Another explanation of the mutual fund fee puzzle

International Review of Economics and Finance 42 (2016) 134–152

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International Review of Economics and Finance journal homepage: www.elsevier.com/locate/iref

Another explanation of the mutual fund fee puzzle May Hu a, Chi-Chur Chao b,⁎, Jin Hao Lim c a b c

Department of Finance, Deakin Business School, Deakin University, Geelong, Australia Department of Economics, Deakin Business School, Deakin University, Geelong, Australia Monash University, Melbourne, Australia

a r t i c l e

i n f o

Article history: Received 2 July 2015 Received in revised form 11 November 2015 Accepted 11 November 2015 Available online 17 November 2015 JEL classification: G02 G23 G12

a b s t r a c t This paper demonstrates that investor sentiment explains the recent puzzle of the negative relation between fees and before-fee performance of equity mutual funds. Using a composite proxy for investor sentiment, the puzzle can be explained stronger by investor sentiment, compared to the strategic fee-setting explanation discussed in the literature. More-sentiment driven investors would like to select more skilled fund managers, leading to a better future performance in short run. Additionally, when sentiment is high (low), it results in lower (higher) fees. Our results highlight the use of investor sentiment approach in determining mutual fund fees and performance. © 2015 Elsevier Inc. All rights reserved.

Keywords: Investor sentiment Mutual funds Fees Performance

1. Introduction Mutual fund investors pay fees largely for quality portfolio management provided by the fund. Hence, higher fees should reflect better portfolio management and consequently, translate to better risk-adjusted performance. A negative relation between before-fee risk-adjusted performance and fees would thus be contradictory. This contradiction was highlighted in Gil-Bazo and Ruiz-Verdú (2009) (GR hereafter), in the period from January 1962 to December 2005. Two explanations were offered for their anomalous observation which are cost based explanation and strategic explanation (GR, 2009). However, their empirical analysis shows that although strategic fee setting is a significant contributing factor to the fee–performance anomaly, both cost based explanation and strategic explanation do not explain why managers choose to adjust fees instead of undertaking other means to improve expected performance. We aim to fill this gap by proposing a behavioural explanation for the fee–performance anomaly. Recent advances in the field of behavioural finance have consistently produced empirical evidences that suggest significant effects of investor sentiment in the mutual fund industry (albeit with much controversy). Baker and Wurgler (2006) (BW hereafter) argue that low (high) future stock returns are associated with high (low) investor sentiment. Gruber (1996) and Barber, Odean, and Zheng (2005) argue that mutual funds picked by retail investors are those with high fees. Bailey, Kumar, and Ng (2011) show that the performance of behaviourally biased investors are poor due to an inclination to make poor decisions when it comes to investments in mutual funds.

⁎ Corresponding author at: Department of Economics, Deakin University, 70 Elgar Road, Burwood, Victoria 3125, Australia. E-mail address: [email protected] (C.-C. Chao).

http://dx.doi.org/10.1016/j.iref.2015.11.002 1059-0560/© 2015 Elsevier Inc. All rights reserved.

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Generally, past papers have often used mutual fund flows as a sentiment measure for the mutual fund industry. Fund characteristics such as age, participation costs1 and flow to past performance sensitivity were identified as determinants of mutual fund flow. These prior studies found a convex relation between flow and past performance. For example, Frazzini and Lamont (2008) describe mutual fund flows as “dumb money” whereby fund inflows (outflows) are correlated with low (high) future returns. However, Spiegel and Zhang (2013) show that evidences of a convex relation between flow and past performance from previous studies may be untrue. These authors argue that prior conclusions of a convex relation between flow and past performance are caused by misspecification of the empirical model and that the relation should instead be linear. Due to data availability constraint and to avoid producing false convexity estimates as described by Spiegel and Zhang (2013), we opt to use the BW composite sentiment index instead of a fund flow measure. Other proxies for investor sentiment such as overconfidence,2 representativeness, and conservatism3 have also been considered but as BW (2007) argue, the use of a few selected bias and trading frictions do not sufficiently describe the complexity of real investors and markets. The BW composite sentiment index also serves as an alternative macro-level approach in measuring investor sentiment. To the best of our knowledge, the negative relation between mutual fund fees and before-fee risk-adjusted performance has yet to be tested with the BW composite sentiment index. According to BW (2006), investor sentiment can affect future stock returns. Arguing this point, it could then be reasonable to suggest that sentiment affects fees through stock returns if fund managers respond to changes in after-fee performance by adjusting fees charged to investors. We hypothesise that when sentiment is high (low), it results in lower (higher) fees and leads to better (poorer) before-fee risk-adjusted performance. To examine the role of investor sentiment on the before-fee performance and fees of mutual funds, we first confirm and extend GR (2009) demonstration of the negative relation between fees and before-fee risk-adjusted performance. Next, we seek explanations to the puzzle by investigating the role of performance in the determination of fund fees. Lastly, we consider whether better fund governance has a positive or negative influence on the effect of sentiment on fees and performance. If better fund governance reduces the likelihood of fund managers adjusting fees in response to investor sentiment, then it would bring fees more in line with performance. Therefore, we also investigate whether stronger fund governance reduces the effects of investor sentiment on the relation between fees and before-fee performance. The contribution of this paper differs from GR (2009) in that we show the significance and importance of using a behavioural hypothesis approach to explain mutual fund fees and performance. Expanding research in this field is also important. First, the use of mutual funds as an investment tool is increasingly popular among retail investors in past decades. Second, the number of retail investors holding individual stocks has steadily declined. This trend is observable in French (2008) where he reports that during 1980 to 2007, individual holdings of the market fell by 26.4% whereas open-end mutual fund holdings increased by 27.8%. This paper is organised as follows. Section 2 describes the critical literature review on mutual fund fees and performance. Section 3 describes the hypothesis development. Section 4 describes the sample and data set. Section 5 presents the methodology and empirical results. Conclusions are drawn in Section 6.

2. Literature review According to Berk and Green (2004), funds should not generate any after-fee risk-adjusted returns if the market is frictionless and at equilibrium. Funds with positive expected risk-adjusted returns will attract too much demand and likewise, funds with negative expected risk-adjusted returns will wind up with excessive supply. Arguing this point, before-fee risk-adjusted performance should have a positive relation with fees if the market is frictionless and at equilibrium. Otherwise, rational and informed investors would rather invest in index funds with considerably lower fees. When a fund increases (decreases) its fees, investors would demand higher (lower) risk-adjusted returns. That way, funds would create value for investors when there are differences in fees. GR (2009) demonstrate that there is a negative relation between fees and before-fee risk-adjusted performance. This inconsistency with a frictionless market has prompted us to test whether investor sentiment as a behavioural explanation, can be an answer to this puzzle. Till date, academics continue to debate regarding the Efficient Market Hypothesis (EMH) and behavioural finance arguments. According to De Long, Shleifer, Summers, and Waldmann (1990), a market can be described as interaction between arbitrageurs whose actions reflect general rationality and noise traders whose actions reflect irrational sentiments. Even with rational investors in the market, mispricing can still exist and persist due to the limits to arbitrage faced by rational investors which discourages them from exploiting any mispricing (De Long et al., 1990; Feng, Zhou, & Chan, 2014; Lam, C., & Wei, 2011; Shiller, 1984; Titman, Wei, K., & Xie, 2004; Wei, C., & Xie, 2008). This paradigm is at odds with EMH which tells us that any mispricing caused by noise traders will disappear quickly as it gets exploited by arbitrageurs (Fama, 1970, 1991). BW (2006) propose that when investor sentiment is high, investors are more uncertain about stock valuations. As speculation among investors becomes pervasive, it generates higher risk for holding on to stocks. As to what makes some stocks more speculative than others, BW (2007) point out that differences in the level of speculation among stocks could be due to difficulty and ambiguity in determining its underlying fundamental value. These speculative stocks that are difficult to value are also likely to have greater limits to arbitrage. In behavioural finance, this uncertainty means that investors are more susceptible to the effect of cognitive biases such as overconfidence, and conservatism, thereby triggering investor overreaction or underreaction which then leads to significant and even persistent stock market mispricing. 1 2 3

Huang, Wei, and Yan (2007) show cross-sectional effects of participation costs on mutual fund flows. For examples see Daniel, Hirshleifer and Subrahmanyam (1998) and Chui, Titman, and Wei (2010). For example see Barberis, Shleifer and Vishny (1998).

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Moreover, previous studies in equity mutual funds show evidence of quasi-rational investor behaviour. For example, Gruber (1996) explains that actively managed mutual funds typically perform poorer than index funds but are still immensely popular among investors. Additionally, these retail investors show tendency to buy funds with high fees (Barber et al., 2005; Gruber, 1996). Sirri and Tufano (1998), Bergstresser and Poterba (2002), and Sapp and Tiwari (2004) document performance chasing behaviour of retail investors whereby fund flows move towards funds with high past returns. Frazzini and Lamont (2008) later describe fund flows as “dumb money” whereby fund inflows (outflows) are correlated with low (high) future returns. Also, the popularity of Morningstar's rating of funds based on past returns might have encouraged performance chasing behaviour and consequently affect mutual fund flows (Del Guercio & Tkac, 2008). An argument for the survival of poor performing funds despite performance chasing behaviour is representativeness, whereby investors anchor to past performance and fail to sufficiently adjust for differential risk (Harless & Peterson, 1998 Tversky & Kahneman, 1974). An assumption made in this paper is that resilient mispricing is a product of investors acting irrationally and smart managers responding rationally (Baker, Wurgler, & Yuan, 2012; Yang & Zhou, 2015). These authors argue that although it is also possible for quasi-rational managers and rational investors to coexist, there are reasons to assume that managers are rational. For instance, managers have superior information about their own funds than the outside investors. Furthermore, Meulbroek (1992) and Seyhun (1992) highlight that managers can abuse information asymmetry to make higher abnormal returns.

