEs Yes Yes Yes Yes Month FEs No No Yes Yes 50

51 Table A45 Additional Lags of Turnover The dependent variable is GrossR i,t. FundTurn i,t 1 is fund turnover in the previous fiscal year, FundTurn i,t 2 is fund turnover two fiscal years ago, and FundTurn i,t 3 is fund turnover three fiscal years ago. The regression includes fund fixed effects. t-statistics are computed as in Table 1. Data are from FundTurn i,t (3.84) FundTurn i,t (-0.89) FundTurn i,t (0.39) Observations

52 Table A46 Counterpart of Paper s Table 1 in Cold-IPO-Market Subperiod ( ) This table is the same as Table 1 in the main paper, but uses data from Month Fixed Effects Fund Fixed Effects No Yes Yes (3.21) (3.23) No (-1.95) (-2.67) 52

53 Table 47: Economic Significance This table computes the economic significance of the turnover-performance slopes. To conform as closely as possible to our turnover-performance regressions, we compute the withinfund standard deviation of each turnover variable using monthly data, and we require that GrossR is non-missing. The last column indicates the source of the estimated slope coefficient. All slopes are from regressions of GrossR it on lagged turnover. Within-fund Economic significance Variable Stdev. Slope =Stdev*Slope*1200 Source of slope FundTurn i,t Table 7 column Table 7 column 6 AvgTurn i,t Table 7 column Table 7 column 6 AvgTurnSim i,t Table 7 column Table 7 column 6 53

54 De meaned GrossR(i,t) De meaned FundTurn(i,t 1) 95% CI lpoly smooth: Demeaned GrossR(i,t) Figure A2. Nonlinearities in the Turnover-Performance Relation? We perform a kernel-weighted local polynomial regression of GrossR i,t on FundTurn i,t 1, both de-meaned at the fund level. We plot the smoothed values with 95% confidence bands. We use the ruleof-thumb bandwidth, epanechnikov kernel, and local-mean smoothing. We drop the bottom 1% of fund-demeaned FundTurn observations (values less than -106%) and the top 1% (values greater than 155%). 54

Internet Appendix for. Fund Tradeoffs. ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH, and LUCIAN A. TAYLOR

Internet Appendix for. Fund Tradeoffs. ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH, and LUCIAN A. TAYLOR Internet Appendix for Fund Tradeoffs ĽUBOŠ PÁSTOR, ROBERT F. STAMBAUGH, and LUCIAN A. TAYLOR This Internet Appendix presents additional empirical results, mostly robustness results, complementing the results

More information

Internet Appendix to Leverage Constraints and Asset Prices: Insights from Mutual Fund Risk Taking

Internet Appendix to Leverage Constraints and Asset Prices: Insights from Mutual Fund Risk Taking Internet Appendix to Leverage Constraints and Asset Prices: Insights from Mutual Fund Risk Taking In this Internet Appendix, we provide further discussion and additional empirical results to evaluate robustness

More information

SCALE AND SKILL IN ACTIVE MANAGEMENT. Robert F. Stambaugh. Lucian A. Taylor

SCALE AND SKILL IN ACTIVE MANAGEMENT. Robert F. Stambaugh. Lucian A. Taylor SCALE AND SKILL IN ACTIVE MANAGEMENT Ľuboš Pástor University of Chicago, NBER, CEPR National Bank of Slovakia Robert F. Stambaugh University of Pennsylvania, NBER Lucian A. Taylor University of Pennsylvania

More information

Table I Descriptive Statistics This table shows the breakdown of the eligible funds as at May 2011. AUM refers to assets under management. Panel A: Fund Breakdown Fund Count Vintage count Avg AUM US$ MM

More information

SCALE AND SKILL IN ACTIVE MANAGEMENT. Lubos Pastor. Robert F. Stambaugh. Lucian A. Taylor

SCALE AND SKILL IN ACTIVE MANAGEMENT. Lubos Pastor. Robert F. Stambaugh. Lucian A. Taylor SCALE AND SKILL IN ACTIVE MANAGEMENT Lubos Pastor Booth School of Business University of Chicago Robert F. Stambaugh The Wharton School University of Pennsylvania Lucian A. Taylor The Wharton School University

More information

Do Funds Make More When They Trade More?

