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Management of flow risk in mutual funds

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Abstract

This paper is the first to relate the investment practices of U.S. equity mutual funds to their management of flow risk, defined as the adverse effect of investor in- and outflows on fund performance. Using a comprehensive merged sample of 2585 actively managed U.S. domestic equity funds from the CRSP mutual fund database and the SEC’s regulatory N-SAR filings, we are the first to detect differences in funds’ responses to flow risk. We find that funds using derivatives, such as options and futures on indices as well as individual stocks, have higher performance than non-using funds. We further show that this outperformance is the result of superior flow risk management using these derivatives and not a result of derivatives based stock-picking or market-timing activities. Overall, our findings document that superior flow management ability is valuable when managing open-end mutual funds and should be considered by investors and researches when evaluating fund performance.

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Notes

  1. http://www.wsj.com/articles/sec-preps-mutual-fund-rules-1410137113 (accessed 04/13/2015).

  2. See Table 20 of Investment Company Institute Fact Book, 2014.

  3. ReFlow is a company offering so-called NAV swaps to its clients to help them manage the adverse impact of investor flows on performance. http://www.reflow.com/.

  4. Cao et al. (2011) also use N-SAR data on derivatives use. However, they use a very short time interval from June 1996 to January 1998.

  5. We elaborate on the regulation of mutual fund derivative use in Sect. 3.1 to provide a more detailed understanding of how such strategies work.

  6. The asset coverage ratio is defined as the ratio of fund total net assets plus the market value of senior securities to the market value of senior securities.

  7. For a detailed description of mutual fund investment practice regulation, see Chen et al. (2013).

  8. We fill missing entries for TNA in CRSP similar to the procedure laid out in Rohleder et al. (2011).

  9. Our results are robust to changing this threshold to $25 million.

  10. Our results are robust to changing this threshold to 48 monthly observations.

  11. N-SAR filings from the SEC are used in several other studies. Edelen (1999) investigates the impact of investor flows on fund trading behavior. Deli and Varma (2002) and Almazan et al. (2004) analyze fund investment restrictions. O’Neal (2004) studies gross investor flows. Reuter (2006) investigates the relation between underwriter commissions and initial public offerings, while Kuhnen (2009) and Warner and Wu (2011) analyze investment advisory contracts. Edelen et al. (2012) examine brokerage commissions. Cashman et al. (2012, 2014), Clifford et al. (2013), and Fulkerson et al. (2013) analyze the effect of performance on future gross investor flows. Christoffersen et al. (2013) focus on the relationship between gross investor flows and fees. Chen et al. (2013) investigate mutual funds using short sales. Evans et al. (2014) analyze security lending by mutual funds and Clifford et al. (2014) analyze the determinants of investment practice permission.

  12. The N-SAR filings are available for download at http://www.sec.gov/edgar.shtml.

  13. We thank Kenneth R. French for providing data on risk free rate, market, size, book-to-market, and momentum factors at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  14. Results are qualitatively the same when we use mean squared net flow instead of mean absolute net flows as a proxy for adverse investor flows.

  15. In alternative specifications, the cross-sectional dummy derivatives is one if a fund uses derivatives at least 10, 25, 50, or 75 % of the time, respectively. The results remain the same. For brevity, these analyses are not reported in the main text, but available from the authors upon request.

  16. To control for any self-selection bias arising from these self-imposed restrictions we use an additional propensity score matching analysis. The results to this test are in line with our main findings and presented in Sect. 5.1.

  17. While Sapp and Tiwari (2004) argue that these findings are due to these studies not controlling for stock momentum, Keswani and Stolin (2008) show that even when controlling for momentum a smart money effect exists.

  18. Additional propensity score analyses for the individual components of derivatives show similar results. For brevity, they are not reported in the paper but available from the authors upon request.

  19. The conditioning variables are the S&P 500 dividend yield obtained from Thomson Reuters Datastream, the term spread (yield spread between 10-year treasury bond yield and 3-month treasury bill yield), the default spread (yield spread between BAA-rated and AAA-rated corporate bonds), and the 3-month treasury bill yield. We obtain all yield time-series from the St. Louis Federal Reserve website.