3. Hypothesis development As mentioned earlier, GR (2009) argue two types of explanations (cost-based and strategic explanations) for the negative relation between fees and before-fee risk-adjusted performance. When there is an increase in costs of operating the fund, fees will also increase to reflect the added costs (GR, 2009). Therefore, according to cost-based explanations, fund fees are positively correlated with operating costs of the fund. If lower operating costs result in lower fees and consequently lead to better before-fee risk-adjusted performance, then the negative relation between fees and performance will become evident. Strategic explanations basically involve strategic fee-setting by mutual fund managers (GR, 2009). There are three strategic explanations for the negative relation between before-fee performance and fees. The first involves setting fees according to the elasticity of the demand for the mutual fund shares. Christoffersen and Musto (2002) argue that funds with poor ex-post performance will have a greater proportion of performance insensitive investors if we expect performance sensitive investors to make the switch towards better performing funds. Managers will then find it more optimal to increase fees since it will not likely cause a large outflow of funds. The second strategic explanation proposed by GR (2009) involves asymmetric information. These authors argue that mutual fund managers target performance-sensitive or performance-insensitive investors depending on the mutual fund's expected performance. The authors show that competition among mutual funds for the money of performance-sensitive investors causes funds with good ex-ante performance to price their fees low. Since poor ex-ante performing funds are not able to compete with funds with good ex-ante performance for the money of performance-sensitive investors, they choose to target their performance-insensitive investors instead by increasing fees. The third strategic explanation involves marketing costs and is also proposed by GR (2009). They argue that funds that perform poorly choose to increase marketing costs which leads to higher distribution costs. One likely possibility is that the use of marketing strategies targeted at less sophisticated investors could result in higher fund inflows. The increase in fund costs will then be burdened by performance-insensitive investors in the form of higher fees. Although GR (2009) show consistent results for all strategic explanations, the implementation of variables associated with both cost-based and strategic explanations fail to eliminate the negative association between fees and expected before-fee performance. The relation between investor sentiment and mutual fund performance has been examined in previous literature fairly rigorously but, there is currently no known study that can explain the direction of causality between sentiment and fund fees. Furthermore, whether the relationship between investor sentiment and fund fees is asymmetric or symmetric is also unknown. It is also entirely plausible that the relation between sentiment and fund fees is not significant or may not even exist at all. Frazzini and Lamont (2008) argue that future low returns are largely attributable to mutual fund flows rather than fund fees and expenses. In this paper, we follow the BW (2006) definition of investor sentiment, namely, the propensity to speculate. High investor sentiment is normally associated with high asset prices and asset under management. Asset management firms need to pay fixed fees to cover their operations. During high investor sentiment periods, the fixed fees can be covered by even a lower percentage of asset under management, which indicates a negative relation between investor sentiment and the fees. According to the “smart money” effect, fund investors can identify good fund managers who have superior skills to trade, and send their money to those skilled managers (Gruber, 1996; Zheng, 1999). Although this “smart money” effect has been argued by Sapp and Tiwari (2004); that is simply explained by the momentum effect in stock returns, more recent study by Keswani and Stolin (2008) find a robust “smart money” effect after controlling for the momentum factor. Frazzini and Lamont (2008) also report that the smart money hypothesis is particularly evident in the short horizons. Keswani and Stolin (2008) compare the effect in between the US and UK using monthly data instead of quarterly US data, and find that money is comparably smart, especially in the post-1991 period. They document that the “smart money” effect is mainly caused by the buying behaviour of both institutional and individual investors. With high investor sentiment, investors' propensity to speculate increases and they tend to trade more aggressively with more buying behaviour (BW, 2007). Therefore, moresentiment driven investors would like to select more skilled fund managers, leading to a better future performance in the short run. Additionally, the adjustment of fees could also be the fund managers' response to changes in investor sentiment by taking part in strategic fee-setting. When sentiment is low, investors are more likely to accept that the mutual fund share prices and fund fees are “fair” or priced correctly. Fund managers, influenced by utility maximisation and information asymmetry, then try to increase

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fees since investors are more attracted to funds with high profile or “superior” managers, high fees, and high previous returns. Consequently, this increase in fees leads to lower future returns.4 We propose the following hypotheses: H1. When sentiment is high (low), it results in lower (higher) fees. One of the purposes of having a board of directors is to protect shareholder interests. The board of directors have the fiduciary duty to ensure that the fees that investors pay are not grossly deviated from its underlying fundamental value. Though past literatures including GR (2009) suggest that better fund governance leads to fees that are more in line with performance, we suggest that the effectiveness of fund governance can be limited by investor sentiment. If investor sentiment is high enough, managers' likelihood of exploiting quasi-rational investors by adjusting fees should increase. Using daily data, Bollen and Busse (2001) show evidence of timing ability of fund managers. It is noted that managers do not have the authority to increase fees as and when they desire since they require mandate from the board of directors. But, the board of directors may not necessarily defend fund investors' interests due to conflicts of interest. Furthermore, there could be a link between preferential hiring and business connections (Kuhnen, 2009; Warner & Wu, 2011). Besides, fund directors are originally hired by the fund's management company and Tufano and Sevick (1997) find that independent directors who receive higher compensation tend to approve higher shareholder fees. Hence, these studies show that conflicts of interest could encourage collusion between the fund's management company and the fund directors. This compromises the integrity of fund directors in performing its fiduciary duty. Currently there is no consensus as to whether these conflicts of interest can be reduced by improving fund governance. Further, the measurement of the amount of influence that the fund's management company has on fund directors is problematic. Ceteris paribus, if better fund governance reduces speculation and uncertainty about a particular stock, then the effect of investor sentiment should weaken and hasten the arbitrage process. Thus, we hypothesise that best-governed funds are less affected by investor sentiment and bring fees more in line with performance, and vice versa. Our next hypothesis is: H2. Stronger fund governance reduces the effects of investor sentiment on the fees and performance of mutual funds. 4. Data Mutual fund data is extracted from the Center for Research in Security Prices (CRSP) Survivor-bias Free U.S. Mutual Fund Database. The data period is from January 1962 to December 2011. The CRSP database has different classifications that categorise each fund according to their investment objective. However, there is no classification that covers the entire 1962 to 2011 period. We follow the GR (2009) by combining different classifications to obtain our final sample of diversified domestic retail active equity mutual funds. Like in GR (2009), a fund is included in our sample if the type of securities mainly held by the fund is common stock, this is identified as ‘CS’ under Policy code or ‘EQ’ under Lipper asset code; and/or the fund is labelled as “Growth”, “Growth and Current Income”, “Longterm Growth”, “Maximum Capital Gains”, or “Small Capitalization Growth” under the Wiesenberger Classification code; and/or the fund is labelled as “Aggressive Growth”, “Growth Mid Cap”, “Growth and Income”, “Growth”, or “Small Company Growth” under Strategic Insight objective code. Since the Strategic Insight object code from CRSP ends at September 1998, we include an additional criterion where funds are labelled as “Capital Appreciation Funds”, “Mid-Cap Growth Funds”, “Growth and Income Funds”, “Growth Funds”, or “Small-Cap Growth Funds” under the Lipper Objective and Classification codes for the period between 1998 and 2011. The sample excludes institutional and passively managed index funds. We argue that this is necessary because the effect of investor sentiment is likely to be much higher for retail funds than in institutional funds. Despite the implementation of front- and rear-end loads designed to deter early redemption, individual investors are more likely to reallocate their money across different mutual funds than institutional investors (Frazzini & Lamont, 2008). As a final step, outliers which constitute approximately 0.20% of the sample were removed. The choice of proxy for investor sentiment in this study is the BW (2006) composite sentiment index, formed by taking the first principal component of six measures of investor sentiment. The BW composite sentiment index data as well as data pertaining to the six underlying proxies for investor sentiment are publicly available on Jeffrey Wurgler's website.5 Measuring sentiment however has its problems. Traditionally there are two approaches where researchers can try to quantify investor sentiment. First, is the use of direct sentiment measures, often in the form of surveys.6 Second, is the use of indirect sentiment measures7 which involve a combination of indirect measures to proxy investor sentiment. Both approaches have their strengths and weaknesses. The reason for selecting the BW composite sentiment index is that it offers a macroeconomic perspective of investor sentiment (BW, 2007). Besides, survey type direct measure of investor sentiment is susceptible to “prestige bias” and most survey type direct measures do not make any distinction between different levels of optimism or pessimism. Even though there is debate on whether the BW composite index is a reliable proxy, recent studies show that its application at both the US and global level can reap strong meaningful results. For example, Chang, Faff, and Hwang (2009) show that investor sentiment affects global equity markets, particularly in developed markets. Baker et al. (2012) also show that stock returns can be affected by contagion 4 Nevertheless, we note that the notion of managerial ability is controversial as there are both arguments and support from past papers. For instance, Carhart (1997) points out that short term persistence of good mutual fund performance does not reflect superior managerial ability. 5 Link to Jeffrey Wurgler's website: http://people.stern.nyu.edu/jwurgler/. 6 For examples see Brown and Cliff (2005) and De Bondt (1993). 7 For examples see Baker and Wurgler (2006), Tetlock (2007), and Ben-Rephael, Kandel, and Wohl (2012).

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sentiment at a local or global level. Nevertheless, the use of the BW (2006) investor sentiment index is by no means uncontroversial. For example, if there is a large shift in investors' preference from closed-end funds to open-end funds, the closed-end fund discount would not be a meaningful proxy (Zouaoui, Nouyrigat, & Beer, 2011). For further robustness and to examine how board quality and investor sentiment interact with each other, we use Morningstar's “Stewardship Grade” for mutual funds using a premium account for their website to proxy board quality. One of the five components of Morningstar's “Stewardship Grade” measures board quality8 and is based on three factors. The first of which is the proportion of independent directors (25% of board quality score). The second factor weighs the significance of investments made by independent directors in the fund (25% of board quality score). The third factor involves how well independent directors have served investors (50% of board quality score). Points are then assigned to funds ranging from 0 (Very Poor) to 2 (Excellent). However, due to the limitation of the premium account, we are unable to separate the board quality component from the other four components of the “Stewardship Grade”. Hence, we use an aggregate measure which is a reflection of all five components of the “Stewardship Grade”. The other four components are; regulatory issues; manager incentives; fees; and corporate culture. According to the Morningstar website (Morningstar, 2012), this measure helps investors make informed decision based on three main factors. Mainly, the manner in which funds are run, how much can shareholders expect their interests to be protected from potentially conflicting interests of the management company, and to what extent are interests of the management company and fund board aligned with shareholders (Lutton, Rushkewicz, Liu, & Ling, 2011). Table 1 shows the summary statistics of the sample of diversified domestic retail active equity mutual funds from 1962 to 2011. We found prima facie evidence that while after-expense returns, total loads, and total net assets (TNA) experience a modest decrease compared to GR (2009). One possible explanation for the decline in after-expense returns is the series of stock market crashes, such as the U.S. subprime mortgage crisis and the internet bubble. More importantly, the relatively unchanged expense ratio suggests that investors could be insensitive to fund fees. This is consistent with GR's (2009) strategic explanation that mutual fund managers target performance-sensitive or performance-insensitive investors, depending on the mutual fund's expected performance. Additionally, mean monthly TNA is 409.51 million whereas the median TNA is only 38.20 million. This indicates severe skewness in fund sizes. Our sample consists of 646,105 fund-month observations but 642,741 observations were used for our age variable due to missing values. To observe whether the negative relation between fees and before-fee performance exist in our sample of equity mutual funds, we follow the methodology in GR (2009) in Sections 5.1 and 5.2. 5. Methodology and empirical results 5.1. Relation between fees and before-fee risk-adjusted performance Berk and Green (2004) suggest that funds should not have any after-fee risk-adjusted returns if the market is at equilibrium and there are no market frictions. For a market that is consistent with the EMH, the relation between mutual fund fees and performance should be positive (GR, 2009). Following GR (2009), the negative relationship between fund fees, fit and before-fee risk-adjusted performance, α it is verified by estimating the following pooled ordinary least squares (OLS) regression equation: α it ¼ δ0t þ δ1 f it þ ξit ; i ¼ 1; …; N; t ¼ 1; …; T;