Do Funds Make More When They Trade More? Do Funds Make More When They Trade More? Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor * February 9, 2015 Abstract We find that active mutual funds perform better after trading more. This time-series

More information

Internet Appendix to The Booms and Busts of Beta Arbitrage

Internet Appendix to The Booms and Busts of Beta Arbitrage Internet Appendix to The Booms and Busts of Beta Arbitrage Table A1: Event Time CoBAR This table reports some basic statistics of CoBAR, the excess comovement among low beta stocks over the period 1970

More information

Internet Appendix to Is Information Risk Priced? Evidence from Abnormal Idiosyncratic Volatility

Internet Appendix to Is Information Risk Priced? Evidence from Abnormal Idiosyncratic Volatility Internet Appendix to Is Information Risk Priced? Evidence from Abnormal Idiosyncratic Volatility Table IA.1 Further Summary Statistics This table presents the summary statistics of further variables used

More information

Internet Appendix for: Change You Can Believe In? Hedge Fund Data Revisions

Internet Appendix for: Change You Can Believe In? Hedge Fund Data Revisions Internet Appendix for: Change You Can Believe In? Hedge Fund Data Revisions Andrew J. Patton, Tarun Ramadorai, Michael P. Streatfield 22 March 2013 Appendix A The Consolidated Hedge Fund Database... 2

More information

Supplementary Appendix for Outsourcing Mutual Fund Management: Firm Boundaries, Incentives and Performance

Supplementary Appendix for Outsourcing Mutual Fund Management: Firm Boundaries, Incentives and Performance Supplementary Appendix for Outsourcing Mutual Fund Management: Firm Boundaries, Incentives and Performance JOSEPH CHEN, HARRISON HONG, WENXI JIANG, and JEFFREY D. KUBIK * This appendix provides details

More information

Caught on Tape: Institutional Trading, Stock Returns, and Earnings Announcements

Caught on Tape: Institutional Trading, Stock Returns, and Earnings Announcements Caught on Tape: Institutional Trading, Stock Returns, and Earnings Announcements The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.

More information

Trading Costs of Asset Pricing Anomalies Appendix: Additional Empirical Results

Trading Costs of Asset Pricing Anomalies Appendix: Additional Empirical Results Trading Costs of Asset Pricing Anomalies Appendix: Additional Empirical Results ANDREA FRAZZINI, RONEN ISRAEL, AND TOBIAS J. MOSKOWITZ This Appendix contains additional analysis and results. Table A1 reports

More information

Scale and Skill in Active Management

Scale and Skill in Active Management Scale and Skill in Active Management Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor * August 12, 2013 Preliminary and Incomplete Abstract We empirically analyze the nature of returns to scale in active

More information

Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix

Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix Daily Data is Bad for Beta: Opacity and Frequency-Dependent Betas Online Appendix Thomas Gilbert Christopher Hrdlicka Jonathan Kalodimos Stephan Siegel December 17, 2013 Abstract In this Online Appendix,

More information

Appendix Tables for: A Flow-Based Explanation for Return Predictability. Dong Lou London School of Economics

Appendix Tables for: A Flow-Based Explanation for Return Predictability. Dong Lou London School of Economics Appendix Tables for: A Flow-Based Explanation for Return Predictability Dong Lou London School of Economics Table A1: A Horse Race between Two Definitions of This table reports Fama-MacBeth stocks regressions.

More information

Scale and Skill in Active Management

Scale and Skill in Active Management Scale and Skill in Active Management Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor * November 17, 2013 Abstract We empirically analyze the nature of returns to scale in active mutual fund management.

More information

Online Appendix of. This appendix complements the evidence shown in the text. 1. Simulations

Online Appendix of. This appendix complements the evidence shown in the text. 1. Simulations Online Appendix of Heterogeneity in Returns to Wealth and the Measurement of Wealth Inequality By ANDREAS FAGERENG, LUIGI GUISO, DAVIDE MALACRINO AND LUIGI PISTAFERRI This appendix complements the evidence

More information

Double Adjusted Mutual Fund Performance

Double Adjusted Mutual Fund Performance Double Adjusted Mutual Fund Performance February 2016 ABSTRACT We develop a new approach for estimating mutual fund performance that controls for both factor model betas and stock characteristics in one

More information

Bayesian Alphas and Mutual Fund Persistence. Jeffrey A. Busse. Paul J. Irvine * February Abstract

Bayesian Alphas and Mutual Fund Persistence. Jeffrey A. Busse. Paul J. Irvine * February Abstract Bayesian Alphas and Mutual Fund Persistence Jeffrey A. Busse Paul J. Irvine * February 00 Abstract Using daily returns, we find that Bayesian alphas predict future mutual fund Sharpe ratios significantly

More information

Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation. Martijn Cremers (Yale) Antti Petajisto (Yale) Eric Zitzewitz (Dartmouth)

Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation. Martijn Cremers (Yale) Antti Petajisto (Yale) Eric Zitzewitz (Dartmouth) Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation Martijn Cremers (Yale) Antti Petajisto (Yale) Eric Zitzewitz (Dartmouth) How Would You Evaluate These Funds? Regress 3 stock portfolios

