Evaluation of Mutual Fund Flow-Performance Relationship
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1. Mutual fund flow-performance relationship
There has been extensive analysis regarding the flow-performance relationships across a broad range of funds since the 1990s. Ipolito (1992) examine the relationship between investor fund allocation decisions and past performance of mutual funds. Using a large span of data from 1965 to 1984, the author discovers that investors react to new information regarding product quality (prior performance) and respond accordingly. Rational investors exploit this information by allocating investable monies away from lowest ranked funds toward recent good performers considering of transaction costs. Chevalier and Ellison (1997)’ paper finds a similar result and examines funds that make the annual Morningstar best fund list attract more attention by relatively uninformed investors and therefore obtaining higher flows. His research uniquely divides the sample of young (2-5 years) and old (6 years and above) retail funds across period of 1988 to 1994 and uncover a linear fund flow/performance relationship for younger funds and a convex relationship for older funds. The tendency for younger funds to undertake riskier investment to improve performance and to avoid significant outflow contributes to this linear fund flow/ performance relationship.
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However, plenty of literatures find a non-linear fund flow/ performance relationship—mutual fund investors chase past performance by rewarding “winners” but failing to punish “losers”. The asymmetric flow–performance relationship for mutual funds has attracted much attention and researchers have investigated this issue from different aspects. For example, Fant and O'Neal, (2014) document an increase in the flow‐performance asymmetry in the second period (1988-1997) that exacerbates the adverse incentive for fund managers to increase portfolio risk. They conclude that, though top performing funds are rewarded with greater fund flows in the sample of 1988 to 1997, the change is due solely to the increase in aggregate fund flows and not to an increased reliance on performance by individual fund investors. While Fant and O’Neal, (2014) have revealed the asymmetric flow-performance relationship, other researchers investigate the rationale behind this phenomenon and the major explanations can be summarized as followed.
1.1 Asymmetric flow/performance relationship
Qian, Meifen, Xu, Chongbo, & Yu, Bin. (2014). Performance Manipulation and Fund Flow: Evidence from China. Emerging Markets Finance and Trade, 50(3), 221–239. https://doi.org/10.2753/REE1540-496X500312 Given that past performance is acknowledged as an important factor in the selection of preferred funds, many researchers also demonstrate an asymmetric relationship between flow and performance (Bollen and Pool 2008; Del Guercio and Tkac 2002; Sirri and Tufano 1998). Asymmetry pertains to the relationship in which poor performance goes unpunished to the same extent that strong performance is rewarded. Lynch and Musto (2003) argue that investors expect a strategy change because after a bad performance the fund manager would adopt a remedy. Thus, poor past performance does not convey important information about future return prospects and is, therefore, irrelevant to fund flows. Recent research also suggests that in addition to past performance, concurrent fund returns should be considered. Chevalier and Ellison (1997) test the contemporaneous relationship between flow and performance and find that year-to-date performance has a significantly positive effect on flow. Benson et al. (2010) and Deaves (2004), who respectively adopt monthly and annual data, further confirm this effect on current returns. Conversely, some studies find that money flow can predict subsequent fund returns, though the results obtained by these studies are mixed. For example, some researchers find that funds that attract more flows do poorly in subsequent periods (Benson et al. 2010; Lamont and Frazzini 2005). Zheng (1999) shows that investors tend to use their money wisely—that is, they shift their investment to top-performing funds. This interaction suggests an endogenous relationship: investors gather up-to-date information through modern information systems and then quickly change their investment decisions accordingly. Consequently, fund managers are required to handle the resultant money inflows and outflows in a consistent and timely manner, while sudden abnormal flows can be problematic and influence current performance. Other information, including age, fees, volatility of returns, participation costs, marketing, fund size, and group size, are also regarded as control variables in the fund literature. Various studies (e.g., Edelen 1999; Huang et al. 2007) examine the effects of these variables on fund flows.