ð1Þ

where fit is the fund's monthly expense ratio and α it is its before-fee risk-adjusted performance estimated using Carhart's (1997) two-step process from Eqs. (A.1) and (A.2) (shown in Appendix A). It is also noted there is an alternative approach where fit is set as the dependent variable and α it is used as the explanatory variable: f it ¼ δ0t þ δ1 α it þ ξit ; i ¼ 1; …; N; t ¼ 1; …; T;

ð2Þ

where fit is the fund's expense ratio and α it is its before-fee risk-adjusted performance estimated using Carhart's (1997) two-step process from Eqs. (A.1) and (A.2). The reason to why this paper has opted for Eq. (1) rather than Eq. (2) is because past literatures such as Carhart (1997) and Chevalier and Ellison (1999) have regressed different measures of fund performance on expense ratios. Hence, our results can be compared to the results in previous studies. Secondly, funds' actual alphas cannot be observed directly and have to be estimated instead whereas fund expenses can be directly obtained from the database. Thus, the estimation of funds' alphas is prone to greater measurement errors. If Eq. (2) is used instead, the estimated coefficient of before-fee performance will be biased towards zero due to attenuation error caused by measurement error (Levi, 1973). If the market is efficient and the model is correct, GR argue that the coefficient, δ1 at market equilibrium should be equal to one where α it ¼ f it for all i. The results of pooled OLS regressions in rows (1) and (2) of Table 2 confirm that there is a negative relation between fees and before-fee risk-adjusted performance from 1962 to 2011. The coefficient of the fund's expense ratio, δ1 is negative and significantly different from zero at the 1% level. This suggests that funds with poor before-fee performance charge higher fees. Furthermore, rows (3) and (4) of Table 2 show the results of using before-fee risk-adjusted performance estimated by the Fama and French three factor model. The results are virtually identical regardless of whether before-fee risk-adjusted 8

GR (2009) use Morningstar Principia CD to obtain data on board quality.

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Table 1 Summary statistics: diversified domestic retail active equity mutual funds.

Returns Expenses Total Loads TNA AGE

Obs.

Mean

Median

S. D.

1st pctile.

25th pctile

75th pctile.

99th pctile.

646,105 646,105 646,105 646,105 642,741

0.57 1.53 3.00 409.51 22.21

0.94 1.48 2.75 38.20 16.00

5.39 0.61 2.80 2521.53 17.45

−15.60 0.33 0.00 0.10 3.00

−2.20 1.08 0.00 7.77 11.00

3.72 1.98 5.00 170.20 26.00

13.22 3.10 8.63 5907.40 79.00

This table shows summary statistics for the sample of diversified domestic retail actively managed equity mutual funds from 1962 to 2011. Obs. is the number of fundmonth observations for a particular variable. Returns is the monthly after-expense returns in percentage. Expenses is annual expense ratio in percentage. Total Loads is the sum of maximum front-end and back-end load in percentage. TNA is monthly total net assets in millions of dollars (USD). Age is number of years the fund offered.

performance is estimated with or without the momentum factor. Also, similar to GR (2009), it appears that there is cross-sectional correlation of residuals. By adjusting standard errors clustered by month, row 2 of Table 3 shows that standard error clustered by time (0.2527) is more than two times larger than the White standard error (0.0677). Even though the pooled OLS regression method does not have to rely on the asymptotic properties of random effects, a potential problem is that the estimates generated may be subject to fund-level unobserved heterogeneity bias. The endogeneity problem arises if fund's monthly expense ratio from Eq. (1) is endogenous, and hence correlated with the disturbance term. If this is the case, producing fixed effects estimates would be the more suitable approach. The Hausman specification test suggests the use of fixed effects over random effects as one would usually expect from large samples. Row (5) of Table 2 shows the panel regression results with both time and fund fixed effects. Since the coefficient of expense ratio is positive and significantly different from zero, it indicates that contrary to GR's findings, there is a positive relation between fees and before-fee risk-adjusted performance. 5.2. Verifying the robustness of the fees and before-fee performance puzzle To assess whether the anomaly is robust, we repeat the analysis in Section 5.2 using different subsamples. The results are presented in Table 3. To test whether the anomaly is due to small funds that have at least four years of previous returns data, Eq. (1) is estimated with a sample that excludes funds in the lowest size decile. Panel A of Table 3 shows that after controlling for small funds, the coefficient is still negative and significant at the 1% level. In Table 2, fund fees are limited only to expense ratios. However, investors often incur additional expenses such as front-end loads and rear-end loads. Thus, in Table 3 panel B, Eq. (1) is estimated using a subsample of no-load funds, load funds with a 2-year holding period, and load-funds with a 7-year holding period. The results show that the no-load fund subsample and load funds with a 7-year holding period subsample have significantly negative coefficients at the 1% level. However, the subsample consisting of load funds with a 2-year holding period generates a significantly positive coefficient at the 1% level. For further robustness, Eq. (1) is also estimated by categorising the sample into different sub-periods and investment objectives, namely; capital appreciation funds; growth funds; growth and income funds; mid-cap growth funds; and small-cap growth funds. Since the Strategic Insight objective code ends at September 1998, the Lipper Objective and Classification code is used for sub-periods starting 1998 and 1999, respectively. Panel C of Table 3 shows that the relation between fees and before-fee riskadjusted performance is significantly negative in the 1977 to 1986, 1987 to 1996, and 1997 to 2006 sub-periods. Nevertheless, there is a lack of significance for the 1967 to 1976 sub-period and the coefficient is positive. Similarly, GR (2009) found the coefficient from this sub-period to be not significantly different from zero. They suggest that it could be due to the lower number of fund observations in the sub-period which results in larger standard errors. Moreover, the first three sub-periods generate higher adjusted R2. This could also be due to lower fund observations in these sub-periods. Table 2 Before-fee risk-adjusted performance and expense ratios. Risk-adjusted performance

Method

Standard errors

Coefficient

T-stat

Adj. R2

Carhart

Pooled OLS

White

−28.47

3.15

Carhart

Pooled OLS

Clustered by time

−7.62

3.15

Fama–French

Pooled OLS

White

−27.45

3.31

Fama–French

Pooled OLS

Clustered by time

−7.81

3.31

Carhart

Fixed effects

Heteroscedasticity-robust

−1.9261*** (0.0677) −1.9261*** (0.2527) −1.9493*** (0.0710) −1.9493*** (0.2496) 0.0924*** (0.0024)

38.39

8.55

This table shows estimated slope coefficients for the OLS regression of funds' monthly before-fee risk-adjusted performance on monthly expense ratios in the period from 1962 to 2011. Coefficients are estimated using Eq. (1) in the paper. Rows (1) and (2) report univariate results using before-fee risk-adjusted performance estimated using the Carhart four factor model. Rows (3) and (4) report univariate results using before-fee risk-adjusted performance estimated using Fama–French three factor model. Row (5) reports panel regression results with both fund and time fixed effects. Standard errors (in parentheses) are clustered by time (month) and adjusted for heteroscedasticity using the White estimator. Adjusted R2 statistics are in percentage. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. Number of observations is 346,085.

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Table 3 Regression by subsamples. Subsample

Period

Coefficient

T-stat

P-value

Adj. R2

Obs.

Panel A: effect of small funds Deciles 2–10

1967–2011

−1.5682*** (0.3531)

−4.40

0.0010

2.79

261,254

Panel B: other fees No-load Funds

1967–2011

−5.97

b0.0001

4.01

109,001

Load funds (2-year holding period)

1967–2011

6.08

b0.0001

2.67

232,198

Load funds (7-year holding period)

1967–2011

−1.5305*** (0.2564) 0.9135*** (0.1502) −0.9009*** (0.2209)

−4.08

0.0018

2.51

232,198

1.3666 (1.1592) −1.0830** (0.4272) −0.5930* (0.3098) −1.3376*** (0.3310) 0.6114** (0.2766)

1.18

0.2633

5.16

23,072

−2.54

0.0277

13.04

32,899

−1.91

0.0820

8.10

50,441

−4.30

0.0013

2.09

122,212

2.21

0.0492

1.56

112,575

−4.00

0.0021

2.57

3641

−2.57

0.0262

8.39

1383

−1.59

0.1406

14.62

10,152

−2.76

0.0186

7.20

17,161

−1.19

0.2588

7.08

−1.26

0.2325

1.79

20,713

−0.98

0.3493

4.56

20,669

0.21

0.8349

2.78

68,146

−1.07

0.3068

1.25

102,944

−0.53

0.6081

3.02

20,219

Panel C: regressions by subperiods 1967–1976 1977–1986 1987–1996 1997–2006 2007–2011

Panel D: regression by investment objective (Strategic Insight objective code) Aggressive Growth Funds 1992–2005 −2.8355*** (0.7096) Growth MidCap Funds 1992–2005 −2.4826** (0.9672) Growth and Income Funds 1992–2005 −0.5047 (0.3178) Growth Funds 1992–2005 −1.0208** (0.3701) 1992–2005 −0.9177 Small Company Growth Funds (0.7706) Panel E: regression by investment objective (Lipper Objective and Classification code) Capital Appreciation Funds 1998–2011 −0.6267 (0.4960) Mid-Cap Growth Funds 1999–2011 −0.4154 (0.4249) Growth and Income Funds 1998–2011 0.0700 (0.3280) Growth Funds 1998–2011 −0.2199 (0.2052) Small-Cap Growth Funds 1999–2011 −0.2348 (0.4448)

5859

This table shows estimated slope coefficients for the OLS regression of funds' monthly before-fee risk-adjusted performance on monthly fees from 1962 to 2011 for Panels A to C, and from 1996 to 2011 for Panel D. Betas are estimated using Carhart's four-factor model with a 5-year estimation period. Risk-adjusted performance in month t is the difference between the fund's monthly before-expense return and the product of betas and the factor realisations in t. Monthly fees are defined as the annual expense ratio divided by 12, except for Panel B, where monthly fees are computed as annual expense ratios divided by 12 plus the sum of front-end and back-end loads divided by the assumed holding period in months. In Panel A the sample excludes each month the decile of fund-month observations with the lowest total net assets among all actively managed retail funds. In Panel B No-load Funds are funds with total loads equal to zero. Standard errors are clustered by time and reported in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively.