More information

MUTUAL FUND PERFORMANCE ANALYSIS PRE AND POST FINANCIAL CRISIS OF 2008

MUTUAL FUND PERFORMANCE ANALYSIS PRE AND POST FINANCIAL CRISIS OF 2008 MUTUAL FUND PERFORMANCE ANALYSIS PRE AND POST FINANCIAL CRISIS OF 2008 by Asadov, Elvin Bachelor of Science in International Economics, Management and Finance, 2015 and Dinger, Tim Bachelor of Business

More information

The cross section of expected stock returns

The cross section of expected stock returns The cross section of expected stock returns Jonathan Lewellen Dartmouth College and NBER This version: March 2013 First draft: October 2010 Tel: 603-646-8650; email: jon.lewellen@dartmouth.edu. I am grateful

More information

Scale and Skill in Active Management

Scale and Skill in Active Management Scale and Skill in Active Management Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor * January 31, 2014 Abstract We empirically analyze the nature of returns to scale in active mutual fund management.

More information

Liquidity and IPO performance in the last decade

Liquidity and IPO performance in the last decade Liquidity and IPO performance in the last decade Saurav Roychoudhury Associate Professor School of Management and Leadership Capital University Abstract It is well documented by that if long run IPO underperformance

More information

Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Full Sample Industry Average

Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Full Sample Industry Average Table IA.1 CEO Pay-Size Elasticity and Increased Labor Demand Panel A: IPOs Scaled by Industry Average (1) (2) (3) (4) (5) Ln(Market Value) 0.423 0.419 0.423 0.423 0.255 (33.29) (30.84) (33.29) (33.29)

More information

Do Discount Rates Predict Returns? Evidence from Private Commercial Real Estate. Liang Peng

Do Discount Rates Predict Returns? Evidence from Private Commercial Real Estate. Liang Peng Do Discount Rates Predict Returns? Evidence from Private Commercial Real Estate Liang Peng Smeal College of Business The Pennsylvania State University University Park, PA 16802 Phone: (814) 863 1046 Fax:

More information

Internet Appendix for. On the High Frequency Dynamics of Hedge Fund Risk Exposures

Internet Appendix for. On the High Frequency Dynamics of Hedge Fund Risk Exposures Internet Appendix for On the High Frequency Dynamics of Hedge Fund Risk Exposures This internet appendix provides supplemental analyses to the main tables in On the High Frequency Dynamics of Hedge Fund

More information

Behind the Scenes of Mutual Fund Alpha

Behind the Scenes of Mutual Fund Alpha Behind the Scenes of Mutual Fund Alpha Qiang Bu Penn State University-Harrisburg This study examines whether fund alpha exists and whether it comes from manager skill. We found that the probability and

More information

Online Appendix to. The Value of Crowdsourced Earnings Forecasts

Online Appendix to. The Value of Crowdsourced Earnings Forecasts Online Appendix to The Value of Crowdsourced Earnings Forecasts This online appendix tabulates and discusses the results of robustness checks and supplementary analyses mentioned in the paper. A1. Estimating

More information

Mutual Fund s R 2 as Predictor of Performance

Mutual Fund s R 2 as Predictor of Performance Mutual Fund s R 2 as Predictor of Performance By Yakov Amihud * and Ruslan Goyenko ** Abstract: We propose that fund performance is predicted by its R 2, obtained by regressing its return on the Fama-French-Carhart

More information

Online Appendix to Do Short-Sellers. Trade on Private Information or False. Information?

Online Appendix to Do Short-Sellers. Trade on Private Information or False. Information? Online Appendix to Do Short-Sellers Trade on Private Information or False Information? by Amiyatosh Purnanandam and Nejat Seyhun December 12, 2017 Purnanandam, amiyatos@umich.edu, University of Michigan,

More information

Liquidity skewness premium

Liquidity skewness premium Liquidity skewness premium Giho Jeong, Jangkoo Kang, and Kyung Yoon Kwon * Abstract Risk-averse investors may dislike decrease of liquidity rather than increase of liquidity, and thus there can be asymmetric

More information

The Effect of Financial Constraints, Investment Policy and Product Market Competition on the Value of Cash Holdings

The Effect of Financial Constraints, Investment Policy and Product Market Competition on the Value of Cash Holdings The Effect of Financial Constraints, Investment Policy and Product Market Competition on the Value of Cash Holdings Abstract This paper empirically investigates the value shareholders place on excess cash

More information

Online Appendix for Overpriced Winners

Online Appendix for Overpriced Winners Online Appendix for Overpriced Winners A Model: Who Gains and Who Loses When Divergence-of-Opinion is Resolved? In the baseline model, the pessimist s gain or loss is equal to her shorting demand times

More information

Internet Appendix for: Does Going Public Affect Innovation?