Previous studies generally use regression analysis to characterize the flow of new money into mutual funds as a function of fund attributes. These studies concentrate on the relation between fund flows and prior‐period performance for equity mutual funds. Zeckhauser, Patel, and Hendricks (1991) look at a sample of no‐load funds from 1975 to 1987. They find that current‐period fund flows are a linear function of prior‐period fund flows, prior‐period raw and ranked returns, and fund size. Using a sample of funds from 1965 to 1984, Ippolito (1992) documents an asymmetry in the flow‐performance relation. Specifically, funds outperforming the market experience a more positive flow response than those that underperform. In perhaps the most widely cited study of the relation between performance and fund flows, Sirri and Tufano (1998) examine funds from 1971 to 1990. They find no relation between rank performance and fund flows for the quintile of worst performers, but they find a positive relation for the upper four quintiles, particularly the top quintile. Goetzmann and Peles (1997) document similar asymmetric results using a data set drawn from 1976 to 1988. Chevalier and Ellison (1997) use kernel regression to document an asymmetric flow‐performance relation that is primarily present for smaller funds only.
Institutional explanations for the asymmetric relation include such market frictions as capital gains taxes, prohibitive search costs, retirement plan investment restrictions, and recurring load charges. Capital gains are recognized when investors sell fund shares, potentially preventing investors from selling if performance is positive but subpar. Loads increase the transaction costs of pursuing an active fund trading strategy. Retirement plans generally restrict investors to one or several fund families, limiting the choices an investor might consider to replace a poorly performing fund.
The asymmetric flow‐performance relation is also consistent with several psychological models of investor behavior, which attribute irrationality to individual investors. Zeckhauser, Patel, and Hendricks (1991) point to status‐quo bias—the tendency to stick with strategies already adopted. Kahneman and Tversky (1982) cite regret aversion, which causes investors to regard errors of omission less seriously than errors of commission. Goetzmann and Peles (1997) discuss cognitive dissonance, where investors adjust their beliefs to justify previous decisions. Of course, both institutional and behavioral explanations may contribute to the observed asymmetric relation between performance and fund flows.
Brown, Harlow, and Starks (1996) present findings regarding the propensity of mutual fund managers to alter portfolio risk. They find that managers who are losers in the first half of the year tend to increase the volatility of their portfolios in the second half of the year. Chevalier and Ellison (1997) find that only managers of smaller funds increase nonsystematic risk in response to fund‐flow incentives. No evidence is found that managers increase systematic risk to attract fund flows. In a study of the characteristics of stocks held in mutual fund portfolios, Falkenstein (1996) finds that mutual fund managers are averse to stocks with low idiosyncratic volatility, consistent with the idea that managers purposely load on unsystematic risk to increase returns and fund flows.
1.1.1 Investor psychological factor
Goetzmann and Peles, (1997) examine this flow/performance relationship by studying the psychological factor of investors using the current psychological model and shows the investor inertia contributes to this asymmetric and convex flow/performance. As a result, the past top performing funds attract disproportionately large inflows in subsequent periods, whereas past poor performers suffer minimal outflows. Similarly, building on the Kahneman and Tversky’s aversion to loss realization theory, Shefrin and Statman's (1985) place this behaviour pattern into a wider theoretical framework concerning a general disposition to sell winners too early and ride losers too long, which explains the reason for a less manifestation outflow for underperformed fund. Other findings related to investor behavioural bias include Zeckhauser, Patel, and Hendricks (1991), who point to status‐quo bias—the tendency to stick with strategies already adopted and Kahneman and Tversky (1982), who cite regret aversion, which causes investors to regard errors of omission less seriously than errors of commission. Therefore, both institutional and behavioural explanations contribute to the observed asymmetric relation between performance and fund flows.