Panel D of Table 3 shows that the coefficient is negative for all five subsamples based on different investment objectives. There is however, a lack of significance for “Growth and Income” and “Small Company Growth” funds. Lastly with the exception of growth funds, in Panel E of Table 3, regression by investment objective using the Lipper Objective and Classification code produces coefficients that are negative but insignificantly different from zero. Overall, the results in Table 3 show that the negative relation between fees and performance established in Table 2 is mostly robust with the exception of Panel E where subsamples were formed according to the Lipper Objective and Classification code. 5.3. Explaining the puzzle using investor sentiment, along with cost-based and strategic explanations Using the coefficient of funds' expense ratio generated using Eq. (1) and the BW (2006) composite sentiment index, we compare the two trends shown in Fig. 1. The graph shows that high investor sentiment is associated with a strong negative relation between fees and before-fee performance, and vice versa. This suggests a potential role of investor sentiment in the mutual fund fee–performance relationship.

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To explore reasons for the puzzle's existence, the following multivariate model is estimated: 0

f it ¼ γ X it−1 þ λS Sit þ λT T it þ λα α it þ vit

ð3Þ

where fund fees, fit is a linear function of four components, i.e. cost-based explanation, strategic explanation, investor sentiment explanation and the expected before-fee performance (αit). Table 4 shows that the performance sensitivity measure has a much greater standard deviation than that of the BW composite sentiment index. Also, after rolling regressions, the mean and standard deviation of the age of funds is 3.04 and 0.64, respectively. This is much smaller when compared to the age of funds in Table 1. Following GR (2009), cost-based explanation is proxied by the fund's operating cost which is determined by lagged values of a number of different variables, Xit−1. The variables identified as likely to influence operating costs of a fund are, size of a fund (denoted as Size), computed as log of the year-end total net asset (TNA) value; age of a fund (denoted as Age), computed as the log of the number of years since fund inception; complex size (denoted as Co.Size), computed as the log of TNA value of all funds managed by the management company; number of funds in complex (denoted as #funds), computed as the number of funds managed by the management company; reported annual turnover (denoted as Turnover); and volatility of a fund's returns (denoted as σt), computed as the standard deviation of the fund's monthly returns in the year. Dummy variables are also included for time, different fee structures, and fund's investment objective. Strategic explanation is proxied by the performance-sensitivity of mutual fund flows, Sit. To construct this variable, we follow the Christoffersen and Musto (2002) measure shown in Eq. A.3 in Appendix A. We extend the GR (2009) empirical model by including an investor sentiment measure, proxied by the Baker and Wurgler (2006) composite sentiment index, Tit. Pooled OLS regressions will be performed using different variables for the dependent variable, fit. Mainly, total ownership cost (TOC), calculated as total loads divided by seven plus the annual expense ratio, marketing fee, calculated as total loads divided by seven plus 12b-1 fees and non-marketing fee, calculated as expense ratio minus 12b-1 fees. In Table 5, we examine fund characteristics according to size quintiles. As mentioned earlier, GR (2009) discuss that small and young funds typically perform poorly and charge higher fees. This is consistent with the descriptive statistics shown in Panels A and B of Table 5. Funds in the smallest (largest) size quintile have the lowest (highest) gross of fee alpha and the highest (lowest) fees, measured by total ownership cost. Panel C of Table 5 shows that the performance sensitivity of funds across each quintile does not differ much. The difference in mean performance sensitivity between the largest and smallest quintiles is 4.76%. To observe how well investor sentiment elucidate the negative relation between fees and expected before-fee risk-adjusted performance, in Table 6, Eq. (3) is estimated first using TOC as the dependent variable. Column (1) of Table 6 shows the results of estimating Eq. (3) without the performance sensitivity and investor sentiment measure. If changes in fund's operating cost can explain the negative relation between fees and before-fee risk-adjusted performance, then the coefficient of the expected performance should become positive, zero, or become statistically insignificant. This result is not attained as shown in column (1). Although the coefficients of the cost-based explanation variables are all significant, the coefficient of expected performance is still significantly negative at the 5% level. Nonetheless, it suggests little economic significance as the magnitude of the coefficient is small (−21 basis points). A 1% increase in before-fee risk-adjusted performance results in a decrease in fees by 0.21%. More importantly, the coefficient of expected performance remains virtually unchanged in columns (2), (3) and (4) where the model includes the performance sensitivity and investor sentiment measure. This suggests that including performance sensitivity and investor sentiment in the model does not eliminate the negative relation between fees and before-fee risk-adjusted 2.00 1.50 1.00 0.50 0.00 -0.50 -1.00 -1.50 -2.00 -2.50 -3.00 -3.50 -4.00 -4.50 Coefficient of Funds' Expense Ratio

Composite Sentiment Index

Source: Composite sentiment index from 1971 to 2010is from BW (2006). Fig. 1. Coefficient of funds' expense ratio and composite sentiment index.

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Table 4 Summary statistics of sentiment variables, performance sensitivity, and other fund characteristics. Variable

Obs.

Mean

S. D.

Median

Minimum

Maximum

Sizet−1 Aget−1 Co . Sizet−1 #fundst−1 Turnovert−1 σt−1 αt St Tt DIVt NIPOt RIPOt CEFt ISSUEt NYSEt

21,980 22,096 21,980 22,112 21,256 19,033 25,759 23,544 23,947 23,947 23,947 23,947 23,947 23,947 23,947

2.6305 3.0446 4.2326 11.156 82.3684 15.2414 3.1096 58.5218 0.0103 −6.1837 257.9304 14.6163 7.2897 0.1411 0.9008

0.5631 0.6402 1.5550 12.8290 72.7280 16.9538 7.11530 52.9914 0.6314 10.0470 211.6429 11.6874 5.5438 0.0761 0.3572

2.6333 2.8332 4.3509 6.0000 65.0000 8.7664 2.5662 57.0633 −0.0171 −8.5302 201.0000 12.0174 6.1600 0.1385 0.9416

−0.2485 1.3863 −0.2485 1.0000 0.0000 0.0000 −48.4422 0.0054 −2.2374 −33.7925 9.0000 −1.6714 −10.9100 0.0489 0.1497

5.3880 4.4659 7.7224 62.0000 1239.0000 117.5052 67.3616 4807.6900 2.6915 28.2405 953.0000 69.5335 23.5337 0.4300 1.4284

This table shows the summary statistics of the final sample consisting of 3442 mutual funds from 1967 to 2010. Sizet−1 is the log of the year-end total net asset value. Aget−1 is the log of the number of years since fund inception. Co.Sizet−1 is the size of management company and #fundst−1 is the number of funds under management company. Turnovert−1 is the fund's reported annual turnover and σt−1 is volatility, defined as the standard deviation of the fund's monthly returns in the year. Expected performance, α t is the estimated alpha calculated using Carhart's (1997) two stage estimation process. St, is a proxy for performance sensitivity in year t. Investor sentiment is proxied by the BW (2006) composite sentiment index (denoted by Tt). DIV is dividend premium; NIPO is the number of initial public offerings; RIPO is average first-day returns on initial public offerings; CEF is closed-end fund discount; ISSUE is equity share in new issues; and NYSE is New York Stock Exchange share turnover. All figures except Aget−1 and #fundst−1 are presented in percentage.

performance. Column (4) reports the results of estimating the full model (Eq. 3). Both the coefficients of performance sensitivity and investor sentiment variable are significant at the 1% level but the magnitude is much larger for the investor sentiment variable (−0.0467). This implies that in comparison to performance sensitivity, a change in investor sentiment results in a greater change in fees. Also, the higher magnitude of the investor sentiment variable suggests that it is a more important determinant of fees than performance sensitivity. The coefficient of the investor sentiment variable from columns (3), (4) and (5) are all significantly negative. This is consistent with our hypothesis that investor sentiment is negatively associated with fees. However, our results indicate that investor sentiment is not the solution to the puzzle as the coefficient of expected before-fee performance in columns (3), (4) and (5) remains negative and statistically significant. In column (5), alpha squared is added to the model to account for possible nonlinearities as in GR (2009). As column (5) shows, the positive coefficient suggests that the slope describing the relation between fees and performance becomes flatter for funds with better performance. In columns (6) and (7) of Table 6, the dependent variable is marketing fee and non-marketing fee, respectively. Marketing fee is defined as total loads divided by seven, and non-marketing fees is defined as the expense ratio minus 12b-1 fees. Both columns (6) and (7) show that the coefficients of before-fee risk-adjusted performance are negative but more importantly, lacking in significance. Unlike columns (3), (4) and (5), the coefficients of the sentiment variable in columns (6) and (7) are positive. Contrary to our hypotheses, our results in columns (6) and (7) indicate that investor sentiment is instead positively related to fees and that the negative relation between fees and before-fee performance cannot be observed. Also, the magnitudes of the sentiment coefficients are much larger than that of performance sensitivity. Moreover, the coefficient of the performance sensitivity measure is significant in column (7) but not (6). Thus, it shows that investor sentiment has better explanatory power than performance sensitivity and can be responsible for the lack of negative association between fees and before-fee performance. Still, we do not dismiss other potential reasons for the lack of significance of gross of fee alpha. Between 2006 and 2010, funds could be shifting towards spending less on marketing and perhaps, spending more on other areas. It may also be due to unsophisticated investors being less receptive to advertising efforts by worst-performing funds. Previously in Table 3, we show that the negative relation between fees and before-fee risk-adjusted performance still stands for various subsamples estimated using Eq. (3). In Table 7, funds' investment objectives are taken into account by adding dummy variables (not reported) for each investment objective for the 1993 to 2010 period. Contrary to Table 6, fund observations prior to 1993 are not included in the regression shown in Table 7. This is because data on different classification of funds by investment objective is consistent only after 1993. The results in Table 7 and Table 6 are different in the sense that there is lack of significance for the coefficient of risk-adjusted performance for all model specifications except in column (5) where alpha squared is included. This is likely due to the disappearance of the fee–performance anomaly for subperiod 2007 to 2011 shown in Table 3. Nevertheless, univariate regression result (not shown here) for the 1993 to 2010 period using Eq. (3) indicates that fees are overall still negatively related to risk-adjusted performance. Slope of the expense ratio is significantly negative (−0.69) at the 5% level. While the magnitude and significance level of the performance sensitivity coefficients in columns (2), (4), and (5) from Table 7 remain unchanged, the magnitude and standard errors of the sentiment coefficients in columns (3), (4), and (5) are much larger and positive rather than negative as shown in Table 6. This implies that sentiment is an important determinant of fees but is not consistent with our hypothesis since it also shows that sentiment is positively related to fees. Again, this outcome might be due to sub-period differences.

