We now further investigate how past fund performance affects the inference about the predictability of flows. We sort the sample funds into 5 quintiles based on their returns in the previous year. By examining the predictability of flows within each quintile, we control for past fund return. If the predictability of flows is accounted for by past performance, we would expect insignificant alpha spread in the quintiles.
22
Table 12 reports alpha spreads between the flow-sorted portfolios for the quintile groups.
The central message is that the predictability of flows is largely subsumed by performance persistence. For the overall sample, the smart money effect is insignificant in 4 out of the 5 quintile groups, with the group of poorest-performing funds as the only exception. A similar impression is obtained for both investment-grade and high-yield bond funds; the predictability of flows is insignificant for 9 out of the 10 cases. Therefore, once we control for past fund return, the predictability of flows tends to disappear.
Taken together, our results suggest that the predictability of flows in corporate bond funds arises from the facts that investors chase fund performance and fund performance persists.
That is, capital flows into (out of) past winner (loser) funds so that the positive (negative)-flow portfolio contains more of past winner (loser) funds. Because of performance persistence, the positive-flow portfolio continues to outperform the negative-flow portfolio.
To have an integrated understanding of the predictability of flows, we relate our results in to those documented for equity funds. In the context of equity funds, Gruber (1996) and Zheng (1999) also suggest that the smart money effect is related to performance persistence, though they do not test the explanation intensively. Sapp and Tiwari (2004) find that return momentum explains the predictability of flows in equity funds. Lou (2012) shows that flow-induced price pressure on stock prices accounts for the predictability of flows. Interestingly, return momentum and price pressure are found to explain performance persistence in equity funds (Carhart, 1997;
Lou, 2012), and thus performance persistence is likely to contribute to the smart money effect in equity funds as well.
23 4.7 Idiosyncratic Flows
We have shown that the predictability of flows is likely to be information-driven. Now, we investigate the nature of information—public or private information—used by investors to direct capital into different funds.
We perform a test to examine the predictability of idiosyncratic flow that is defined as the component of flow unexpected by recent fund performance or macro condition.17 This component of flow is idiosyncratic for two reasons. First, since it is unrelated to recent fund-specific or macro-level variables, it is more likely to reflect investors’ idiosyncratic decisions (e.g., idiosyncratic liquidity needs). Second, it is computed for each individual fund separately.
Each month, idiosyncratic flow for each individual fund is the intercept plus residual from a time-series regression of the fund’s investor flow on lagged values of fund-specific and macro-level variables.
where fund-specific variables (including fund returns and characteristics) and macro variables are those described in Section 3. If investors possess superior information about the funds, idiosyncratic flows should predict fund performance. However, if investors mainly rely on public information in decision-making, idiosyncratic flows reflect their idiosyncratic need and should not relate to future fund performance.
Similar to the portfolios constructed based on sorts of fund flows in the previous month, we form four portfolios of funds based on idiosyncratic flows.18
17 Ferson and Kim (2012) examine systematic versus idiosyncratic flow based on factor analysis using eigenvectors extracted from aggregate fund flows.
18 For each fund in each month, idiosyncratic flow is estimated based on the fund’s whole record and thus has look-ahead bias. However, our purpose is not to develop a trading strategy, but to examine whether fund flow contains information above and beyond public information. In a robustness test, we eliminate the look-ahead bias by estimating idiosyncratic flow using only the fund’s record up to that month, and our inference is unchanged.
24
Portfolio 5: Equal-weighted portfolio of all funds with positive idiosyncratic flows.
Portfolio 6: Equal-weighted portfolio of all funds with negative idiosyncratic flows.
Portfolio 7: Flow-weighted portfolio of all funds with positive idiosyncratic flows.
Portfolio 8: Flow-weighted portfolio of all funds with negative idiosyncratic flows.
We refer to Portfolios 5 and 7 as “positive-idiosyncratic-flow portfolios,” while Portfolios 6 and 8 as “negative-idiosyncratic-flow portfolios.” As before, we examine the spread of risk-adjusted performance between these portfolios based on the two-factor and the four-factor models.
Table 13 shows that idiosyncratic flows do not significantly predict fund performance.
The four-factor alpha spread between Portfolios 7 and 8, though still positive at 0.06% per month (t-statistic = 1.19), is no longer of economic or statistical significance. In other words, the funds with positive idiosyncratic flows do not subsequently outperform the funds with negative idiosyncratic flows. Figure 2 illustrates the difference in predicative power of raw flows versus idiosyncratic flows. With recent fund-specific and macro variables controlled for, the unexpected flows exhibit little predictability for fund performance.
