The effect of investor service costs on mutual fund performance

We examine the effect of investor service costs on mutual fund performance. Benchmarking passive funds against ETFs managed by the same fund company with the same investment objectives, we show that passive funds on average underperform ETFs by 42 bps in annualized net returns, of which about 90% can be attributed to investor service costs. Further benchmarking active funds against passive funds, we show that active funds on average outperform passive funds by 31 bps in annualized gross returns. However, the higher expense ratios charged by active funds lead to about 28 bps of underperformance in annualized net fund returns.     

Does Stock Return Momentum Explain the “Smart Money” Effect?

Does the “smart money” effect documented by Gruber (1996) and Zheng (1999) reflect fund selection ability of mutual fund investors? We examine the finding that investors are able to predict mutual fund performance and invest accordingly. We show that the smart money effect is explained by the stock return momentum phenomenon documented by Jegadeesh and Titman (1993) . Further evidence suggests investors do not select funds based on a momentum investing style, but rather simply chase funds that were recent winners. Our finding that a common factor in stock returns explains the smart money effect offers no affirmation of investor fund selection ability.     

Unskilled fund managers: Replicating active fund performance with few ETFs

This paper uses Exchange Traded Funds (ETFs) instead of risk factors as benchmarks to examine active mutual fund performance distribution. While transaction costs are included in the ETF returns, that is not true regarding risk factors, making it more challenging to characterize extraordinary performances via alphas. Assessments are based on the estimation of the skilled funds proportion defined by Barras et al. (2010). After evaluating several ETF combinations, we conclude that sets of 3 to 5 ETFs replicate most levels of active fund performance. Finally, we propose specific ETF selection algorithms, whereby we estimate that 95% of active management funds fail to generate value for their investors. Alphas calculated with ETFs are higher than those using risk factors, and the difference is similar to the transaction costs required for investing in risk factor portfolios (Frazzini et al. (2012)).     

Selling winners, holding losers: Effect on fund flows and survival of disposition-prone mutual funds

Recent studies suggest that presence of a disposition effect in a large subset of investors can create stock mispricings, which has serious implications for market efficiency. We examine whether US equity mutual funds are disposition-prone, how that effect influences performance, investor flows and fund survival, and whether the disposition orientation of mutual funds affects stock prices in a sustained manner.We find that about 30% of all funds exhibit some degree of disposition behavior and that such funds underperform funds that are not disposition-prone by 4-6% per year. Moreover, after controlling for performance, tax overhang and other factors that potentially affect flows, disposition-prone funds attract significantly smaller flows than other funds. The results suggest that mutual fund investors are smart enough to minimize investment in disposition-prone funds. Consequently, disposition-prone funds have significantly higher rates of failure than other funds, thereby reducing the impact of such trading behavior on security prices.     

ETF Premiums and Liquidity Segmentation

Exchange traded funds (ETFs) provide a means for investors to access assets indirectly that may be accessible at a high cost otherwise. I show that liquidity segmentation can explain the tendency for ETFs to trade at a premium to net asset value (NAV) as well as the life‐cycle pattern in premiums. ETFs with larger NAV tracking error standard deviations (TESDs) tend to trade at higher premiums and the liquidity benefits offered by foreign ETFs and fixed income ETFs are revealed to be the most valuable to investors. Further tests validate that TESD has the desirable properties of a liquidity segmentation measure.     

A Comparative Analysis of the Investment Characteristics of Alternative Gold Assets

Employing daily data over the period 1987–2010, we examine the diversifying, hedging and safe haven properties of gold bullion, gold stocks, gold mutual funds, and gold exchange traded funds (ETFs). First, with regard to gold bullion, we document a clear and strong hedging role over a mere diversifying capability. Second, our results highlight that gold stocks, gold mutual funds, and gold ETFs tend to be diversifiers. Third, both gold bullion and gold ETFs show support for the safe haven property. However, gold stocks and gold mutual funds display very little evidence of the safe haven characteristic. Consequently, investors who are keen on securing the safe haven features of gold investment cannot generally rely on gold stocks or mutual funds. Instead, they need to take positions directly in bullion or gold ETFs.     

What Drives ETF Flows?

The value of exchange traded fund (ETF) assets has increased from $66 billion in 2000 to almost a trillion dollars in 2010. We use this massive expansion in ETF assets to study what drives ETF flows. Using a data set of over 500 ETFs from 2001 to 2010, we show that ETF investors chase returns in the same way as mutual fund investors. While there is an active debate about whether return chasing by mutual fund investors represents the pursuit of superior talent, the existence of return chasing in this passively managed environment should not represent a search for skilled managers. We also show that ETF flows increase following high volume, small spreads, and high price/net asset value ratios. Finally, we find little evidence of superior market timing in ETF flows. Our results suggest that return chasing in both mutual funds and ETFs is more likely the result of naïve extrapolation bias on the part of investors that has contributed to the growth of the ETF industry.     