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Table 5 Summary statistics of sentiment variables, performance sensitivity in quintiles. Variable Panel A TOC

Panel B α̂t

Panel C St

Panel D Tt

Panel E DIVt

Panel F NIPOt

Panel G RIPOt

Panel H CEFt

Panel I ISSUEt

Panel J NYSEt

Size quintile

Obs.

Mean

S. D.

Median

1 Small 2 3 4 5 Large

5151 5154 5150 5153 5151

2.20 2.06 1.96 1.79 1.63

0.64 0.55 0.55 0.57 0.55

2.18 2.09 2.03 1.88 1.71

1 Small 2 3 4 5 Large

5151 5154 5150 5153 5151

1.70 2.79 3.31 3.88 3.87

6.75 7.25 7.46 7.25 6.59

1.37 2.07 2.69 3.42 3.38

1 Small 2 3 4 5 Large

4619 4601 4691 4674 4959

58.06 54.49 57.05 59.86 62.82

47.19 37.63 41.25 81.60 44.99

55.56 52.59 56.43 55.90 62.99

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

−0.08 −0.06 0.03 0.06 0.10

0.56 0.68 0.66 0.65 0.58

−0.02 −0.02 −0.02 −0.01 0.01

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

−4.44 −5.05 −6.50 −6.97 −7.79

9.95 10.71 10.32 9.86 8.96

−8.53 −8.53 −9.21 −9.21 −9.21

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

178.57 220.57 258.40 302.34 322.52

149.43 192.35 209.80 231.78 227.25

194.00 197.00 201.00 213.00 227.00

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

12.31 13.64 14.89 15.20 16.83

8.78 10.43 12.03 11.84 13.99

9.95 12.02 12.02 12.26 12.34

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

7.95 7.67 7.04 7.07 6.78

5.59 6.01 5.55 5.49 4.96

9.58 6.16 6.16 6.16 6.16

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

0.15 0.14 0.14 0.14 0.13

0.08 0.08 0.08 0.08 0.07

0.16 0.14 0.12 0.14 0.12

1 Small 2 3 4 5 Large

4511 4794 4834 4888 4920

1.04 0.91 0.88 0.83 0.85

0.35 0.38 0.36 0.35 0.32

1.06 0.96 0.94 0.89 0.89

This table shows the summary statistics of the final sample consisting of 3442 mutual funds from 1967 to 2010 sorted according to size (year-end TNA) quintiles. Total Ownership Cost (TOC) is the total loads divided by seven plus the annual expense ratio. Expected performance, α t is the estimated alpha calculated using Carhart (1997) two stage estimation process. St, is a proxy for performance sensitivity in year t. Investor sentiment variables are the same as the ones described in Table 4.

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Table 6 Mutual fund characteristics and composite sentiment index (without investment objective dummies). TOC

Sizet−1 Aget−1 Co . Sizet−1 #fundst−1 Turnovert−1 σt−1 α̂t

Mark.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.4457*** (0.0316) −0.2444*** (0.0309) 0.2348*** (0.0263) −0.0134*** (0.0016) 0.0003** (0.0001) 0.0014*** (0.0004) −0.0021** (0.0009)

−0.4345*** (0.0323) −0.2590*** (0.0323) 0.2332*** (0.0261) −0.0137*** (0.0016) 0.0002 (0.0001) 0.0015*** (0.0004) −0.0026*** (0.0009)

−0.4429*** (0.0317) −0.2447*** (0.0309) 0.2353*** (0.0263) −0.0135*** (0.0016) 0.0003** (0.0001) 0.0014*** (0.0004) −0.0019** (0.0009)

−0.4312*** (0.0324) −0.2592*** (0.0323) 0.2338*** (0.0261) −0.0138*** (0.0016) 0.0002 (0.0001) 0.0015*** (0.0004) −0.0022** (0.0009)

−0.2288*** (0.0259) −0.1000*** (0.0218) 0.1630*** (0.0214) −0.0066*** (0.0012) −0.0004*** (0.0001) 0.0004 (−0.0003) −0.0011 (0.0007)

−0.0153 (0.0276) −0.1538*** (0.0189) −0.0375 (0.0250) −0.0042*** (0.0013) 0.0008*** (0.0001) 0.0019*** (0.0003) −0.0001 (0.0007)

−0.0264** (0.0109) 16,790 22.3

−0.0022*** (0.0004) −0.0410*** (0.0121) 15,710 26.43

−0.4341*** (0.0321) −0.2561*** (0.0322) 0.2364*** (0.0258) −0.0138*** (0.0016) 0.0002 (0.0001) 0.0012*** (0.0004) −0.0051*** (0.0011) 0.0003*** (0.0001) −0.0021*** (0.0004) −0.0467*** (0.0121) 15,710 26.67

−0.0022 (0.0002) 0.8885*** (0.1258) 11,996 27.72

−0.0012*** (0.0002) 0.6704*** (0.1231) 11,669 16.05

2

α̂t

−0.0022*** (0.0004)

St Tt Obs. Adj. R2

N-Mark.

16,790 22.26

15,710 26.34

This table reports estimated coefficients for yearly regressions of funds' fees on selected fund characteristics and investor sentiment from 1967 to 2010 using Eq. (3) in the paper. The dependent variable in columns (1) to (5) is the total annual ownership cost (TOC), computed as total loads divided by seven plus the annual expense ratio. In columns (6) and (7), the dependent variable is marketing fees (Mark.), defined as the total loads divided by seven plus 12b-1 fees, and nonmarketing fees (N-Mark.), calculated as the expense ratio minus 12b-1 fees, respectively. Size and Age are the size and age of a fund in year t. The size and number of funds in the management company are denoted by Co.Size and #funds, respectively. Turnover denotes reported annual turnover in year t. σt is the standard deviation of the fund's monthly returns in year t. α t is year t Carhart four-factor alpha. St, is a proxy for performance sensitivity in year t. Investor sentiment is proxied by the BW (2006) composite index (denoted by Tt). All regressions include year dummies. All standard errors are clustered by fund and shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Table 7 Mutual fund characteristics and composite sentiment index (with investment objective dummies). TOC

Sizet−1 Aget−1 Co . Sizet−1 #fundst−1 Turnovert−1 σt−1 α̂t

Mark.

N-Mark.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.3907*** (0.0327) −0.2419*** (0.0293) 0.2454*** (0.0264) −0.0144*** (0.0016) 0.0003** (0.0001) 0.0023*** (0.0004) −0.0013 (0.0011)

−0.3733*** (0.0325) −0.2739*** (0.0298) 0.2422*** (0.0259) −0.0148*** (0.0016) 0.0003** (0.0001) 0.0020*** (0.0004) −0.0011 (0.0010)

−0.3907*** (0.0327) −0.2419*** (0.0293) 0.2454*** (0.0264) −0.0144*** (0.0016) 0.0003** (0.0001) 0.0023*** (0.0004) −0.0013 (0.0011)

−0.3733*** (0.0325) −0.2739*** (0.0298) 0.2422*** (0.0259) −0.0148*** (0.0016) 0.0003** (0.0001) 0.0020*** (0.0004) −0.0011 (0.0010)

−0.2220*** (0.0260) −0.1008*** (0.0215) 0.1635*** (0.0215) −0.0067*** (0.0012) −0.0004*** (0.0001) 0.0006* (0.0003) −0.0009 (0.0007)

−0.0164 (0.0274) −0.1479*** (0.0188) −0.0370 (0.02498) −0.0043*** (0.0013) 0.0008*** (0.0001) 0.0019*** (0.0003) 0.0003 (0.0007)

2.3763*** (0.1662) 13,053 21.26

−0.0034*** (0.0003) 2.0347*** (0.1658) 12,832 24.57

−0.3764*** (0.0323) −0.2718*** (0.0298) 0.2444*** (0.0257) −0.0148*** (0.0015) 0.0002* (0.0001) 0.0019*** (0.0004) −0.0032*** (0.0012) 0.0003*** (0.0001) −0.0033*** (0.0003) 2.0581*** (0.1654) 12,832 24.78

−0.0022*** (0.0002) 0.8744*** (0.1259) 11,669 26.71

−0.0013*** (0.0002) 0.6504*** (0.1214) 11,669 16.40

2

α̂t

−0.0034*** (0.0003)

St Tt Obs. Adj. R2

13,053 21.26

12,832 24.57

This table reports estimated coefficients for yearly regressions of funds' fees on selected fund characteristics and investor sentiment from 1993 to 2010 using Eq. (3). This time period is use specifically to account for the different fund investment objectives which are consistent only after 1993. The dependent variable in columns (1) to (5) is total annual ownership cost (TOC), computed as total loads divided by seven plus the annual expense ratio. In columns (6) and (7), the dependent variable is marketing fees (Mark.), defined as the total loads divided by seven plus 12b-1 fees, and nonmarketing fees (N-Mark.), calculated as the expense ratio minus 12b-1 fees, respectively. All the variables are the same as the ones in Table 6. All regressions include year dummies and dummy variables for the different investment objectives. All standard errors are clustered by fund and shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