Internet Appendix for: Does Going Public Affect Innovation? Internet Appendix for: Does Going Public Affect Innovation? July 3, 2014 I Variable Definitions Innovation Measures 1. Citations - Number of citations a patent receives in its grant year and the following

More information

Institutional Money Manager Mutual Funds *

Institutional Money Manager Mutual Funds * Institutional Money Manager Mutual Funds * William Beggs September 1, 2017 Abstract Using Form ADV data, I document the extent to which investment advisers to mutual funds manage accounts and assets for

More information

Monthly Holdings Data and the Selection of Superior Mutual Funds + Edwin J. Elton* Martin J. Gruber*

Monthly Holdings Data and the Selection of Superior Mutual Funds + Edwin J. Elton* Martin J. Gruber* Monthly Holdings Data and the Selection of Superior Mutual Funds + Edwin J. Elton* (eelton@stern.nyu.edu) Martin J. Gruber* (mgruber@stern.nyu.edu) Christopher R. Blake** (cblake@fordham.edu) July 2, 2007

More information

Do Funds Make More When They Trade More?

Do Funds Make More When They Trade More? Do Funds Make More When They Trade More? Ľuboš Pástor Robert F. Stambaugh Lucian A. Taylor * August 26, 2016 Abstract We model fund turnover in the presence of time-varying profit opportunities. Our model

More information

Volatility Appendix. B.1 Firm-Specific Uncertainty and Aggregate Volatility

Volatility Appendix. B.1 Firm-Specific Uncertainty and Aggregate Volatility B Volatility Appendix The aggregate volatility risk explanation of the turnover effect relies on three empirical facts. First, the explanation assumes that firm-specific uncertainty comoves with aggregate

More information

Internet Appendix for Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle *

Internet Appendix for Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle * Internet Appendix for Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle * ROBERT F. STAMBAUGH, JIANFENG YU, and YU YUAN * This appendix contains additional results not reported in the published

More information

Online Appendix. Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle

Online Appendix. Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle Online Appendix to accompany Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle by Robert F. Stambaugh, Jianfeng Yu, and Yu Yuan November 4, 2014 Contents Table AI: Idiosyncratic Volatility Effects

More information

Excess Cash and Mutual Fund Performance

Excess Cash and Mutual Fund Performance Excess Cash and Mutual Fund Performance Mikhail Simutin The University of British Columbia November 22, 2009 Abstract I document a positive relationship between excess cash holdings of actively managed

More information

Internet Appendix to Do the Rich Get Richer in the Stock Market? Evidence from India

Internet Appendix to Do the Rich Get Richer in the Stock Market? Evidence from India Internet Appendix to Do the Rich Get Richer in the Stock Market? Evidence from India John Y. Campbell, Tarun Ramadorai, and Benjamin Ranish 1 First draft: March 2018 1 Campbell: Department of Economics,

More information

Asubstantial portion of the academic

Asubstantial portion of the academic The Decline of Informed Trading in the Equity and Options Markets Charles Cao, David Gempesaw, and Timothy Simin Charles Cao is the Smeal Chair Professor of Finance in the Smeal College of Business at

More information

Internet Appendix. Fundamental Trading under the Microscope: Evidence from Detailed Hedge Fund Transaction Data. Sandro Lunghi Inalytics

Internet Appendix. Fundamental Trading under the Microscope: Evidence from Detailed Hedge Fund Transaction Data. Sandro Lunghi Inalytics Internet Appendix Fundamental Trading under the Microscope: Evidence from Detailed Hedge Fund Transaction Data Bastian von Beschwitz Federal Reserve Board Sandro Lunghi Inalytics Daniel Schmidt HEC Paris

More information

Optimal Debt-to-Equity Ratios and Stock Returns

Optimal Debt-to-Equity Ratios and Stock Returns Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-2014 Optimal Debt-to-Equity Ratios and Stock Returns Courtney D. Winn Utah State University Follow this

More information

Deviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective

Deviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective Deviations from Optimal Corporate Cash Holdings and the Valuation from a Shareholder s Perspective Zhenxu Tong * University of Exeter Abstract The tradeoff theory of corporate cash holdings predicts that

More information

NBER WORKING PAPER SERIES SCALE AND SKILL IN ACTIVE MANAGEMENT. Lubos Pastor Robert F. Stambaugh Lucian A. Taylor

NBER WORKING PAPER SERIES SCALE AND SKILL IN ACTIVE MANAGEMENT. Lubos Pastor Robert F. Stambaugh Lucian A. Taylor NBER WORKING PAPER SERIES SCALE AND SKILL IN ACTIVE MANAGEMENT Lubos Pastor Robert F. Stambaugh Lucian A. Taylor Working Paper 19891 http://www.nber.org/papers/w19891 NATIONAL BUREAU OF ECONOMIC RESEARCH