Credit ETFs in Mutual Funds and Corporate Bond Liquidity

We show that high yield (HY) mutual funds own and trade ETFs to manage liquidity needs driven by fund flows, whereas investment grade (IG) funds do not. The use of ETFs by HY mutual funds to manage liquidity shifts some trading away from bonds and into ETFs, which reduces the liquidity of the underlying bonds. This substitution effect outweighs the better-understood inclusion effect, whereby bond liquidity benefits from increased ETF ownership, such that the net effect of ETFs is to reduce HY liquidity. In IG, the substitution effect is not significant and ETFs result in increased bond liquidity.     

Is “voting with your feet” an effective mutual fund governance mechanism?

Investors in open-end mutual funds can vote with their feet by withdrawing assets from or adding assets to these funds. This paper assesses the effectiveness of this market discipline mechanism by investigating whether voting with the feet prevents the abusive practices that led to the 2003-2004 trading scandals. The research results indicate that funds with higher flow sensitivity—that is, a higher density of vigilant clients—have lower arbitrage potential and fewer abnormal flows, which in turn implies less opportunistic trading. As a result, these funds have a lower probability of being implicated in scandals. These findings suggest that investor ability to withdraw assets from or add assets to the funds is an effective mutual fund governance mechanism. In funds with less sophisticated investors who cannot use this option, other means of governance are especially important.     

Can exchange traded funds be used to exploit industry and country momentum?

There is overwhelming empirical evidence on the existence of country and industry momentum effects. This line of research suggests that investors who buy country and industry portfolios with relatively high past returns and sell countries and industries with relatively low past returns will earn positive risk-adjusted returns. These studies focus on country and industry indexes that cannot be traded directly by investors. This raises the question of whether country and industry momentum effects really can be exploited by investors or whether they are illusionary in nature because they exist only on non-tradable assets. We analyze the profitability of country and industry momentum strategies using actual price data on exchange traded funds (ETFs). We find that over the sample periods during which these ETFs were traded, an investor would have been able to exploit country and industry momentum strategies with an excess return of about 5 % per annum. These returns cannot be explained by unconditional exposures to the Fama–French factors. The daily average bid-ask spreads on ETFs are substantially below the implied break-even transaction cost levels. Hence, we conclude that investors who are not willing or able to trade individual stocks may use ETFs to benefit from momentum effects in country and industry portfolios.     

Investor monitoring and differences in mutual fund performance

A number of mutual funds cater exclusively to institutional investors. Although institutional funds might be a natural place to look for “smart money”, agency costs associated with delegated monitoring may lead to less monitoring and worse overall performance. We split institutional funds based on proxies for the degree of investor oversight, and we find that institutional funds with low initial investment requirements and funds with retail mates perform significantly worse than other institutional funds both before and after adjusting for risk and expenses. Tracking error is especially important in the flow-performance relationship of institutional funds with high minimum investment requirements.     

The benefits of option use by mutual funds

Based on comprehensive regulatory data on equity mutual fund option use from the SEC's N-SAR filings, we are the first to present consistent evidence that equity funds' option use generates higher risk-adjusted performance. We further show that this is a direct effect of option use and not an indirect effect of other fund characteristics. Option use also directly results in lower systematic risk, as funds show significantly lower market betas during periods of options usage. Finally, mutual funds use options mainly for hedging as they primarily use protective puts and covered calls. These results are independent of known phenomena, such as the low beta anomaly, and robust to tests for endogeneity and a novel 5-factor model including an investable option strategy factor (IOS). Overall, our findings show that mutual fund option use is beneficial to investors and does not pose risk to the financial system as feared by the SEC. Our results are thus important for investors as well as regulators.     

Sentimental mutual fund flows

We show that many stylized empirical patterns for mutual fund flows are driven by investor sentiment. Specifically, when sentiment is high, investors exhibit a stronger tendency of chasing past fund performance; fund flows are less sensitive to fund expenses; and investors are attracted more to funds with sheer visibility. Moreover, the well‐documented positive relation between fund flows and future fund performance is significant only during high sentiment periods and is mainly driven by expected component of fund flows. Finally, we show that mutual fund investors exhibit a significantly negative timing ability at the individual fund level when sentiment is high.     