M. Hu et al. / International Review of Economics and Finance 42 (2016) 134–152

145

To identify how each of the six proxies for investor sentiment interact with fund fees and before-fee performance, Eq. (3) is estimated where the composite investor sentiment variable is decomposed into its individual proxies, i.e. closed-end fund discount (CEF); NYSE share turnover (NYSE); number of initial public offerings (NIPO); average first-day returns on initial public offerings (RIPO); equity share in new issues (ISSUE); and dividend premium (DIV). The following model is estimated: 0

f it ¼ γ X it−1 þ λS Sit þ λD DIV t þ λN NIPOt þ λR RIPOt þ λC CE F t þ λE ISSUEt þ λN NYSEt þ λα α it þ vit

ð4Þ

where fund fees, fit is a linear function of four components, i.e. cost-based explanation, strategic explanation, investor sentiment explanation and the expected before-fee performance (αit). Composite sentiment index (Tt) from Eq. (3) is decomposed into six different proxies for investor sentiment. Table 8 presents the regression results of using the individual components of the investor sentiment index. We find that fund fees are negatively associated with dividend premium (DIV), number of initial public offerings (NIPO), equity share in new issues (ISSUE), and New York Stock Exchange share turnover (NYSE). The other two variables, average first-day returns on initial public offerings (RIPO) and closed-end fund discount (CEF) are positively associated with fund fees. Using TOC as the dependent variable, columns (3), (4) and (5) show that the coefficients of all six sentiment proxies are significantly different from zero, with the NYSE share turnover and share of equity issues having the greatest magnitude. According to Baker and Wurgler (2007), investor sentiment is positively related to the number of initial public offerings (NIPO), average first-day returns on initial public offerings (RIPO), equity share in new issues (ISSUE), and NYSE share turnover (NYSE). Conversely, closed-end fund discount (CEF) and dividend premium (DIV) are inversely related to investor sentiment. Taking this into account, we summarise our results from columns (3) to (7) of Table 8 as follows in the scenario where investor sentiment is high. Firstly, dividend premium decreases, meaning firms' propensity to pay dividends fall. This in turn triggers an increase in fees. Secondly, there is an increase in IPO activity and fees decrease. Thirdly, IPO first day returns are high and fees increase. Fourth, closed-end fund discount shrinks and fees decrease. Fifth, equity share in new issues increases and fees decrease. And lastly, market turnover is high and fees decrease. Table 8 Mutual fund characteristics and the six proxies for investor sentiment. TOC

Sizet−1 Aget−1 Co . Sizet−1 #fundst−1 Turnovert−1 σt−1 α̂t

Mark.

N-Mark.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.3907*** (0.0327) −0.2419*** (0.0293) 0.2454*** (0.0264) −0.0144*** (0.0016) 0.0003** (0.0001) 0.0023*** (0.0004) −0.0013 (0.0011)

−0.3733*** (0.0325) −0.2739*** (0.0298) 0.2422*** (0.0259) −0.0148*** (0.0016) 0.0003** (0.0001) 0.0020*** (0.0004) −0.0011 (0.0010)

−0.3907*** (0.0327) −0.2419*** (0.0293) 0.2454*** (0.0264) −0.0144*** (0.0016) 0.0003** (0.0001) 0.0023*** (0.0004) −0.0013 (0.0011)

−0.3733*** (0.0325) −0.2739*** (0.0298) 0.2422*** (0.0259) −0.0148*** (0.0016) 0.0003** (0.0001) 0.0020*** (0.0004) −0.0011 (0.0010)

−0.2220*** (0.0260) −0.1008*** (0.0215) 0.1635*** (0.0215) −0.0067*** (0.0012) −0.0004*** (0.0001) 0.0006* (0.0003) −0.0009 (0.0007)

−0.0164 (0.0274) −0.1479*** (0.0188) −0.0370 (0.0250) −0.0043*** (0.0013) 0.0008*** (0.0001) 0.0019*** (0.0003) 0.0003 (0.0007)

−0.0074*** (0.0019) −0.0047*** (0.0003) 0.0755*** (0.0060) 0.0303*** (0.0032) −4.2988*** (0.2884) −0.5107*** (0.0780) 13,053 21.26

−0.0034*** (0.0003) −0.0107*** (0.0019) −0.0048*** (0.0003) 0.0854*** (0.0060) 0.0343*** (0.0032) −4.4366*** (0.2837) −0.6012*** (0.0780) 12,832 24.57

−0.3764*** (0.0323) −0.2718*** (0.0298) 0.2444*** (0.0257) −0.0148*** (0.0015) 0.0002* (0.0001) 0.0019*** (0.0004) −0.0032*** (0.0012) 0.0003*** (0.0001) −0.0033*** (0.0003) −0.0114*** (0.0019) −0.0048*** (0.0003) 0.0845*** (0.0059) 0.0338*** (0.0032) −4.3926*** (0.2811) −0.5947*** (0.0779) 12,832 24.78

−0.0022*** (0.0002) −0.0045*** (0.0014) −0.0022*** (0.0002) 0.0426*** (0.0043) 0.0173*** (0.0023) −2.2271*** (0.2087) −0.2891*** (0.0529) 11,669 26.71

−0.0013*** (0.0002) −0.0023* (0.0012) −0.0019*** (0.0002) 0.0354*** (0.0045) 0.0122*** (0.0023) −1.8175*** (0.1981) −0.3582*** (0.0590) 11,669 16.4

2

α̂t

−0.0034*** (0.0003)

St DIVt NIPOt RIPOt CEFt ISSUEt NYSEt Obs. Adj. R2

13,053 21.26

12,832 24.57

The table reports estimated coefficients for yearly regressions of funds' fees on selected fund characteristics and investor sentiment from 1993 to 2010 using Eq. (4) in the paper. Composite sentiment index (Tt) is decomposed into six different proxies for investor sentiment: DIV, NIPO, RIPO, CEF, ISSUE, and NYSE. Dependent variables (TOC, Mark., and N-Mark.), cost-based explanation component variables (Size, Age, Co.Size, #funds, and Turnover), and performance sensitivity measure (St) are described in Appendix B. All regressions include year dummies and dummy variables for the different investment objectives. All standard errors are clustered by fund and shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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Therefore, this bolsters the argument for using the BW composite sentiment index as each of the six proxies contributes in part to the negative relation between fees and performance. From columns (3) to (7) of Table 8, negative loadings on dividend premium (DIV) suggest that when relative investor demand for dividend-paying funds increases, fund fees decrease. Nonetheless, BW (2006) state that even if dividend premium is negative, fund managers may decide to initiate dividends and benefit from positive announcement effects. Loadings on number of initial public offerings (NIPO) and average first-day returns (RIPO) are opposite in signs. This is consistent with expectations. Negative loadings on NIPO suggest that increasing IPO activity is associated with lower fees as one would expect from IPO underpricing. Conversely, positive loadings on RIPO suggest that it is positively associated with fees and indicate investor enthusiasm. Lee, Shleifer, and Thaler (1991) posit that fluctuations in noise trader sentiment drive the closed-end fund discount (CEF) as rational investors would demand higher returns for an increase in risk associated with noise trader sentiment. Thus, the closed-end fund discount widens (narrows down) when sentiment is low (high). This reasoning corroborates with the results in Table 8 as positive coefficients of CEF indicate that fees increase as the closed-end fund discount widens. Among the six proxies for sentiment, the share of equity issue in total equity and debt issues (ISSUE) generates the most negative and largest coefficient in terms of magnitude. This suggests that fees are highly sensitive to equity issues. Baker and Wurgler (2000) find that high values of equity issue are associated with low expected returns. If this relation holds, then the results can be explained as the outcome of fund managers' response to equity issues. Lastly, negative loadings on NYSE share turnover (NYSE) are also consistent with expectations. NYSE share turnover is a measure of liquidity and captures the effect of sentiment (BW, 2006). In a market with short-sale constraints and interaction between rational and quasi-rational investors, high NYSE indicates overvaluation resulting from greater participation of quasi-rational investors (Baker & Stein, 2004). Besides, high turnover is associated with low expected market returns. If high turnover is associated with low expected fund performance, then managers should respond to added risk imposed by noise traders by reducing fees. In Table 9, we test whether the results obtained in Tables 6 and 8 are robust to different sub-periods. It is noted that the number of observations increases dramatically for each sub-period. This is reasonable with reference to Figs. 2 and 3. Fig. 2 shows that TNA increases sharply from the late 1980s to 1997 before plummeting in subsequent years and Fig. 3 shows rapid growth in number of funds beyond the 1980s. Regardless, Panel A of Table 9 shows that the magnitude of the coefficient of composite sentiment index is larger than that of performance sensitivity for all sub-periods. For sub-period 1987 to 1996, both coefficients of before-fee risk-adjusted performance and composite sentiment index are negative and significantly different from zero at the 1% level whereas the coefficient of the performance sensitivity variable is not significantly different from zero. This suggests that the sentiment variable is an important determinant of fees and has stronger explanatory power than performance sensitivity. Nevertheless, the results for the subsequent sub-period (1997–2005) are completely opposite. Coefficient of the performance sensitivity variable is negative and significantly different from zero at the 1% level while the coefficients for risk-adjusted performance and investor sentiment are not significantly different from zero. Additionally, the adjusted R2 increased to 28%, signifying a better fit of the model. Table 9 Regression by subperiods: comparing performance sensitivity and investor sentiment explanations. Panel A: Using the BW composite sentiment index Subperiod

α̂t

St

Tt

Adj. R2

1967–1976

−0.0087** (0.0039) 0.0007 (0.0035) −0.0101*** (0.0025) −0.0013 (0.0017) 0.0028*** (0.0010)

0.0002 (0.0005) 0.0002 (0.0005) −0.0008 (0.0007) −0.0017*** (0.0004) −0.0049*** (0.0004)

−0.1487 (0.2870) −0.0185 (0.1004) −0.1508*** (0.0562) 0.4004 (0.5883) 1.6005*** (0.1197)

19.84

517

16.67

1200

15.04

2375

28.00

5140

17.76

6478

1977–1986 1987–1996 1997–2005 2006–2010

Obs.