Therefore, the result suggests that fund investors make investment decisions based on public information rather than private information. However, from the perspective of investors, the use of public information seems rational since it is associated with better investment payoffs in the future.
5. Conclusion
This paper provides a comprehensive examination of investor flows in corporate bond funds. Corporate bond funds offer a new and important setting to study investor flows. Using a
25
sample of 418 US corporate bond funds over the period 1991–2014, we present new insights about the behavior of mutual fund investors.
First, we show that investor flows in corporate bond funds chase recent fund performance, but unlike the case for equity funds, the flow-performance relation is not convex in corporate bond funds. We also find that investor flows are sensitive to recent macro condition.
Next, we present robust evidence of the predictability of flows, i.e., funds experiencing net inflows subsequently outperform those with net outflows. This is similar to the prior finding for equity funds (e.g., Gruber, 1996; Zheng, 1999). More importantly, we show that this predictability cannot be explained by momentum or price pressure but is subsumed by fund performance persistence.
Finally, we investigate the nature of information used by fund investors by looking into idiosyncratic flows that are unrelated to past fund performance and macro condition. Our result reveals little evidence that idiosyncratic flows predict future fund performance. Hence, we conclude that fund investors do not use finer-than-public information in their decision-making.
It would be interesting for future research to examine the risk-shifting behavior in corporate bond funds, given a lack of convex flow-performance relationship. Another potential area is to study fund managers’ purchasing and selling activities in response to capital flows, when detailed information on fund holdings becomes available. Finally, our measure of idiosyncratic flow can be used in other settings such as equity funds and pension funds. We leave these topics for future research.
26 References
Agarwal, Vikas, Naveen Daniel, and Narayan Naik, 2004, Flows, performance, and managerial incentives in hedge funds, Working paper, available on SSRN.
Asness, Clifford, Robert Krail, and John Liew, 2001, Do hedge funds hedge? Journal of Portfolio Management 28, 6–19.
Berk, Jonathan, and Richard Green, 2004, Mutual fund flows in rational markets, Journal of Political Economy 112, 1269–1295.
Blake, Christopher, Edwin Elton, and Martin Gruber, 1993, The performance of bond mutual funds, Journal of Business 66, 371–403.
Brown, Keith, W. Van Harlow, and Laura Starks, 1996, Of tournaments and temptations: An analysis of managerial incentives in the mutual fund industry, Journal of Finance 51, 85-110.
Brown, Stephen, William Goetzmann, and James Park, 2001, Careers and survival: Competition and risk in the hedge fund and CTA industry, Journal of Finance 56, 1869–1886.
Carhart, Mark, 1997, On persistence in mutual fund performance, Journal of Finance 52, 57–82.
Chen, Joseph, Harrison Hong, Ming Huang, and Jeffrey D. Kubik, 2004, Does fund size erode mutual fund performance? The role of liquidity and organization, American Economics Review 94, 1276–1302.
Chen, Yong, Wayne Ferson, and Helen Peters, 2010, Measuring the timing ability and performance of bond mutual funds, Journal of Financial Economics 98, 72–89.
Chevalier, Judith, and Glenn Ellison, 1997, Risk taking by mutual funds as a response to incentives, Journal of Political Economy 105, 1167–1200.
Cici, Gjergji, and Scott Gibson, 2012, The performance of corporate-bond mutual funds:
Evidence based on security-level holdings, Journal of Financial and Quantitative Analysis 47, 159–178.
Cici, Gjergji, Scott Gibson, and John Merrick, 2011, Missing the marks: Dispersion in corporate bond valuations across mutual funds, Journal of Financial Economics 101, 206–226.
Comer, George, and Javier Rodriguez, 2008, An analysis of investment style, performance, and cash flows of fixed income funds, Working paper, Georgetown University.
Coval, Joshua, and Erik Stafford, 2007, Asset fire sales (and purchases) in equity markets, Journal of Financial Economics 86, 479–512.
27
Del Guercio, Diane, and Paula Tkac, 2002, The determinants of the flow of funds of managed portfolios: Mutual funds vs. pension funds, Journal of Financial and Quantitative Analysis 37, 523–557.
Dimson, Elroy, 1979, Risk measurement when shares are subject to infrequent trading, Journal of Financial Economics 7, 197–226.