Rating mutual funds: Construction and information content of an investor-cost based rating of Danish mutual funds

We develop a new rating of mutual funds: the atpRating. The atpRating assigns crowns to each individual mutual fund based upon the costs an investor pays when investing in the fund in relation to what it would cost to invest in the fund's peers. Within each investment category, the rating assigns five crowns to funds with the lowest costs and one crown to funds with the highest costs.We investigate the ability of the atpRating to predict the future performance of a fund. We find that an investor who has invested in the funds with the lowest costs within an investment category would have obtained a risk-adjusted excess return that is approximately 3–4 percentage points higher per annum than if the funds with the highest costs had been invested in.We compare the atpRating with the Morningstar Rating. We show that one reason why the atpRating and the Morningstar Rating contain different information is that the returns Morningstar uses as inputs when rating funds are highly volatile whereas the costs the atpRating uses as inputs when rating funds are highly persistent. In other words, a fund that has low costs one year will most likely also have low costs the following year, whereas the return of a fund in a certain year generally contains only little information about the future return that the fund will generate.Finally, we have information on the investments in different mutual funds made by a small subgroup of investors known to have been exposed to both the atpRating and the Morningstar Rating. We find that investors have clear preferences for funds rated high by both the atpRating and the Morningstar Rating.     

Management of flow risk in mutual funds

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

Cautious Risk Takers: Investor Preferences and Demand for Active Management

Despite their mediocre mean performance, actively managed mutual funds are distinct from passive funds in their return distributions. Active value funds better hedge downside risk, while active growth funds better capture upside potential. Since such performance features may appeal to investors with tail‐overweighting preferences, we show that preferences for downside protection and upside potential estimated from the empirical pricing kernel can help explain active fund flows in the value and growth categories, respectively. This effect of investor risk preferences varies significantly with funds' downside‐hedging and upside‐capturing ability, with levels of active management, and across retirement and retail funds.     

Mutual fund flows and investor returns: An empirical examination of fund investor timing ability

We examine the timing ability of mutual fund investors using cash flow data at the individual fund level. Over 1991–2004 equity fund investor timing decisions reduce fund investor average returns by 1.56% annually. Underperformance due to poor timing is greater in load funds and funds with relatively large risk-adjusted returns. In particular, the magnitude of investor underperformance due to poor timing largely offsets the risk-adjusted alpha gains offered by good-performing funds. Investors in both actively managed funds and index funds exhibit poor investment timing. We demonstrate that our empirical results are consistent with investor return-chasing behavior.     

REIT Institutional Ownership Dynamics and the Financial Crisis

Collectively, institutional investors hold large ownership stakes in REITs. The traditional view is that institutions are both long-term and passive investors. The financial crisis beginning in 2007 provides an opportunity to analyze the investment choices of institutional investors before, during, and after the crisis. Our results indicate that institutional ownership increased prior to the financial crisis, declined significantly during the period of market stress, but rebounded after. These results hold for four institutional investor subtypes: mutual funds/investment advisors, bank trusts, insurance companies, and other institutions, with mutual funds/investment advisors and bank trusts most clearly exhibiting this pattern. We also find evidence that institutions actively manage their REIT portfolios, displaying a “flight to quality” after the market downturn by reducing beta and individual risk exposure, and by increasing ownership in larger REITs.     

全球基础设施投资基金研究：投资者偏好和委托代理问题

基于2005年1月至2019年6月间公开发售的2246支来自全球的基建基金数据,本文研究基建基金投资者偏好和委托代理问题。研究结果表明:投资者偏好客户忠诚度较高、投资于国际市场、团队管理型的基建基金;在基建基金公司层面,投资者存在跟风投资行为,且随着信息不透明度和收益不确定性加剧;投资者与基金管理人存在委托代理问题,体现为基金费率与预期业绩不匹配。该现象源于基金管理人利用投资者较高的预期收益设置费率,可以由基金特征因素和年份效应解释,尤其是投资于国际市场、收益分配或非团队管理等特征。本文结论对创新基础设施融资模式、稳定资金来源和微观审慎监管具有重要借鉴意义。     

How smart is money? An investigation into investor behaviour in the Australian managed fund industry

Gruber [Gruber, M., 1996. Another puzzle: the growth in actively managed mutual funds. Journal of Finance 51, 783–810] and Zheng [Zheng, L., 1999. Is money smart? A study of mutual fund investors’ fund selection ability. Journal of Finance 54, 901–933] document that managed fund investors demonstrate fund selection ability as they invest in funds whose subsequent performance is greater than that of funds from which they divest. This phenomenon has been since been termed the ‘smart money effect’. In contrast, Sapp and Tiwari [Sapp, T., Tiwari, A., 2004. Does stock return momentum explain the ‘smart money’ effect? Journal of Finance 59, 2605–2622] find that after controlling for stock return momentum, there is no evidence of a smart money effect. In this paper, we investigate whether a smart money effect exists in the Australian managed funds industry. The key findings of our paper are that there is a smart money effect in Australia and that stock return momentum does not explain this effect. We also find that the effect is not conditional on fund size. Our cross-sectional analysis indicates that investors are chasing funds that have performed well in the past and that cash flows to funds are persistent. However, we do not find any evidence that investors are pursuing funds that employ momentum trading strategies.