Panel B: using six proxies for investor sentiment

1967–1976 1977–1986 1987–1996 1997–2005 2006–2010

α̂t

St

DIVt

NIPOt

RIPOt

CEFt

ISSUEt

NYSEt

Adj. R2

−0.0088*** (0.0039) 0.0014 (0.0032) −0.0105*** (0.0025) −0.0013 (0.0017) 0.0028*** (0.0010)

0.0002 (0.0005) 0.0002 (0.0005) −0.0008 (0.0007) −0.0017*** (0.0004) −0.0049*** (0.0004)

0.0014 (0.0032) 0.0048* (0.0026) −0.0780** (0.0332) −0.0046 (0.0052) 0.0167*** (0.0029)

−0.0002 (0.0002) −0.0002*** (0.0001) 0.0042** (0.0019) −0.0005*** (0.0002) 0.0001 (0.0002)

0.0006 (0.0037) −0.001 (0.0012) −0.1313** (0.0575) −0.0024 (0.0055) 0.0381*** (0.0037)

−0.0076 (0.0046) 0.0005 (0.0026) 0.0747*** (0.0272) 0.0479*** (0.0126) −0.0271*** (0.0032)

0.0520 (0.6176) 0.4825* (0.2521) −22.2678** (9.6403) 2.0656 (3.0199) 0.0000 (0.0000)

−3.5436** (1.7918) 0.4770 (0.3842) −6.4538** (2.7284) −0.2306 (0.2690) (0.000) (0.000)

19.98

517

16.67

1200

15.04

2375

28.05

5140

17.76

6478

Obs.

This table shows the estimated coefficients for yearly regressions in different subperiods using the full model in Eq. (3). Only the coefficients of estimated beforefee risk-adjusted performance (α t ), performance sensitivity (St), and composite sentiment index (Tt) are presented. In panel B, Eq. (4) is estimated instead. The dependent variable in both models is total annual ownership cost (TOC). As in Appendix B, cost-based explanation component (Xit−1) comprises of Size, Age, Co.Size, #funds, and Turnover. All regressions include year dummies. All standard errors are clustered by fund and shown in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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147

900.00 800.00 700.00 600.00 500.00 400.00 300.00 200.00 100.00 2010

2008

2006

2004

2002

2000

1996

1998

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

0.00

Total TNA (billions of USD) Fig. 2. Total net assets.

In the most recent sub-period (2006–2010), coefficient of expected before-fee performance is positive and significantly different from zero. This implies that there is a positive relation between fees and before-fee performance rather than a negative one. Besides this, the coefficients of performance sensitivity and composite sentiment index are both significantly different from zero but the coefficient of the sentiment variable is positive and much greater in magnitude (1.6005). This implies that during the period between 2006 and 2010, investor sentiment is positively related to fees. It can be inferred from Fig. 4 that sentiment is comparatively low for the two sub-periods for which the estimated sentiment coefficients are significant. The negative coefficient for sub-period 1987 to 1996 could be due to the increase in sentiment from 1995 to 1996. For the remaining three sub-periods, there are large fluctuations in the composite sentiment index for each sub-period. In Panel B of Table 9, we show the results of using individual proxies for sentiment estimated using Eq. (4) for different sub-periods. The results are undesirably mixed. In the two subperiods (1987–1996 and 2007–2011) where there are fewer fluctuations in the composite sentiment index, the coefficients of all sentiment proxies are not consistent in terms of sign and magnitude. In addition, the coefficients of three sentiment proxies, i.e. NIPO, ISSUE and NYSE are not significantly different from zero. Thus, it appears that the composite sentiment index performs better than the six underlying proxies. 5.4. Fund governance and fees Table 10 shows the summary statistics of funds in 2011 that are rated by Morningstar's Stewardship grade system. The table indicates that majority of funds have “Fair”, followed by “Good” grades. Since only 12 funds were graded “Very Poor”, we consider these funds as “Poor” funds. Also, among the 4486 funds in the 2011 subsample, only 1459 funds were found to have information on Stewardship grade. Similar to the governance sample in GR (2009), these funds tend to be older, have higher than average size (TNA), and lower expense ratios than the overall 1962 to 2011 sample. Care has to be taken in the interpretation of results in this section as the Morningstar stewardship grade obtained is a measure of five different components. Although this is the case, we assume that the grade reflects the board quality component sufficiently. Moreover, the subsample used in Table 11 is not a random sample of the population. To analyse how fund governance influences fund fees and test the robustness of investor sentiment, we extend Eq. (3) by introducing four dummy variables to reflect each Morningstar stewardship grade. According to GR (2009), if better fund governance is associated with fees being more in line with performance, we would expect 6000 5000 4000 3000 2000 1000

Number of Funds Fig. 3. Number of funds.

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

0

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3 2 1 0 -1 -2 -3

BW Composite Sentiment index Fig. 4. The Baker and Wurgler (2006) composite sentiment index.

the coefficient of the expected before-fee risk-adjusted performance, αit to be more positive (negative) for funds with better (poorer) fund governance. It is unclear as to how investor sentiment impacts funds with different levels of fund governance. To test the effectiveness of fund governance in the presence of investor sentiment, we estimate the following model: 0

f it ¼ γ X it−1 þ λS Sit þ λT T it þ λα α it þ Poorit þ Fair it þ Goodit þ Excellit þ vit

ð5Þ

where Poorit denotes poor board quality; Fairit denotes fair board quality; Goodit denotes good board quality; and Excellit denotes excellent board quality. All board quality grades are according to Morningstar board quality measure. Table 11 presents the regression results. Following the framework in GR (2009), dummy variable for the “Poor” grade is omitted to allow coefficients associated with ‘Fair”, “Good”, or “Excellent” to represent differences with respect to funds with a “Poor” grade. We find results similar to GR (2009) in that with the exception of column (5) where the dependent variable is marketing fees, funds with “Good” and “Excellent” grades are associated with lower fund fees. In columns (7) and (8), the dependent variable is management fees for the period 2003 to 2010 due to data availability on CRSP. Likewise, loadings on the stewardship grade dummy variables show that funds with better stewardship grades are associated with lower fund fees. Nonetheless, a positive relation between better fund stewardship grade and expected before-fee risk-adjusted performance cannot be observed in Table 11. Firstly, only the coefficients of before-fee risk-adjusted performance associated with “Good” grade are significant in columns (3), (4), (5), and (7). Secondly, these coefficients of before-fee risk-adjusted performance associated with “Good” grade are smaller than the coefficients of performance associated with “Fair” grade. Thus, it does not follow the notion that better fund governance is associated with better fund performance. In fact, columns (3), (4), (5), (7), and (8) indicate that funds associated with “Fair” grades perform better than funds associated with “Good” or “Excellent” grades. Also, comparing columns (2), (3), and (4), the inclusion of investor sentiment variable does not affect the relation between fund governance and fees. Overall, there are two main findings derived from the results. Firstly, worst-governed funds charge higher fees but do not necessarily perform worse than better-governed funds. Secondly, there is no clear evidence that stronger fund governance reduces the effects of investor sentiment. 5.5. Robustness check A number of robustness tests were conducted to verify the effect of investor sentiment on the relation between mutual fund fees and before-fee risk-adjusted performance. Using the Fama and French three factor model to estimate risk-adjusted performance, the interpretation of the results does not change. The loadings on estimated before-fee performance in Eq. (3) remain statistically significant. Hence, the momentum factor does not seem to be a vital factor in estimating risk-adjusted performance of equity mutual funds. Next, we estimate again Eq. (3) using different subsamples. Using a subsample that excludes funds in the lowest size decile to estimate Eq. (3), the relation between fees and risk-adjusted performance is still negative and significant. This test is motivated by the concern Table 10 Summary statistics: Morningstar stewardship grade for mutual funds. Stewardship grade

Frequency % Cumulative

Total

Excellent (A)

Good (B)

Fair (C)

Poor (D)

Very Poor (F)

170 11.65 11.65

326 22.34 34.00

766 52.50 86.50

185 12.68 99.18

12 0.82 100

1459 100

The table shows the distribution of the Morningstar Stewardship Grade for mutual funds in the year 2011, ranging from “Very Poor” (grade F) to “Excellent” (grade A). The first row reports the frequency of each grade; the second row reports the relative frequency (in percentage terms); and the last row reports the cumulative frequency (in percentage terms).

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Table 11 Relation between sentiment, Morningstar Stewardship Grade, and fees.Source: Composite sentiment index from 1971 to 2010 is from Baker and Wurgler (2006).

Sizet−1 Aget−1 Co . Sizet−1 #fundst−1 Turnovert−1 σt−1 Fair Good Excell.

N-Mark. (2003–2010)

TOC

TOC

TOC

TOC

Mark.

N-Mark.

(2003–2010)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

−0.4496*** (0.0583) −0.1936*** (0.0399) 0.3190*** (0.0442) −0.0142*** (0.0026) −0.0012*** (0.0003) 0.0007 (0.0006) −0.2355*** (0.0733) −0.3556*** (0.0775) −0.4948*** (0.1022)

−0.4464*** (0.0553) −0.2109*** (0.0396) 0.3392*** (0.0422) −0.0162*** (0.0025) −0.0012*** (0.0003) 0.0007 (0.0006) −0.1512** (0.0761) −0.2910*** (0.0835) −0.2613** (0.1125) 0.0008 (0.0031) 0.0018 (0.0034) −0.0048 (0.0038) 0.0048 (0.0045) −0.0030*** (0.0010) −0.0007 (0.0011) 0.0003 (0.0012) −0.0026* (0.0013)

−0.4531*** (0.0575) −0.1994*** (0.0395) 0.3256*** (0.0436) −0.0144*** (0.0026) −0.0012*** (0.0003) 0.0007 (0.0006) −0.2479*** (0.0718) −0.3398*** (0.0754) −0.5105*** (0.0997) 0.0015 (0.0031) 0.0039 (0.0036) −0.0070* (0.0041) 0.0002 (0.0044)

−0.4476*** (0.0548) −0.2149*** (0.0395) 0.3430*** (0.0419) −0.0163*** (0.0025) −0.0013*** (0.0002) 0.0007 (0.0006) −0.1485* (0.0807) −0.2670*** (0.0867) −0.2294** (0.1142) 0.0018 (0.0031) 0.0021 (0.0035) −0.0083** (0.0041) −0.0016 (0.0042) −0.0027** (0.0011) −0.0009 (0.0012) −0.0002 (0.0012) −0.0030*** (0.0014) 1.9503*** (0.2952) −0.0249 (0.0445) 0.1213** (0.0493) 0.2280*** (0.0663) 3277 38.67

−0.1609*** (0.0535) −0.0721*** (0.0243) 0.0717 (0.0489) −0.0031 (0.0024) −0.0016*** (0.0002) 0.0013** (0.0005) −0.0542 (0.0618) −0.0649 (0.0703) 0.0563 (0.0924) 0.0024 (0.0025) 0.0006 (0.0031) −0.0074* (0.0039) −0.0055 (0.0038) −0.0028*** (0.0010) 0.0005 (0.0011) −0.0002 (0.0012) −0.0018 (0.0013) 0.5873*** (0.2454) −0.0287 (0.0368) 0.0913** (0.0432) 0.0382 (0.0489) 2986 35.16