More information

January 12, Abstract. We identify a team approach in which the asset management company assembles

January 12, Abstract. We identify a team approach in which the asset management company assembles On the Team Approach to Mutual Fund Management: Observability, Incentives, and Performance Jiang Luo Zheng Qiao January 12, 2014 Abstract We identify a team approach in which the asset management company

More information

Assessing the reliability of regression-based estimates of risk

Assessing the reliability of regression-based estimates of risk Assessing the reliability of regression-based estimates of risk 17 June 2013 Stephen Gray and Jason Hall, SFG Consulting Contents 1. PREPARATION OF THIS REPORT... 1 2. EXECUTIVE SUMMARY... 2 3. INTRODUCTION...

More information

Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds. Kevin C.H. Chiang*

Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds. Kevin C.H. Chiang* Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds Kevin C.H. Chiang* School of Management University of Alaska Fairbanks Fairbanks, AK 99775 Kirill Kozhevnikov

More information

The evaluation of the performance of UK American unit trusts

The evaluation of the performance of UK American unit trusts International Review of Economics and Finance 8 (1999) 455 466 The evaluation of the performance of UK American unit trusts Jonathan Fletcher* Department of Finance and Accounting, Glasgow Caledonian University,

More information

Revisiting Idiosyncratic Volatility and Stock Returns. Fatma Sonmez 1

Revisiting Idiosyncratic Volatility and Stock Returns. Fatma Sonmez 1 Revisiting Idiosyncratic Volatility and Stock Returns Fatma Sonmez 1 Abstract This paper s aim is to revisit the relation between idiosyncratic volatility and future stock returns. There are three key

More information

Modern Fool s Gold: Alpha in Recessions

Modern Fool s Gold: Alpha in Recessions T H E J O U R N A L O F THEORY & PRACTICE FOR FUND MANAGERS FALL 2012 Volume 21 Number 3 Modern Fool s Gold: Alpha in Recessions SHAUN A. PFEIFFER AND HAROLD R. EVENSKY The Voices of Influence iijournals.com

More information

Double Adjusted Mutual Fund Performance *

Double Adjusted Mutual Fund Performance * Double Adjusted Mutual Fund Performance * Jeffrey A. Busse Lei Jiang Yuehua Tang November 2014 ABSTRACT We develop a new approach for estimating mutual fund performance that controls for both factor model

More information

Does Transparency Increase Takeover Vulnerability?

Does Transparency Increase Takeover Vulnerability? Does Transparency Increase Takeover Vulnerability? Finance Working Paper N 570/2018 July 2018 Lifeng Gu University of Hong Kong Dirk Hackbarth Boston University, CEPR and ECGI Lifeng Gu and Dirk Hackbarth

More information

TABLE I SUMMARY STATISTICS Panel A: Loan-level Variables (22,176 loans) Variable Mean S.D. Pre-nuclear Test Total Lending (000) 16,479 60,768 Change in Log Lending -0.0028 1.23 Post-nuclear Test Default

More information

Volatility Jump Risk in the Cross-Section of Stock Returns. Yu Li University of Houston. September 29, 2017

Volatility Jump Risk in the Cross-Section of Stock Returns. Yu Li University of Houston. September 29, 2017 Volatility Jump Risk in the Cross-Section of Stock Returns Yu Li University of Houston September 29, 2017 Abstract Jumps in aggregate volatility has been established as an important factor affecting the

More information

Essays on Open-Ended on Equity Mutual Funds in Thailand

Essays on Open-Ended on Equity Mutual Funds in Thailand Essays on Open-Ended on Equity Mutual Funds in Thailand Roongkiat Ratanabanchuen and Kanis Saengchote* Chulalongkorn Business School ABSTRACT Mutual funds provide a convenient and well-diversified option

More information

Management Science Online Appendix Tables: Hiring Cheerleaders: Board Appointments of "Independent" Directors

Management Science Online Appendix Tables: Hiring Cheerleaders: Board Appointments of Independent Directors Management Science Online Appendix Tables: Hiring Cheerleaders: Board Appointments of "Independent" Directors Table A1: Summary Statistics This table shows summary statistics for the sample of sell side

More information

Betting against Beta or Demand for Lottery

Betting against Beta or Demand for Lottery Turan G. Bali 1 Stephen J. Brown 2 Scott Murray 3 Yi Tang 4 1 McDonough School of Business, Georgetown University 2 Stern School of Business, New York University 3 College of Business Administration, University