1.1.2 Transaction fees and search costs
Sirri and Tufano (1998), which is perhaps the most widely cited study of the relation between performance and fund flows. They examine funds from 1971 to 1990 and find no relation between rank performance and fund flows for the quintile of worst performers, but find a positive relation for the upper four quintiles, particularly the top quintile. They suggest that mutual fund investors purchase funds that are easier or less costly for them to identify, such as those with extensive marketing efforts, those receiving more media coverage, and those offered by well-known fund families. In addition, fund flows are fee-sensitive: consumers respond differently to high and low fees as well as to fee increase and decrease, which deters investors to transfer their monies away from underperformed fund. Similarly, Huang et al. (2007) document the transaction costs from purchasing and redeeming fund shares that can hinder investors from removing their monies away from poor performed fund. Moreover, fund characteristics such as age, volatility of past performance, affiliation with a large or "star"-producing fund complex, and marketing expenditures affect both the level of fund flows and the sensitivity of flows to past performance. He also models the effect of investor participation costs on the mutual fund flow-performance relationship: the costs of collecting and analysing information about funds. The authors suggest that participation costs can lead to different flow responses at difference performance levels and, consequently, to an asymmetric flow–performance relationship. Berk and Green (2004) assume a perfectly competitive capital market in which the return to an actively managed fund decreases with its portfolio size. Using variable cost functions for managers, they show that a convex relationship between new investments and past performance exists even in the absence of performance persistence. Lynch and Musto (2003) argue that investment companies can exercise an option to abandon poorly performing strategies and/or fire bad managers. Since poor returns are not likely to be informative about future performance, investors will respond less strongly to bad performance, leading to the convexity in the flow-performance relationship
1.1.3 The investor clientele effect
Guercio and Tkac (2002) compare the relationship between asset flow and performance in the retail mutual fund and fiduciary pension fund segment of the money management industry and relates empirical differences to fundamental differences in the clientele they serve. While pension fund clients punish funds with poor performance by withdrawing assets under management and do not flock disproportionally to recent winners, mutual fund clients do not withdraw assets from funds with poor performance but chase and flock to past winners, displaying an asymmetric performance-flow relationship. Christoffersen and Musto (2002) argue that investors have different demand curves and that the investors of bottom funds are relatively less sensitive to performance and price. Sawicki (2001) investigates the flow–performance relationship using Australian wholesale funds, which are traded primarily by large, institutional investors. She finds that institutional investors in Australia react to recent performance, but the response is not asymmetric.
1.1.4 Star funds and the flow–performance relationship
Gruber (1996) finds evidence that “sophisticated” investors are able to recognize superior management, witnessed by the fact that the flow of new money into and out of mutual funds follows the predictors of future performance. Fund families recognize the importance and the benefits of having popular, well-performing funds include not only the superior performance of their managers but also their funds in general to increase investor inflows and thus increase total net assets managed and management fees. Nanda et al. (2004) also finds a positive spill over effect on the inflows of other family funds resulting from having a star performing fund without the negative effect from a poor performing fund. Using portfolio analysis, they find that factors that enhance the ex ante odds of producing stars are associated with a significantly poorer family performance, which is consistent with lower-ability families pursuing strategies to take advantages of the cash flow response to a star performance. They also find a naïve strategy of chasing families with star performers does not enhance investor return.
1.2 Fund flow volatility – performance relationship
Rakowaski (2010) provide a detailed analysis of the impact of daily mutual fund flow volatility on fund performance and document a significant negative relationship between the volatility of daily fund flows and cross-sectional differences in risk-adjusted performance. The negative relationship is strongest for domestic equity funds. This study collect data from several sources from March 2000 until October 2000 and use cross-sectional regression analysis to find that flow volatility remains significant after correcting for funds’ turnover, suggesting it is not simply the increase trading by fund managers that drives the link between flow volatility and performance.