Edelen, Roger, Richard Evans, and Gregory Kadlec. 2007, Scale effects in mutual fund performance: The role of trading costs. Working paper, Virginia Tech.
Edelen, Roger, and Jerold Warner, 2001, Aggregate price effects of institutional trading: A study of mutual fund flow and market returns, Journal of Financial Economics 59, 195–220.
Ellul, Andrew, Chotibhak Jotikasthira, and Christian Lundbad, 2012, Regulatory pressure and fire sales in the corporate bond market, Journal of Financial Economics 101, 596–620.
Elton, Edwin, Martin Gruber, and Christopher Blake, 1995, Fundamental economic variables, expected returns, and bond fund performance, Journal of Finance 50, 1229–1256.
Evans, Richard, 2010, Mutual fund incubation, Journal of Finance 65, 1581–1611.
Fama, Eugene, and James MacBeth, 1973, Risk, return, and equilibrium: Empirical tests, Journal of Political Economy 81, 607–636.
Ferson, Wayne, Tyler Henry, and Darren Kisgen, 2006, Evaluating government bond funds using stochastic discount factors, Review of Financial Studies 19, 423–455.
Ferson, Wayne, and Min Kim, 2012, The factor structure of mutual fund flows, International Journal of Portfolio Analysis and Management 1, 112–143.
Ferson, Wayne, and Rudi Schadt, 1996, Measuring fund strategy and performance in changing economic conditions, Journal of Finance 51, 425–460.
Fulkerson, Jon, Bradford Jordan, and Timothy Riley, 2013, Return chasing in bond funds, Journal of Fixed Income 22, 90–103.
Gebhardt, William, Soeren Hvidkjaer, and Bhaskaran Swaminathan, 2005, Stock and bond market interaction: Does momentum spill over? Journal of Financial Economics 75, 651–690.
Getmansky, Mila, Bing Liang, Christopher Schwarz, and Russ Wermers, 2011, Share restrictions and investor flows in the hedge fund industry, Working Paper.
Gruber, Martin, 1996, Another puzzle: The growth in actively managed mutual funds, Journal of Finance 51, 783–810.
28
Gutierrez, Roberto, William Maxwell, and Danielle Xu, 2009, On economies of scale and persistent performance in corporate-bond mutual funds, Working Paper.
Huang, Jennifer, Clemens Sialm, and Hanjiang Zhang, 2011, Risk shifting and mutual fund performance, Review of Financial Studies 24, 2575–2616.
Huang, Jennifer, Kelsey Wei, and Hong Yan, 2007, Participation costs and the sensitivity of fund flows to past performance, Journal of Finance 62, 1273–1311.
Huang, Jennifer, Kelsey Wei, and Hong Yan, 2012, Investor learning and mutual fund flows, Working paper.
Huang, Jing-Zhi, and Ying Wang, 2014, Timing ability of government bond fund managers:
Evidence from portfolio holdings, Management Science 60, 2091–2109.
Huij, Joop, and Jeroen Derwall, 2008, “Hot hands” in bond funds, Journal of Banking and Finance 32, 559–572.
Ippolito, Richard, 1992, Consumer reaction to measures of poor quality: evidence from the mutual fund industry, Journal of Law and Economics 35, 45-69.
Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, 65–91.
Jostova, Gergana, Stanislava Nikolova, Alexander Philipov, and Christof Stahel, 2013, Momentum in corporate bond returns, Review of Financial Studies 26, 1649–1693.
Keswani, Aneel, and David Stolin, 2008, Which money is smart? Mutual fund buys and sells of individual and institutional investors, Journal of Finance 63, 85–118.
Kim, Hwagyun, Arvind Mahajan, and Alex Petkevich, 2012, Sources of momentum in bonds, Working paper, Texas A&M University.
Kim, Min, 2011, Changes in Mutual Fund Flows and Managerial Incentives, Working paper, University of New South Wales.
Lou, Dong, 2012, A flow-based explanation for return predictability, Review of Financial Studies 25, 3457-3489.
Moneta, Fabio, 2015, Measuring bond mutual fund performance with portfolio holdings, Journal of Empirical Finance, forthcoming.
Nanda, Vikram, Zhi Wang, and Lu Zheng, 2004, Family Values and the Star Phenomenon:
Strategies of Mutual Fund Families, Review of Financial Studies 17, 667–698.
29
Nanda, Vikram, Zhi Wang, and Lu Zheng, 2009, The ABCs of mutual funds: On the introduction of multiple share classes, Journal of Financial Intermediation 18, 329–361.