More information

Economic Review. Wenting Jiao * and Jean-Jacques Lilti

Economic Review. Wenting Jiao * and Jean-Jacques Lilti Jiao and Lilti China Finance and Economic Review (2017) 5:7 DOI 10.1186/s40589-017-0051-5 China Finance and Economic Review RESEARCH Open Access Whether profitability and investment factors have additional

More information

Credit Supply and House Prices: Evidence from Mortgage Market Segmentation Online Appendix

Credit Supply and House Prices: Evidence from Mortgage Market Segmentation Online Appendix Credit Supply and House Prices: Evidence from Mortgage Market Segmentation Online Appendix Manuel Adelino Duke University Antoinette Schoar MIT and NBER June 19, 2013 Felipe Severino MIT 1 Robustness and

More information

Mutual Funds and the Sentiment-Related. Mispricing of Stocks

Mutual Funds and the Sentiment-Related. Mispricing of Stocks Mutual Funds and the Sentiment-Related Mispricing of Stocks Jiang Luo January 14, 2015 Abstract Baker and Wurgler (2006) show that when sentiment is high (low), difficult-tovalue stocks, including young

More information

Web Appendix: Do Arbitrageurs Amplify Economic Shocks?

Web Appendix: Do Arbitrageurs Amplify Economic Shocks? Web Appendix: Do Arbitrageurs Amplify Economic Shocks? Harrison Hong Princeton University Jeffrey D. Kubik Syracuse University Tal Fishman Parkcentral Capital Management We have carried out a number of

More information

Pension Funds: Performance, Benchmarks and Costs

Pension Funds: Performance, Benchmarks and Costs Pension Funds: Performance, Benchmarks and Costs Rob Bauer (Maastricht University) Co-authors: Martijn Cremers (Yale University) and Rik Frehen (Tilburg University) October 20 th 2009, Q-Group Fall 2009

More information

Enhancing equity portfolio diversification with fundamentally weighted strategies.

Enhancing equity portfolio diversification with fundamentally weighted strategies. Enhancing equity portfolio diversification with fundamentally weighted strategies. This is the second update to a paper originally published in October, 2014. In this second revision, we have included

More information

Debt/Equity Ratio and Asset Pricing Analysis

Debt/Equity Ratio and Asset Pricing Analysis Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies Summer 8-1-2017 Debt/Equity Ratio and Asset Pricing Analysis Nicholas Lyle Follow this and additional works

More information

Common Risk Factors in the Cross-Section of Corporate Bond Returns

Common Risk Factors in the Cross-Section of Corporate Bond Returns Common Risk Factors in the Cross-Section of Corporate Bond Returns Online Appendix Section A.1 discusses the results from orthogonalized risk characteristics. Section A.2 reports the results for the downside

More information

The study of enhanced performance measurement of mutual funds in Asia Pacific Market

The study of enhanced performance measurement of mutual funds in Asia Pacific Market Lingnan Journal of Banking, Finance and Economics Volume 6 2015/2016 Academic Year Issue Article 1 December 2016 The study of enhanced performance measurement of mutual funds in Asia Pacific Market Juzhen

More information

Hedging Factor Risk Preliminary Version

Hedging Factor Risk Preliminary Version Hedging Factor Risk Preliminary Version Bernard Herskovic, Alan Moreira, and Tyler Muir March 15, 2018 Abstract Standard risk factors can be hedged with minimal reduction in average return. This is true

More information

How to measure mutual fund performance: economic versus statistical relevance

How to measure mutual fund performance: economic versus statistical relevance Accounting and Finance 44 (2004) 203 222 How to measure mutual fund performance: economic versus statistical relevance Blackwell Oxford, ACFI Accounting 0810-5391 AFAANZ, 44 2ORIGINAL R. Otten, UK D. Publishing,

More information

Asset Pricing and Excess Returns over the Market Return

Asset Pricing and Excess Returns over the Market Return Supplemental material for Asset Pricing and Excess Returns over the Market Return Seung C. Ahn Arizona State University Alex R. Horenstein University of Miami This documents contains an additional figure

More information

Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk

Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk Risk-managed 52-week high industry momentum, momentum crashes, and hedging macroeconomic risk Klaus Grobys¹ This draft: January 23, 2017 Abstract This is the first study that investigates the profitability

More information

Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation *

Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation * Should Benchmark Indices Have Alpha? Revisiting Performance Evaluation * Martijn Cremers Antti Petajisto Eric Zitzewitz December 31, 8 Abstract Standard Fama-French and Carhart models produce economically

More information

Properties of the estimated five-factor model

Properties of the estimated five-factor model Informationin(andnotin)thetermstructure Appendix. Additional results Greg Duffee Johns Hopkins This draft: October 8, Properties of the estimated five-factor model No stationary term structure model is