Wang et al. (2018) study this relationship from another perspective. He examines the impact of fund volatility and fee charges on flow-performance sensitivity. They use quarterly data for January 1999 to December 2011 from CRSP Survivor-bias free mutual fund database and test flow-performance sensitivity under three phases: pre-GFC (January 1999-June 2007); GFC period (July 2007-March 2009); and post GFC period (April 2009-December 2011). They find that investors react negatively to fund volatility which means that low volatility funds experience the greatest flow-performance relationship, implying they generate greater net flows in relation to past performance. They also identify the impact of pure operating expense. High expenses are an additional burden born by investors and weaken flow-performance sensitivity, which implies that funds with higher pure operating expense generate less net flows. It is suggested that investors dislike fee charges and learn to avoid them and their reaction is stronger post-GFC. In addition, this paper concludes that advertising effects increase investors’ awareness and encourage greater net flows even in sluggish time.
2. Mutual fund managerial structure
Over the past two decades, team-based portfolio management has become very popular in the U.S. mutual fund industry. For example, in 2010, more than 70% of all U.S. domestic equity mutual funds were managed by “teams” of portfolio managers compared to only 30% in 1992 (Patel and Sarissian, 2017). Researchers are attempting to find the rationale behind this striking trend. This section review literatures from performance and risk dimensions of the pros and cons for team and sole managerial structure.
2.1 Managerial structure and fund performance
Previous literatures explain this trend predominantly from the fund performance viewpoint. A prevalent view is multiple manager fund outperforms sole managers. For example, Han et al. (2008) develop a model where fund performance is driven by organizational design (team or solo-managed) and managerial ability. In their model, team structure leads to better performance by improving information quality, suggesting a positive relation between fund performance and team management. They use empirical evidence to prove that team-managed funds outperform solo-managed funds by 23–38 basis points per year, but only after controlling for managerial self-selection. However, in their framework superior managers may self-select into solo-managed funds where they are not subject to the “dilution in the reward from superior performance” attendant in team structures. Consequently, it is possible that a solo-managed fund may yield better performance than an otherwise equivalent team-managed fund. Similarly, Patel and Sarkissian (2017) argue large discrepancies of the data used by previous studies and challenge the previous conclusions of no performance benefits from team management. Utilising more accurate and detailed managerial-level Morningstar Direct data from 1992 to 2010 in U.S., they find that team-managed funds outperform single-managed funds across various performance metrics and fund benefit the most from teams of 3 portfolio managers.
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Contrary with Han et al. (2008) and Patel and Sarkissian (2017) s’ finding, other empirical studies find little evidence of performance benefits of teamwork in the fund industry. For instance, Prather and Middleton (2002) and Prather and Middleton (2006) examine performance differences between solo and team-managed funds in the context of classical versus behavioural decision-making theory. The former is predicated on the assumption that all decision-makers are rational, and that management structure does not impact performance outcomes. On the other hand, behavioural decision-making theory suggests that team management leads to more integrated information gathering and decision processes with fewer biases, more consistency, more comprehensive use of information and rationality, all of which should lead to better performance outcomes. Prather and Middleton (2002) and Prather and Middleton (2006) empirical results support the classical decision-making framework as they find an insignificant difference in the performance outcomes between team-managed and solo-managed mutual funds. Similarly, Bliss et al. (2008) provide an empirical examination of whether funds managed by individuals perform differently from funds managed by teams. Using a sample of about 3,000 equity mutual funds over a 12-year horizon, the authors find that although the number of funds managed by teams has grown at seven times the rate of funds managed by individuals, no significant difference in risk adjusted performance is observed between team-managed and individually managed funds. More specifically, Chen et al. (2004) document that team-managed funds underperform their solo-manager counterparts by an average of 48 basis points per year. Therefore, these literatures show ambiguous results on the literature reviews of relationship between managerial structure and performances.