−0.0673** (0.0292) −0.1056*** (0.0164) 0.0683*** (0.0227) −0.0067*** (0.0012) 0.0005*** (0.0002) 0.0008** (0.0003) −0.0731* (0.0394) −0.2387*** (0.0411) −0.3522*** (0.0744) 0.0002 (0.0012) 0.0006 (0.0015) 0.0007 (0.0019) 0.0034 (0.0028) −0.0010** (0.0005) −0.0002 (0.0005) 0.0010* (0.0006) 0.0001 (0.0009) 0.6425*** (0.1706) 0.0282 (0.0261) 0.0098 (0.0241) 0.0754*** (0.0306) 2986 36.29

−0.0430* (0.0243) −0.0219 (0.0135) 0.0501** (0.0239) −0.0074*** (0.0012) 0.0005**** (0.0002) 0.0001 (0.0004) −0.0131 (0.0268) −0.0600** (0.0282) −0.0835* (0.0504) 0.0015 (0.0013) 0.0003 (0.0018) −0.0051*** (0.0019) −0.0001 (0.0025) −0.0019** (0.0008) 0.0013 (0.0008) 0.0013 (0.0008) 0.0002 (0.0009) 0.1244 (0.1616) 0.1180** (0.0460) 0.1669*** (0.0445) 0.1664** (0.0836) 2308 25.09

−0.0833*** (0.0315) −0.0793*** (0.0157) 0.0794*** (0.0236) −0.0071*** (0.0013) 0.0006** (0.0003) 0.0007** (0.0004) −0.0787* (0.0425) −0.1866*** (0.0498) −0.2890*** (0.0819) 0.0017 (0.0017) 0.0013 (0.0020) −0.0026 (0.0026) 0.0009 (0.0034) −0.0019** (0.0007) 0.0006 (0.0008) 0.0008 (0.0009) −0.0006 (0.0010) 0.5384*** (0.1740) 0.1302** (0.0569) 0.1911*** (0.0660) 0.2529*** (0.0957) 2095 37.58

α̂t α̂t Fair α̂t Good α̂t Excell. St St Fair St Good St Excell. Tt Tt Fair Tt Good Tt Excell. Obs. Adj. R2

Mgmt.

3307 33.82

3277 38.33

2.1723*** (0.3071) 0.0087 (0.0390) 0.1393*** (0.0458) 0.2725*** (0.0582) 3307 34.35

The table reports estimated coefficients for the pooled OLS regression of funds' fees on selected fund characteristics and investor sentiment from 1993 to 2010, except for columns (7) and (8), where the sample period is 2003–2005. The coefficients are estimated using Eq. (4). All grades are according to Morningstar Stewardship Grade, which comprises of board quality, regulatory issues, manager incentives, fees and corporate culture. In columns (1), (2), (3), and (4) the dependent variable is fund total annual ownership cost (TOC). In column (5) the dependent variable is marketing fees (Mark.). In columns (6) and (8), the dependent variable is nonmarketing fees (N-Mark.). In column (7), the dependent variable is management fees (Mgmt.). All regressions include year dummies and dummy variables for the different investment objectives. Robust standard errors clustered by fund are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

that the anomaly is driven by small funds with high returns and low fees. We also perform regressions by different fee structures, subperiods, and investment objectives. The results show that the anomaly survives most of these robustness tests with the exception of subsamples formed according to the Lipper Objective and Classification code. Other robustness tests include clustering by month for seasonal effects, clustering by fund, including dummy variables in pooled OLS regressions for different years and investment objectives, and using different measures for fund fees such as management fees, marketing fees, and non-marketing fees. 6. Conclusion In conclusion, this paper investigates whether the negative relation between fund fees and before-fee risk-adjusted performance of mutual funds can be explained by investor sentiment. Following GR (2009) we attempt to recreate the fee–performance

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anomaly from 1962 to 2011. Although the results in Table 2 indicate that the anomaly is highly significant, there is no strong evidence for the GR fee–performance anomaly in the 2007 to 2011 subperiod and when fixed effects panel regression is performed. The main empirical finding is that investor sentiment, proxied by the BW (2006) composite sentiment index is a more important determinant of fund fees than the performance sensitivity measure proposed by Christoffersen and Musto (2002). The explanatory power of investor sentiment also appears to be stronger. In periods of low sentiment, the BW composite sentiment index generates significant coefficients. When yearly regressions are performed on selected fund characteristics and investor sentiment in the 1967 to 2010 period, neither performance sensitivity nor the composite sentiment index can eliminate the negative relation between fees and before-fee risk-adjusted performance. Lastly, using Morningstar stewardship grade to proxy for fund governance, we show that worst-governed funds charge higher fees but do not necessarily perform worse than better-governed funds. This is contrary to expectations that bettergoverned funds charge higher fees and perform better than worst-governed funds, and vice versa. Since the implementation of better fund governance likely requires additional costs, it should also translate to higher fees. A well governed fund would impose less risk for the investor as compared to one that is poorly governed. Additionally, the results show no evidence that stronger fund governance reduces the effects of investor sentiment. Perhaps investors are more concerned and influenced by other factors such as past performance and managerial ability rather than how well the fund is being governed. Acknowledgments We are indebted to an anonymous referee for the valuable comments. Appendix A. Estimating before-fee risk adjusted performance As in GR (2009), estimation of before-fee risk-adjusted performance of mutual funds is derived using Carhart's (1997) twostep estimation procedure. In the first step, we obtain each fund's exposure to the Fama–French–Carhart factors (betas) for every fund month from 1967 to 2011. This is achieved by regressing funds' before-fee excess returns on the Fama–French–Carhart (FFC henceforth) factors over the previous five years. Funds must have at least four years of previous data to be considered in the rolling regression. The FFC four-factor model is as follows:

r it ¼ α i þ βrm;i rmt þ βsmb;i smbt þ βhml;i hmlt þ βumd;i umdt þ εit

ðA:1Þ

where rit is fund i's before-expense return in month t in excess of the 30-day risk-free interest rate — proxied by Ibbotson's 1-month Treasury bill rate; rmt is the market portfolio return in excess of the risk-free rate; and smbt and hmlt denote the return on portfolios that proxy for common risk factors associated with size and book-to-market, respectively. The term umdt is the return difference between stocks with high and low returns in the previous year. This term is included to take into account passive momentum strategies by mutual funds and is based on Jegadeesh (1993). With the vector of betas obtained by performing rolling regressions in step 1, the realised risk premium can be estimated by multiplying the vector of betas with the vector of factor realisations in every monthly observation. In the second step, monthly fund performance is estimated by taking the difference between before-expense returns and the realised risk premium. The step 2 estimation model is as follows: α it ¼ r it −βrm;it rmt þ βsmb;it smbt þ βhml;it hmlt þ βumd;it umdt :

ðA:2Þ

Carhart's (1997) two-step estimation procedure generates a total of 351,375 monthly before-fee risk-adjusted returns for the period between 1967 and 2011 (540 months and 4788 different funds). Also to proxy for performance sensitivity, we follow the following method proposed by Christoffersen and Musto (2002):

Sit ¼

TNAit MAX it

ðA:3Þ

where TNAit is fund i's TNA value at the beginning of period t and MAXit is the maximum TNA value of fund i in the time-span up to period t.

Table 12. Correlation matrix of sentiment variables, performance sensitivity, and other fund characteristics.

Sizet−1 Aget−1 Co .Sizet−1 #fundst−1 Turnovert−1 σt−1 α̂t St Tt DIVt NIPOt RIPOt CEFt ISSUEt NYSEt

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

1 −0.0218*** 0.3831*** 0.1041*** −0.1492*** −0.1738*** 0.0203*** 0.0508*** 0.1464*** 0.0015 0.0467*** 0.1023*** −0.0835*** −0.1434*** 0.1635***

1 −0.5779*** −0.3894*** −0.1092*** −0.1898*** 0.3047*** −0.173*** 0.0516*** −0.0427*** 0.4312*** 0.1754*** 0.1268*** 0.2649*** −0.7689***

1 0.8441*** 0.0169** 0.1569*** −0.2597*** 0.0603*** −0.0151** 0.0751*** −0.4631*** −0.1911*** −0.2186*** −0.2959*** 0.7240***

1 0.0389*** 0.1388*** −0.1965*** 0.0008 −0.0541*** 0.0717*** −0.3833*** −0.1863*** −0.1433*** −0.1939*** 0.5474***

1 0.0816*** −0.0693*** −0.0220*** 0.0221*** −0.0068 −0.0480*** −0.0457*** −0.0607*** −0.0472*** 0.1045***

1 −0.1473*** −0.0298*** −0.0500*** −0.0029 −0.1707*** −0.2186*** 0.0394*** 0.2326*** 0.1175***

1 −0.0633*** 0.2087*** −0.1513*** 0.2345*** 0.1899*** 0.0554*** 0.1472*** −0.2383***

1 0.0835*** −0.1522*** 0.0430*** 0.0770*** −0.1561*** −0.2086*** 0.1654***

1 −0.6422*** 0.4449*** 0.4878*** −0.4081*** 0.1414*** 0.0366***

1 −0.4890*** −0.5223*** 0.4844*** 0.1530*** 0.0759***

1 0.3033*** −0.1812*** 0.1389*** −0.4511***

1 0.0087 0.0498*** −0.1877***

1 0.5240*** −0.1335***

1 −0.2639***

1

This table reports the correlation matrix of fund characteristics and investor sentiment variables. Sizet−1 is computed as the log of the year-end total net asset value. Aget−1 denotes the log of the number of years since fund inception. Co.Sizet−1 denotes the size of the management company and #fundst−1 denotes the number of funds under the management company. Turnovert−1 denotes the fund's reported annual turnover and σt−1 denotes volatility, defined as the standard deviation of the fund's monthly returns in the year. Expected performance, α t is the estimated alpha calculated using Carhart's (1997) two stage estimation process. St, is a proxy for performance sensitivity in year t. Investor sentiment is proxied by the BW (2006) composite sentiment index (denoted by Tt). The remaining variables are the six different proxies for investor sentiment. The variables are: dividend premium (DIV); number of initial public offerings (NIPO); average first-day returns on initial public offerings (RIPO); closed-end fund discount (CEF); equity share in new issues (ISSUE); and New York Stock Exchange share turnover (NYSE). *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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Appendix B

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