Why Do Closed-End Bond Funds Exist?
(with Edwin J. Elton, Martin J. Gruber, and Or Shachar; this is currently a working paper)
Abstract
In this paper we explore why closed-end bond funds exist when similar open-end funds are available. The current belief is that closed-end funds can invest in less liquid assets and hold less cash, thus earning a higher return. When we compare closed-end bond funds with a matched set of open-end funds with the same objective, same manager and issued by the same family, we find no difference in asset holdings. There is a difference in performance, and we show that mostly it is due to the use of leverage by closed-end funds. Closed-end funds use leverage, borrowing short and lending long, increasing return in most years. Leverage is issued in years with a high term premium. We show that leverage affects both the average size of the discount on closed-end funds and its time pattern. We close with a brief history of performance since the crisis in the credit markets.
Holdings Data, Security Returns and the Selection of Superior Mutual Funds
(with Edwin J. Elton and Martin J. Gruber; this paper appears in the April 2011 issue of The Journal of Financial and Quantitative Analysis)
Abstract
In this paper we show that selecting mutual funds using alpha computed from a fund’s holdings and security betas produces better future alphas than selecting funds using alpha computed from a time series regression on fund returns. This is true whether future alphas are computed using holdings and security betas or a time series regression on fund returns. Furthermore, we show that the more frequently the holdings data are available, the greater the benefit. This has major implications for the SEC’s recent ruling on the frequency of holdings disclosure and the information plan sponsors should collect from portfolio managers. We also explore the effect of conditioning betas on macro variables as suggested by Ferson and Schadt (1996) to identify superior-performing mutual funds as well as the alternative way of employing holdings data proposed by Grinblatt and Titman (1993).
The Effect of Holdings Data Frequency on Conclusions about Mutual Fund Management Behavior
(with Edwin J. Elton, Martin J. Gruber, Yoel Krasny and Sadi O. Ozelge; this paper appears in the May 2010 issue of The Journal of Banking and Finance)
Abstract
A number of articles in financial economics have used quarterly or semi-annual mutual fund holdings data to test hypotheses about investment manager behavior. This article reexamines four well-known hypotheses in finance to determine whether the results of prior tests of these hypotheses remain valid when higher frequency (monthly) holdings data are employed. The areas examined are: momentum trading, tax-motivated trading, window dressing, and tournament behavior. We find that the use of monthly holdings data rather than quarterly holdings data or, in the case of tournament behavior, holdings data rather than monthly return data, change, and in some cases reverse, previous results. This occurs because monthly holdings data capture a large number of trades missed by quarterly data (18.5% of the trades) and permit a more precise estimation of the timing of trades.
Participant Reaction and the Performance of Funds Offered by 401(k) Plans
(with Edwin J. Elton and Martin J. Gruber; this paper appears in the April 2007 issue of The Journal of Financial Intermediation)
Abstract
This is the first study to examine both how well plan administrators select funds for 401(k) plans and how participants react to plan administrator decisions. We find that, on average, administrators select funds that outperform randomly selected funds of the same type although they do not outperform index funds of the same type. When administrators change offerings, they choose funds that did well in the past, but, after the change, added funds do no better than dropped funds. Plan participants in aggregate change their allocation decisions in a way that accentuates the changes in allocation caused by returns. The change in allocation due to the investment of new money and interfund transfers is about the same size, and in the same direction, as the change due to returns. Participant allocations in aggregate do no better than naïve allocation rules, such as equal investment in each offering.
The Adequacy of Investment Choices Offered by 401(k) Plans
(with Edwin J. Elton and Martin J. Gruber; this paper appears in the August 2006 issue of The Journal of Public Economics)
Abstract
The choices made by 401(k) participants are the product of two different decisions: what is offered and what is chosen. While there have been a number of studies of the decisions made by participants in 401(k) plans, there have been no studies of the adequacy of the full set of choices offered to 401(k) participants. This paper analyzes the adequacy and characteristics of the choices offered to 401(k)-plan participants for over 400 plans.
We find that only 53% of the plans offer an adequate set of options and that over a 20-year period offering inadequate options makes a difference in terminal wealth of over 53%. We find that funds included in the plans are riskier, but have a slightly higher return, than the general population of funds in the same categories. However, we find that the return difference is roughly equal to the difference in expenses between funds selected by plans and randomly selected funds. We study the characteristics of plans that are associated with adequate investment choices, including an analysis of the use of company stock, plan size, and the use of sophisticated strategies.
Marginal Stockholder Tax Effects and Ex-Dividend-Day Behavior -- Thirty-Two Years Later
(with Edwin J. Elton and Martin J. Gruber; a revised version of this paper appears in the August 2005 issue of The Review of Economics and Statistics)
Abstract
Since Elton and Gruber's (E&G) original article on taxes and ex-dividend price behavior was published in 1970, over 100 articles have appeared in the leading journals of financial economics examining whether prices fall by less than the dividends and, if so, whether or not the phenomenon is due to tax effects, market microstructure effects, or some other effect. The microstructure argument is the most serious alternative to the tax argument.
All of the microstructure arguments state that the fall in stock price should be less than the dividend, regardless of whether the dividend is taxable or tax-advantaged. By testing ex-dividend effects on a sample of closed-end funds where dividends are tax-advantaged, we find that taxes should and do cause the fund price to fall by more than the amount of the dividend. This is consistent with a tax argument and inconsistent with a microstructure argument. Examining the sample of tax-free dividends, we find that the E&G and return measures change across the two tax regimes exactly as theory suggests they should if taxes mattered.