2.2 Investment risk and fund flow volatility for different managerial structure
On the other hand, the extant academic literature highlights the benefits of group decision making from risk perspectives. For instance, Sharpe (1981), Barry and Starks (1984), and Sah and Stiglitz (1991) argue that teams in the fund management industry achieve a diversification of style and judgment that reduces portfolio risk, thus inducing better performance. In particular, Barry and Starks (1984) addresses the investor's decision to employ multiple managers for the management of investment funds. Instead of the common believes that specialization of managers (If investment managers specialize and produce special insights into the likely success of certain industries or limited groups of securities, then it is reasonable to employ managers to invest funds in their areas of specialty) and diversification of managers (to protect against the possibility that a particular manager might make a serious error in the management of funds, one can diversify funds among managers, hence "washing out" the danger of overall fund performance being seriously damaged by the bad fortune of a single manager.) can be the potential explanation for a favourable team managed structure, besides, this paper shows that risk sharing considerations may be sufficient and demonstrate that one aspect of the principal-agent relationship, and can influence the decision to use multiple managers.
In addition, Qiu (2003) analyses the difference in the risk-taking behaviour of funds managed by multiple managers and single managers. Single managers have greater incentives to undertake higher portfolio risk investment in order to catch up with the top fund. Their results support the notion that multiple managers provide an effective way of reducing the risk-taking incentives of funds in response to their relative performance. In general, single-manager funds are more aggressive and tend to take higher risk exposure than team-managed funds. In a more comprehensive study, Bliss et al. (2008) exhibit the team-managed advantage compared with sole managed fund from risk, cost and flow perspectives. They find that team managed funds are significantly less risky and exhibit lower turnover, the total cost of owning a team managed mutual fund is, on average, nearly 50 bps lower per year than the cost of owning an individually managed mutual fund and team-managed funds attract significantly greater investor flows than individually managed funds, even after controlling for performance, risk, and expenses.
Bar, Kempf, Ruenzi (2011) analyse the behavioural differences between fund managed teams and single managers and provided empirical evidence by examining investment decisions of mutual fund managers to support the diversification of opinion theory and reject the group shift theory which suggests that the opinion of team members shifts towards the opinion of the dominant person in a team. They find that team managers follow less extreme investment styles, hold less risky portfolios and exhibit lower industry concentration within their portfolios than single managers, which eventually allows them to achieve less extreme performance outcomes and these result hold after taking into account the impact of fund and family characteristics as well as manager characteristics. Their conclusions of more stable and conservative behaviour among team-managed funds and the growth of team management in the mutual fund industry are consistent with increased demand for stability among institutional investors. Stein (2002) argues that in decision settings that involve soft information (e.g., based on research, relationships) a decentralized setting where a solo-manager is in charge is more effective than a more complex organization setting where the decision is reviewed by multiple managers and requires individual managers to persuade other managers. Chen, et al. (2004) apply this concept to mutual fund management structures and argue that organizational design costs are minimal for solo-manager led funds but not for team-managed funds. As investing becomes more complicated with so many new opportunities arising from new industries, markets and companies, team-managed funds make more sense (Kovaleski, 2000). This paper provides a potential explanation for team managerial trend: team-managed mutual funds are on the rise as mutual fund companies look for strength in numbers and try to avoid falling victim to "stars" who leave, which he explains that strong performance can lead to turnover among star managers.
3. Manager turnover
Literatures related to mutual fund manager turnover can be classified into two broad categories, the determinants of replacement of fund manager and consequences of manager turnover.
3.1 Determinants of fund manager turnover
Khorana (1996) studies the relation between managerial replacement and prior fund performance. He finds evidence supporting the presence of an inverse relation between the probability of fund manager replacement and past performance, using the growth rate in the fund’s assets and portfolio returns. However, this relationship is proved to be very weak. In Kostovetsky and Warner (2015)’s paper, they provide one potential explanation for this weak relationship: mutual fund manager turnover data are noisy – it is difficult to distinguish between forced and voluntary departures, biasing downward any estimate of the true turnover–performance relation. They find a significantly stronger connections between manager departures and prior underperformance than previous studies. In addition, they find that characteristic-adjusted returns (incorporating manager tenure) going back as far as 5 years are statistically significant determinants of manager turnover.