  20. We thank Antii Petajisto for providing the data. http://www.petajisto.net/data.html.

  21. We thank Robert F. Stambaugh for providing the time-series of the Pástor and Stambaugh (2003) liquidity factor on his website at http://finance.wharton.upenn.edu/~stambaugh/liq_data_1962_2012.txt.

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Acknowledgments

We are grateful for helpful comments and suggestions by an anonymous referee, Ralf Elsas, Jon Fulkerson, Scott Gibson, Paul Koch, Christian Koziol, Thomas Maehlmann, Michael McKenzie, Blake Phillips, David Rakowski, Stefan Ruenzi, Christian Schlag, Hendrik Scholz, Sara Shirley, Timothy Simin, Erik Theissen, and participants at 2013 German Finance Association doctoral seminar at the University of Wuppertal, 2014 HypoVereinsbank-UniCredit Group doctoral seminar at the University of Mannheim, 2014 International Finance and Banking Society Conference, 2014 Northern Finance Association Conference, 2014 Southern Finance Annual Meeting, 2015 Midwest Finance Annual Meeting, and 2015 Financial Management Association European Conference. We acknowledge financial support by the Research Center Global Business Management of the University of Augsburg. All remaining errors are our own.

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Correspondence to Martin Rohleder.

Appendix: N-SAR-CRSP matching and data screening

Appendix: N-SAR-CRSP matching and data screening

To obtain our data set we download 106,357 individual N-SAR-filings in text format from the SEC’s EDGAR online database for the period 1998–2013. We parse the individual text filings into a consistent table format using regular expressions under Linux. In addition, we extract ticker symbols from the header sections of the filings.

In the next step, we merge the N-SAR filings with the CRSP mutual fund database. Unfortunately, there is no common identifier in both CRSP and N-SAR. Even worse, in N-SAR there is no consistent fund identifier over time. Although the general instructions of the SEC urge registrants to use consistent information, the company identification key (CIK) and series numbers change over time for a substantial number of funds. Consequently, we have to match N-SAR with CRSP by using their fund names for each reporting date. For entries where ticker information is available in both CRSP and N-SAR filings, we additionally use the ticker symbols to match the funds. To improve our matching accuracy we clean fund names in CRSP and N-SAR by hand, e.g., we delete special characters such as “,” and “:” and write abbreviations in a consistent manner (e.g., “Small Cap” for “Small CP” or “Small Capitalization”). Furthermore, as fund name entries in N-SAR are often erroneous we correct them manually. We conduct the actual matching of fund names with Winkler’s (1990) Jaro-Winkler string distance metric as implemented in the SimMetrics open source library. In tests with our database, we have found the Jaro-Winkler algorithm to be superior to other string matching techniques in the SimMetrics library regarding speed and matching accuracy.

Since algorithmic matching techniques partly deliver false positive matches, we manually check all matches for plausibility and clean the merged sample from false positives as in Chen et al. (2013). We discard funds with discrepancies of more than 10 % for net assets reported in N-SAR and CRSP for more than 25 % of the time from our sample. Following Christoffersen et al. (2013) we remove fund months if in- or outflows in month t are larger than 100 % of the TNA from CRSP in month t − 1, or absolute net flows are larger than 50 % of the TNA from CRSP in month t − 1. We further drop all fund months in the top 1.5 % of difference between net flows from N-SAR and implied net flows from CRSP.

Table 12 displays cross-sectional means of fund characteristics for both the merged N-SAR-CRSP sample and the complete actively managed domestic equity fund universe from CRSP. Funds in our sample have higher TNA and they are somewhat older. Evans et al. (2015) find similar results are for their matched sample. Overall, there are no substantial differences between both data sets. Consequently, we conclude that our sample is representative for the universe of all actively managed U.S. domestic equity funds.

Table 12 Comparison of CRSP and N-SAR samples

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Rohleder, M., Schulte, D. & Wilkens, M. Management of flow risk in mutual funds. Rev Quant Finan Acc 48, 31–56 (2017). https://doi.org/10.1007/s11156-015-0541-1

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