We then examine non-tax-advantaged closed-end funds. For these funds we should find the traditional ex-dividend tax effects: the fall in price on the ex-dividend date should be less than the dividend during periods when capital gains taxes are less than income taxes. This is what we find. Furthermore, the ex-dividend behavior of these funds generally moves in the direction we would expect across two changes in tax regimes. The taxable sample not only substantiates the tax effect, it also demonstrates that the fall in price greater than the dividend for closed-end municipal bond funds was not due to some peculiar aspect of either our methodology or the closed-end fund industry.
Thirty-two years after E&G's original study, we find new and compelling evidence that taxes play an important part in affecting share price changes.
Incentive Fees And Mutual Funds
(with Edwin J. Elton and Martin J. Gruber; this paper appears in the April 2003 issue of The Journal of Finance)
Abstract
The impact of incentive fees on management performance has become an increasingly important subject in the literature of financial economics. Yet, despite a large number of theoretical articles on the impact of incentive fees, there has been almost no empirical testing of the theories. In this article we use a carefully constructed sample of mutual funds to study the impact of incentive fees. Mutual funds are a particularly interesting vehicle for studying incentive fees, for funds exist that have incentive fees and funds exist that do not have incentive fees. In addition, the data provided by mutual funds are sufficiently detailed to allow correction for both survivorship bias and self-selective bias. We find that while many of the theoretical implications of the literature on incentive fees are borne out, some are not.
We find that funds with incentive fees have not, on average, been able to earn positive incentive fees. This suggests that managers on average haven't been able to outperform their benchmarks or to design benchmarks which work to their advantage. However, funds with incentive fees have an average risk-adjusted performance of about zero, which is higher than most studies have found for funds without incentive fees. This suggests that funds with incentive fees have at least some tendency to attract superior managers and/or to obtain more effort from managers in place. While there seems to be a modest impact of incentive fees on average returns, there clearly is a larger impact on risk taking. Funds with incentive fees have higher risk than funds without incentive fees. Whether risk taking is measured in terms of tracking error or total risk, incentive fees cause risk taking. In addition, as theories suggest, funds that underperform their benchmarks in the first part of an evaluation period increase risk in the second part, while funds that are overperforming relative to the index tend to reduce risk.
Managers using incentive fees often pursue non-benchmark strategies in an attempt to earn excess returns and higher fees. For example, many funds with the S&P 500 as their benchmark have significant exposure to small stocks. Surprisingly, funds on average have a beta less than one when a beta greater than one would have provided a higher expected return with potentially the same tracking error.
There are two types of managers subject to incentive fees: internal managers and external managers. Internal managers play a larger role in setting the incentive benchmarks, while outside managers are more at risk of being replaced. Inside managers do better relative to the benchmark than outside managers, and they also take greater risk in terms of total risk and deviations from the benchmark.
Finally, we find that funds that have incentive fees attract more capital, ceteris paribus, than funds in general. This is an added reason why managers might choose to use an incentive fee.
(with Edwin J. Elton and Martin J. Gruber; a later version of this paper appears in the December 2001 issue of The Journal of Finance)
Abstract
The CRSP database is a fairly new publicly available database on mutual funds. It is comprehensive and is corrected for survivorship bias. It and the Morningstar database are likely to be the standard databases used by researchers in the future. Despite the care that has been exercised in compiling the CRSP database, we find that it needs to be corrected for certain types of problems. The most obvious bias we found in the CRSP database was that it calculated fund returns for months with multiple distributions on the same day in a way that caused returns in those months to be overstated. Although this bias has been corrected by CRSP in subsequent versions of their database, for studies that used database versions with this bias, we show that this overstatement has an impact on overall returns and alphas which can be of economic significance. The Morningstar database is free of this problem.
We show that while the CRSP database does not suffer from survivorship bias, it does suffer from omission bias. Because only some small funds under $15 million in total net assets have monthly data on the CRSP database, and because the omitted funds have much greater merge and liquidation rates, we show that the returns reported for that group of funds which have monthly data overstate the population returns and alphas. We then examine the data CRSP provides on mergers. While these data are quite good in identifying mergers, we show that there are major problems in merger dates and reporting return data up to the time of the merger.
Finally, after correcting for all of these influences, we compare the data in the CRSP database with the data in the Morningstar database. We examine differences in alphas and return over four five-year periods. There are many differences. The differences are most severe for the smallest funds. For all funds, the differences are larger as we go back in time. We develop a rule for differences in return that allows us to determine when differences in alpha are likely to arise.
Morningstar Ratings and Mutual Fund Performance
(with Matthew R. Morey; this is an expanded version of the paper in the September 2000 issue of The Journal of Financial and Quantitative Analysis)
Abstract
This study examines the degree to which the well-known Morningstar rating system is a predictor of out-of-sample mutual fund performance, an important issue given that high-rated funds receive the lion’s share of investor cash inflow. We use a data set based on domestic equity mutual funds (of various ages and investment objective styles) that is free from survivorship bias and adjusted for load fees to examine the predictive qualities of the rating system. In addition, we use various performance metrics over different time horizons and sample periods. We also compare the predictive qualities of the Morningstar rating system with those of alternative predictors: a “naïve” predictor of in-sample historical average monthly returns, one- and four-index in-sample alphas, and in-sample Sharpe ratios. The results indicate several main findings that are robust across different samples, ages and styles of funds, and different out-of-sample performance measures. First, low ratings from Morningstar generally indicate relatively poor future performance. Second, for the most part, there is little statistical evidence that Morningstar’s highest-rated funds outperform the next-to-highest and median-rated funds. Third, Morningstar ratings, at best, do only slightly better than the alternative predictors in terms of predicting future fund performance.