Chevalier and Ellison (1999) re-examine the performance replacement relation with special focus on the age of the fund manager. They find that younger managers are more likely to experience replacement if the fund’s systematic or unsystematic risk deviates from the average risk level of other funds in the matched investment objective. Ding and Wermers (2005) construct a comprehensive data set on portfolio managers and find evidence that experienced large‐fund managers with better track‐records outperform their size, book to market, and momentum benchmarks. They also report that higher numbers of independent directors predict a better future performance and higher likelihood of replacement of underperforming managers.
Fricke and Eric (2015) examines the relationship between board holdings, compensation and the turnover of underperforming mutual fund managers. Based on 2003 data collected from 606 mutual funds, their results provide evidence that underperforming fund managers have a lower probability of being replaced when their boards have lower holdings and higher compensation. Bryant and Lonnie (2012) first to link managerial turnover to mutual fund managerial structure in a manner that indicates the strong presence of a conflict of interests between investors and fund sponsors in an area of fund governance where we have been led to believe there are strong and well-functioning mechanisms to guard against the exploitation of investors. In addition, the conflict of interests affects the replacement decision, as high expense ratio fund managers have a lower probability of replacement for a given level of underperformance.
In addition, Barron et al. (2013) investigates the effect of Morningstar ratings on mutual fund manager replacement find that not only do Morningstar ratings affect the likelihood fund managers are replaced, but that Morningstar ratings are better predictors of manager replacement than alternative measures of fund performance. They also examine the changes in the management structure of funds that are made in conjunction with manager replacement in response to poor performance.
3.2 Manager turnover and post-replacement performance and fund flow
Prior works present a picture of the consequences of portfolio manager changes. In a firm level setting, Denis and Denis (1995) examine the impact of CEO turnover on the post-replacement performance of the firm. For the subsample of managers experiencing forced replacement, they document forced resignations of top managers are preceded by large and significant declines in operating performance but significant improvements in post-replacement operating performance. However, they find that forced turnovers occur after prolonged periods of poor performance, which leads to a substantial loss in shareholder wealth. On the other hand, normal retirements are followed by small increases in operating income and are also subjective to a slightly higher than normal incidence of post-turnover corporate control activity.
In a particular mutual fund industry, Khorana (2001) examine the impact of mutual fund manager replacement on subsequent fund performance and document significant improvements in post-replacement performance relative to the past performance of the fund. On the other hand, the replacement of overperforming managers results in deterioration in post-replacement performance. They also document that the level of portfolio turnover activity decreases significantly in the post-replacement period and the replacement of poor performers is preceded by significant decreases in net new inflows in the fund. The performance flow relation suggests that replacement of the poorly performing fund managers is preceded by significantly lower asset flows, hence limiting the ability of funds to earn higher investment advisory fees in the pre-replacement years. These findings also suggest that external product markets can play an important role in affecting the managerial replacement decision. Therefore, the replacement of poorly performing managers tends to be a value-enhancing activity for both the investment advisors and shareholders of the fund. In a similar vein, Kostovetsky and Warner (2015) find evidence that flow improves after management is replaced. This suggests that fund investors react positively to changes in management, and fund sponsors may cater to investors to attract inflow or minimize outflow. However, they do not find significant improvements in returns, which is contradict to Korana (2001)’s finding, documenting a significant improvement in post-replacement performance.
Deuskar et al. (2011) relate the turnover of money managers to the career opportunities in the money management industry to understand the turnover of mutual fund managers during a special time period when the land scape of the asset management industry is undergoing an extreme makeover due to the rapid growth of hedge fund. They find that mutual funds can retain managers with good performance in the face of competition from a growing hedge fund industry. On the other hand, poor performers are more likely to leave the mutual fund industry. A small fraction of these poor performers finds jobs with smaller and younger hedge fund companies, especially when the hedge fund industry is growing rapidly. Analogously, a small fraction of the better-performing mutual fund managers is retained by allowing them to manage a hedge fund side-by-side.
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