More information

The Persistent Effect of Temporary Affirmative Action: Online Appendix

The Persistent Effect of Temporary Affirmative Action: Online Appendix The Persistent Effect of Temporary Affirmative Action: Online Appendix Conrad Miller Contents A Extensions and Robustness Checks 2 A. Heterogeneity by Employer Size.............................. 2 A.2

More information

Betting Against Beta

Betting Against Beta Betting Against Beta Andrea Frazzini AQR Capital Management LLC Lasse H. Pedersen NYU, CEPR, and NBER Copyright 2010 by Andrea Frazzini and Lasse H. Pedersen The views and opinions expressed herein are

More information

Factoring Profitability

Factoring Profitability Factoring Profitability Authors Lisa Goldberg * Ran Leshem Michael Branch Recent studies in financial economics posit a connection between a gross-profitability strategy and quality investing. We explore

More information

Return Reversals, Idiosyncratic Risk and Expected Returns

Return Reversals, Idiosyncratic Risk and Expected Returns Return Reversals, Idiosyncratic Risk and Expected Returns Wei Huang, Qianqiu Liu, S.Ghon Rhee and Liang Zhang Shidler College of Business University of Hawaii at Manoa 2404 Maile Way Honolulu, Hawaii,

More information

The Liquidity Style of Mutual Funds

The Liquidity Style of Mutual Funds The Liquidity Style of Mutual Funds Thomas M. Idzorek, CFA President and Global Chief Investment Officer Morningstar Investment Management Chicago, Illinois James X. Xiong, Ph.D., CFA Senior Research Consultant

More information

Can Hedge Funds Time the Market?

Can Hedge Funds Time the Market? International Review of Finance, 2017 Can Hedge Funds Time the Market? MICHAEL W. BRANDT,FEDERICO NUCERA AND GIORGIO VALENTE Duke University, The Fuqua School of Business, Durham, NC LUISS Guido Carli

More information

SFSU FIN822 Project 1

SFSU FIN822 Project 1 SFSU FIN822 Project 1 This project can be done in a team of up to 3 people. Your project report must be accompanied by printouts of programming outputs. You could use any software to solve the problems.

More information

Web Appendix For "Consumer Inertia and Firm Pricing in the Medicare Part D Prescription Drug Insurance Exchange" Keith M Marzilli Ericson

Web Appendix For Consumer Inertia and Firm Pricing in the Medicare Part D Prescription Drug Insurance Exchange Keith M Marzilli Ericson Web Appendix For "Consumer Inertia and Firm Pricing in the Medicare Part D Prescription Drug Insurance Exchange" Keith M Marzilli Ericson A.1 Theory Appendix A.1.1 Optimal Pricing for Multiproduct Firms

More information

Betting Against Betting Against Beta

Betting Against Betting Against Beta Betting Against Betting Against Beta Robert Novy-Marx Mihail Velikov November, 208 Abstract Frazzini and Pedersen s (204) Betting Against Beta (BAB) factor is based on the same basic idea as Black s (972)

More information

Smart Beta #

Smart Beta # Smart Beta This information is provided for registered investment advisors and institutional investors and is not intended for public use. Dimensional Fund Advisors LP is an investment advisor registered

More information

Empirical Methods for Corporate Finance. Regression Discontinuity Design

Empirical Methods for Corporate Finance. Regression Discontinuity Design Empirical Methods for Corporate Finance Regression Discontinuity Design Basic Idea of RDD Observations (e.g. firms, individuals, ) are treated based on cutoff rules that are known ex ante For instance,

More information

Does Herding Behavior Reveal Skill? An Analysis of Mutual fund Performance

Does Herding Behavior Reveal Skill? An Analysis of Mutual fund Performance Does Herding Behavior Reveal Skill? An Analysis of Mutual fund Performance HAO JIANG and MICHELA VERARDO ABSTRACT We uncover a negative relation between herding behavior and skill in the mutual fund industry.

More information

Supplementary Results For Greenwood and Hanson 2009, Catering to Characteristics Last revision: June 2009

Supplementary Results For Greenwood and Hanson 2009, Catering to Characteristics Last revision: June 2009 Supplementary Results For Greenwood and Hanson 2009, Catering to Characteristics Last revision: June 2009 Appendix Table I Robustness to Forecasting Regressions Robustness of regressions of monthly long-short

More information

Identifying Skilled Mutual Fund Managers by their Ability to Forecast Earnings

Identifying Skilled Mutual Fund Managers by their Ability to Forecast Earnings Identifying Skilled Mutual Fund Managers by their Ability to Forecast Earnings Hao Jiang and Lu Zheng November 2012 ABSTRACT This paper proposes a new measure, the Ability to Forecast Earnings (AFE), to