Pool, Veronika, Clemens Sialm, and Irina Stefanescu, 2015, It pays to set the menu: 401(k) investment options in mutual funds, Journal of Finance, forthcoming.
Sapp, Travis, and Ashish Tiwari, 2004, Does stock return momentum explain the “smart money”
effect?, Journal of Finance 59, 2605–2622.
Schultz, Paul, 2001, Corporate bond trading costs: A peek behind the curtain, Journal of Finance 56, 677–698.
Schwarz, Christopher, 2012, Mutual fund tournaments: The sorting bias and new evidence, Review of Financial Studies 25, 913–936.
Shleifer, Andrei, and Robert Vishny, 1992, Liquidation values and debt capacity: A market equilibrium approach, Journal of Finance 47, 1343–1366.
Sialm, Clemens, Laura Starks, and Hanjiang Zhang, 2015, Defined contribution pension plans:
Sticky or discerning money? Journal of Finance 70, 805–838.
Sirri, Erik and Peter Tufano, 1998, Costly search and mutual fund flows, Journal of Finance 53, 1589–1621.
Spiegel, Matthew, and Hong Zhang, 2013, Mutual fund risk and market share-adjusted fund flows, Journal of Financial Economics 108, 506–528.
Warga. Arthur, 1991, Corporate bond price discrepancies in the dealer and exchange markets, Journal of Fixed Income 1, 7–16.
Warther, Vincent, 1995, Aggregate mutual fund flows and security returns, Journal of Financial Economics 39, 209–35.
Wermers, Russ, 2003, Is money really “smart”? New evidence on the relation between mutual fund flows, manager behavior, and performance persistence, Working paper, University of Maryland.
Zhang, Hanjiang, 2011, Asset fire sales, liquidity provision and mutual fund performance, Working paper, Nanyang Technological University.
Zhao, Xinge, 2005, Determinants of flows into retail bond funds, Financial Analysts Journal 61, 47-59.
Zheng, Lu, 1999, Is money smart? A study of mutual fund investors’ fund selection ability, Journal of Finance 54, 901–933.
30 Table 1 Summary Statistics
This table reports summary statistics. The sample contains 418 corporate bond funds, including 189 investment-grade and 229 high-yield bond funds. Panel A summarizes fund characteristics. Panels B and C report time-series average of the cross-sectional distribution of fund returns and flow ratio, respectively.
Panel D summarizes the time-series of the factors used to calculate abnormal fund performance. G is the excess return on the government index over one-month T-bill rate, (HY–IG) is the spread between the high-yield bond index and the investment-grade bond index, 𝑆𝑇𝐾 is the excess return on the CRSP value-weighted stock index, 𝐵𝑂𝑁𝐷 is the excess return on the Barclays aggregate bond index, 𝐷𝐸𝐹 is the return spread between the high-yield index and the intermediate government index, and 𝑂𝑃𝑇𝐼𝑂𝑁 is the return spread between the 𝐺𝑁𝑀𝐴 index and the intermediate government index. ρ1 is first-order autocorrelation.
The sample period is from January 1991 to December 2014.
Panel A: Summary Statistics of Fund Characteristics
Total Investment Grade High Yield
Panel B: Cross-Sectional Distribution of Fund Return (%/month)
All Funds 0.60 1.01 -0.57 -0.07 0.65 1.30 1.74 17.47
Investment Grade 0.47 0.71 -0.26 0.11 0.49 0.87 1.20 10.41
High Yield 0.66 0.71 -0.08 0.33 0.69 1.04 1.39 23.33
Panel C: Cross-Sectional Distribution of Net Fund Flow Ratio (%/month)
All Funds 0.24 6.27 -3.31 -1.50 -0.14 1.49 3.98 29.63
Investment Grade 0.13 5.31 -3.16 -1.36 -0.11 1.35 3.88 18.42
High Yield 0.31 6.03 -3.33 -1.56 -0.12 1.56 4.01 38.87
Panel D: Summary Statistics of Risk Factors (%/month)
G 0.26 1.20 -1.27 -0.44 0.31 0.99 1.76 11.22
31 Table 2
Risk-Adjusted Fund Performance
This table reports the risk-adjusted fund performance for equal-weighted portfolios of the corporate bond funds based on two alternative factor models. We first use a two-factor model as follows.
