Three Essays in Mutual Funds Open Access

Mangipudi, Chandra Sekhar (Summer 2020)

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My dissertation is focused on understanding the investment decisions of retail investors in the mutual funds market. In the first essay, I find that levels of purchases and redemptions are higher at the turn of the year, i.e. December and January, compared to other months. In tests studying the role of distribution channel on these patterns, I find that broker-sold funds experience a pull in purchases from January to December of previous year compared to direct-sold funds. This is consistent with the incentives selling brokers in the distribution channel to meet their annual sales quotas. In tests studying the role of tax-loss selling, I find that higher redemptions in December are concentrated in funds with poor performance but are not systematically different in years with negative and positive aggregate market returns. In the second essay co-authored with Narasimhan Jegadeesh, we investigate the validity of the claim in the recent literature that fund flows reveal the true asset pricing model. Based on the finding that market model alphas are stronger predictors of mutual fund flows than alphas with other models, Berk and van Binsbergen (2016) claim that CAPM is the best asset pricing model but Barber, Huang and Odean (2016) (BHO) claim it is evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show, theoretically and through simulations, that inference about the true asset pricing model is not tenable. The rejection of investor sophistication is tenable, but the appropriate benchmark to judge sophistication is different from the one that BHO use. In the third essay, I study the revealed preferences of equity mutual fund investors to examine the horizon of past performance that matters for buying and selling decisions separately. I find that current buying and selling decisions are sensitive to 52 and 37 months of past performance respectively. I compare the ability of long-horizon information in identifying superior funds next period with that of a simple metric such as prior one-month net return. The performance of portfolios formed using these two information sets indicates that investors’ dependence on long horizons of performance is not optimal.

Table of Contents

Seasonality in Fund Flows at the Turn of the Year: Role of Performance and Distribution Channel 1

1. Introduction 2

2. Literature 10

3. Hypotheses 14

3.1 Level of retail trading activity in equity funds at the turn of the year 14

3.2 Role of distribution channel on turn-of-the-year trading activity in equity funds 15

4. Data and Descriptive Statistics 17

4.1 Sample Selection 17

4.2 Variable Construction 20

4.3 Descriptive Statistics 21

5. Empirical Analysis of Turn-of-the-year Seasonality in Flows 23

5.1 Level of retail trading activity in equity funds at the turn of the year 23

5.2 Is the turn-of-the-year seasonality statistically significant? 25

5.3 Is the turn-of-the-year seasonality driven by fund characteristics? 27

6. Empirical Analysis on the Role of Distribution Channel on Turn-of-the-year Flows 31

6.1 Analysis using month-on-month changes in flow proxies 32

6.2 Difference in difference estimation using month-on-month changes in flow proxies 34

7. Tax-loss Selling and Outflows at the Turn of the year 36

7.1 Cross-sectional tests for tax-loss selling at the turn of the year 37

7.2 Time-series tests for tax-loss selling at the turn of the year 38

8. Robustness Tests 40

9. Discussion and Conclusion 41

10. Appendix 43

10.1 Cleaning and Merging CRSP with Morningstar Direct and N-SAR files 43

What do fund flows reveal about asset pricing models and investor sophistication? 48

1. Introduction 49

2. Fund flows and alphas: Foundation for empirical tests and inferences 53

2.1 Asset pricing model and return generating process 53

2.2 The Model 55

2.3 Alphas and fund flows 58

2.4 Econometricians’ information set and Alpha-fund flows horse race 60

3. Empirical Tests 65

3.1 Precision of alphas 66

4. Simulation Experiment 73

4.1 Simulation: Experimental design 74

4.2 Simulation: Tests and results 77

4.3 Robustness Tests 78

5. Binary Variable Regression 83

6. Model Robustness 85

7. Results in Perspective 87

8. Conclusion 89

9. Appendix 92

9.1 Proofs of Propositions 2.1 and 2.2 92

9.2 Covariance of Flows with Empiricist’s Alpha 96

9.3 Estimating Measurement Error Components 98

9.4 Proof of Proposition 2.5 102

Fund flow sensitivity to long-horizon performance 104

1. Introduction 105

2. Literature 109

3. Data and Descriptive Statistics 111

3.1 Sample Selection 111

3.2 Variable Construction 112

3.3 Descriptive Statistics 114

4. Empirical Results 115

4.1 Flow-performance at long horizons 115

4.2 Lag length selection for inflows and outflows 117

4.3 Performance persistence at long horizons 118

4.4 Economic Significance of Long-horizon Predictability 121

5. Conclusion 123

Figures 125

Tables 141

References 194

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