More information

Empirical Study on Five-Factor Model in Chinese A-share Stock Market

Empirical Study on Five-Factor Model in Chinese A-share Stock Market Empirical Study on Five-Factor Model in Chinese A-share Stock Market Supervisor: Prof. Dr. F.A. de Roon Student name: Qi Zhen Administration number: U165184 Student number: 2004675 Master of Finance Economics

More information

NBER WORKING PAPER SERIES UNOBSERVED ACTIONS OF MUTUAL FUNDS. Marcin Kacperczyk Clemens Sialm Lu Zheng

NBER WORKING PAPER SERIES UNOBSERVED ACTIONS OF MUTUAL FUNDS. Marcin Kacperczyk Clemens Sialm Lu Zheng NBER WORKING PAPER SERIES UNOBSERVED ACTIONS OF MUTUAL FUNDS Marcin Kacperczyk Clemens Sialm Lu Zheng Working Paper 11766 http://www.nber.org/papers/w11766 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts

More information

AN ALTERNATIVE THREE-FACTOR MODEL FOR INTERNATIONAL MARKETS: EVIDENCE FROM THE EUROPEAN MONETARY UNION

AN ALTERNATIVE THREE-FACTOR MODEL FOR INTERNATIONAL MARKETS: EVIDENCE FROM THE EUROPEAN MONETARY UNION AN ALTERNATIVE THREE-FACTOR MODEL FOR INTERNATIONAL MARKETS: EVIDENCE FROM THE EUROPEAN MONETARY UNION MANUEL AMMANN SANDRO ODONI DAVID OESCH WORKING PAPERS ON FINANCE NO. 2012/2 SWISS INSTITUTE OF BANKING

More information

Variation in Liquidity, Costly Arbitrage, and the Cross-Section of Stock Returns

Variation in Liquidity, Costly Arbitrage, and the Cross-Section of Stock Returns Variation in Liquidity, Costly Arbitrage, and the Cross-Section of Stock Returns Badrinath Kottimukkalur * January 2018 Abstract This paper provides an arbitrage based explanation for the puzzling negative

More information

Diversified or Concentrated Factors What are the Investment Beliefs Behind these two Smart Beta Approaches?

Diversified or Concentrated Factors What are the Investment Beliefs Behind these two Smart Beta Approaches? Diversified or Concentrated Factors What are the Investment Beliefs Behind these two Smart Beta Approaches? Noël Amenc, PhD Professor of Finance, EDHEC Risk Institute CEO, ERI Scientific Beta Eric Shirbini,

More information

Liquidity, Liquidity Risk, and the Cross Section of Mutual Fund Returns. Andrew A. Lynch and Xuemin (Sterling) Yan * Abstract

Liquidity, Liquidity Risk, and the Cross Section of Mutual Fund Returns. Andrew A. Lynch and Xuemin (Sterling) Yan * Abstract Liquidity, Liquidity Risk, and the Cross Section of Mutual Fund Returns Andrew A. Lynch and Xuemin (Sterling) Yan * Abstract This paper examines the impact of liquidity and liquidity risk on the cross-section

More information

Quantitative vs. Fundamental Institutional Money Managers: An Empirical Analysis

Quantitative vs. Fundamental Institutional Money Managers: An Empirical Analysis Quantitative vs. Fundamental Institutional Money Managers: An Empirical Analysis Josef Lakonishok and Bhaskaran Swaminathan LSV Asset Management May 2010 Executive Summary The performance of quantitative

More information

A Lottery Demand-Based Explanation of the Beta Anomaly. Online Appendix

A Lottery Demand-Based Explanation of the Beta Anomaly. Online Appendix A Lottery Demand-Based Explanation of the Beta Anomaly Online Appendix Section I provides details of the calculation of the variables used in the paper. Section II examines the robustness of the beta anomaly.

More information

SSE Large&Mid&Small Cap Style Indices Methodology

SSE Large&Mid&Small Cap Style Indices Methodology all Cap Style Indices Methodology. Index Name and Code Index Name all Cap growth all Cap value all Cap relative growth Large&Mid&S mall Cap relative value Index Code 000057 000058 000059 000060 2. Base

More information

Performance-Chasing Behavior in Mutual Funds: New Evidence from Multi-Fund Managers

Performance-Chasing Behavior in Mutual Funds: New Evidence from Multi-Fund Managers Performance-Chasing Behavior in Mutual Funds: New Evidence from Multi-Fund Managers Darwin Choi, HKUST C. Bige Kahraman, SIFR and Stockholm School of Economics Abhiroop Mukherjee, HKUST* August 2012 Abstract

More information