,
where Rt is the return on an equal-weighted portfolio of corporate bond funds in month t, Rft is one-month T-bill rate in month t, G is the excess return on the government index and (HY – IG) is the return spread between the high-yield bond index and the investment-grade bond index. The four-factor model is:
t,
where 𝑆𝑇𝐾 is the excess return on the CRSP value-weighted stock index, 𝐵𝑂𝑁𝐷 is the excess return on the Barclays aggregate bond index, 𝐷𝐸𝐹 is the return spread between the high-yield bond index and the intermediate government bond index, and 𝑂𝑃𝑇𝐼𝑂𝑁 is the return spread between the 𝐺𝑁𝑀𝐴 index and the intermediate government bond index. T-statistics, calculated based on Newey-West heteroskedasticity and autocorrelation-consistent standard errors, are reported in parentheses. The sample period is from January 1991 to December 2014.
Total Investment Grade High Yield
2-factor 4-factor 2-factor 4-factor 2-factor 4-factor
α -0.077 -0.048 -0.069 -0.074 -0.097 -0.053
32 Table 3
The Flow-Performance Relation
This table reports the Fama-MacBeth regression results of the flow-performance relation in corporate bond funds. The dependent variable is monthly net flow ratio for each fund. The independent variables include the natural logarithm of TNA (LnTNA) in the previous month, monthly return volatility (RetVol), expense ratio (Exp), fund age (Age), new flow ratio in the previous month (LagFlow), a dummy variable for family spillover effect, and performance rank that is the fractional rank (ranging from 0 to 1) of fund raw return relative to other funds within the same fund style in the previous 12 months. T-statistics, calculated based on the Newey-West heteroskedasticity and autocorrelation-consistent standard errors, are reported in parentheses. The sample period is from January 1991 to December 2014.
All Funds Investment Grade High Yield
Performance rank 0.014 0.016 0.016
(13.24) (8.23) (11.43)
Bottom performance quintile 0.038 0.033 0.044
(5.05) (2.37) (4.62)
4th performance quintile 0.010 0.011 0.016
(1.95) (1.40) (2.50)
3rd performance quintile 0.009 0.015 0.004
(2.04) (2.08) (0.70)
2nd performance quintile 0.009 0.013 0.012
(1.88) (1.72) (2.13)
Top performance quintile 0.042 0.044 0.048
(5.01) (2.71) (4.76)
Adj. R2 0.21 0.24 0.33 0.41 0.24 0.29
Top-bottom performance quintile 0.004 0.011 0.003
(0.37) (0.54) (0.23)
33 Table 4
Flow Reaction to Macro Condition
This table reports the results from pooled time-series and cross-sectional regressions, where the dependent variable is monthly fund flows and the independent variables are one-month lagged values of both macro-level and fund-macro-level variables. BOND is the excess return on the Barclays aggregate bond index. TB3 is the three-month T-bill rate. DEF is the return spread between the high-yield bond index and the intermediate government bond index, OPTION is the return spread between the GNMA index and the intermediate government bond index. STK is the excess return on the CRSP stock index, and VIX is the implied market volatility index. Fund-specific variables include fund size, return volatility, expense ratio, age, flow ratio in the previous month, family spillover effect, and performance rank that is the fractional rank (ranging from 0 to 1) of fund return relative to other funds in the same fund style in the previous 12 months. The standard errors are clustered at the fund level, and fund fixed effect is included in the regressions. The sample period is from January 1991 to December 2014.
All Funds Investment Grade High Yield
34 Table 5
Performance of Flow-Sorted Portfolios
This table reports the risk-adjusted performance of the portfolios constructed based on investor flows in the previous month t-1. Portfolios 1 and 2 are equal-weighted portfolios of positive-flow and flow funds, respectively. Portfolios 3 and 4 are flow-weighted portfolios of positive-flow and negative-flow funds, respectively. For the portfolios as well as the return spreads between portfolios, we first measure risk-adjusted performance (alpha2) using the two-factor model:
, index and the investment-grade bond index. Alpha is in percent per month. Similarly, we measure risk-adjusted performance (alpha4) using the four-factor model:
t,
where 𝑆𝑇𝐾 is the excess return on the CRSP value-weighted stock index, 𝐵𝑂𝑁𝐷 is the excess return on the Barclays aggregate bond index, 𝐷𝐸𝐹 is the return spread between the high-yield bond index and the
where 𝑆𝑇𝐾 is the excess return on the CRSP value-weighted stock index, 𝐵𝑂𝑁𝐷 is the excess return on the Barclays aggregate bond index, 𝐷𝐸𝐹 is the return spread between the high-yield bond index and the