Timing and selectivity in Portuguese mutual fund performance

We extend the international evidence on timing and selectivity skills of fund managers by applying the Henriksson and Merton [Henriksson, R., Merton, R., 1981. On market timing and investment performance. II. Statistical procedures for evaluating forecasting skills. J. Bus. 54, 513–533] model to Portuguese based mutual funds investing in local, European and International equity.

The results show that managers do not exhibit selectivity and timing abilities, and there is even some evidence of negative timing. Furthermore, we observe a distance effect on stock selection performance, since fund managers that invest locally seem to perform better that those who invest in foreign markets. However, this effect is reverted with respect to market timing skills of fund managers, suggesting that International fund managers are more focused in market timing strategies.     

On the short-term persistence of mutual fund performance in Europe

In this article, we examine the short-term persistence in mutual fund performance in the main European markets between January 1990 and December 2022. The mutual fund industry in Europe has experienced significant growth in recent years as a consequence of the integration of its markets. However, the European mutual fund industry is still an unexplored area of research with only a small number of significant studies compared to the US industry. Using a sample of daily survivorship bias-free data on the five most important European mutual fund countries, which includes 2734 mutual funds across all countries, we find statistically significant persistence in the post-ranking quarter across different performance models for all countries. This evidence is present across all deciles including the top-decile and bottom-decile mutual funds. Further, we also extend our analysis to high inflation periods.     

How to measure mutual fund performance: economic versus statistical relevance

In the present paper a comprehensive assessment of existing mutual fund performance models is presented. Using a survivor‐bias free database of all US mutual funds, we explore the added value of introducing extra variables such as size, book‐to‐market, momentum and a bond index. In addition to that we evaluate the use of introducing time‐variation in betas and alpha. The search for the most suitable model to measure mutual fund performance will be addressed along two lines. First, we are interested in the statistical significance of adding more factors to the single factor model. Second, we focus on the economic importance of more elaborate model specifications. The added value of the present study lies both in the step‐wise process of identifying relevant factors, and the use of a rich US mutual fund database that was recently released by the Center for Research in Security Prices.     

Explaining persistence in mutual fund performance

This study investigates the determinants of persistence in mutual fund performance. Previous research that uses factor-mimicking portfolios and characteristic benchmarks to model fund performance fails to explain all the persistence in fund returns. This study employs a model that directly relates mutual fund returns to the characteristics of the stocks held by funds. Adjusting fund returns for the size of the stocks in which funds invest and financial ratios intended to capture fund manager investment styles explains all the persistence in mutual fund returns from 1976–1985, the period in which persistence is most prevalent.     

Revisiting mutual fund performance evaluation

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark results in different measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence fund performance evaluation utilizing stock selection and timing measures that incorporate the self-reported benchmark. We introduce a new factor exposure based approach for measuring the – static and dynamic – timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be misstating skill because they ignore the managers’ self-reported benchmark in the performance evaluation process.     

The effect of social screening on bond mutual fund performance

This study measures the financial impact of screening for environmental, social and governance criteria on corporate bond portfolios. Specifically, the risk-adjusted financial performance of 103 socially responsible bond funds in the US and the Eurozone is compared with a matched sample of conventional funds. During the period 2001–2014, socially responsible bond funds outperform by one-half of one percent annually. An evaluation of fund holdings and a performance-attribution analysis suggest that this outperformance is directly related to the mitigation of ESG risks, which is achieved by the exclusion of corporate bond issuers with poor corporate social responsibility activities. A separation of crisis and non-crisis periods further indicates that the outperformance is especially likely to occur during recessions or bear market periods. We confirm this crisis-related return effect from a sample of socially screened bond indices. Moreover, our results are robust to alternative definitions of sustainability, survivorship bias, fund characteristics and stable in the US and Eurozone sub-samples.     

A meta-analysis of mutual fund performance

The purpose of this article is to introduce the statistical technique of meta-analysis of regression results using as our example the Lee and Rahmann (1990) study of the performance of 93 mutual funds. Specifically, we derive and estimate the meta-analysis formulas, explicitly adjusted for correlated regression residuals, which quantify the effect of sampling error on their reported regression results. Our analysis of selectivity reveals some real variation around a mean risk-adjusted excess return of about 1% per year; while our analysis of market timing reveals some real variation around a negative mean value and confirms that the correction for heteroscedasticity does make a difference. An examination of the 80% probability interval for the mean selectivity value indicates that the best mutual funds can deliver substantial risk-adjusted excess returns.     

Portfolio concentration and mutual fund performance

Mutual fund managers should choose to increase their portfolio concentration when their information set is valuable enough that the benefits of the expected increase in alpha more than offsets the costs of the expected increase in idiosyncratic volatility. Consistent with that idea, we find that fund performance improves after concentration increases. Because the expected costs of increased concentration vary between funds and over time, the required expected benefits before managers choose to increase concentration should also vary. Among other results, we show that the concentration-performance relation is stronger for funds with less institutional ownership and when investor sentiment is low.     

Firm performance and mutual fund voting

We employ a new comprehensive proxy voting records database to investigate whether mutual funds consider prior firm performance when they vote on a diverse range of management- and shareholder-sponsored proposals relating to governance, compensation, and director election. We argue that prior firm performance plays a role in the monitoring effort of mutual funds as they fulfill their fiduciary duties. Results show that voting is related to prior firm performance for selected management and shareholder proposals and that it is consistent with Institutional Shareholder Services’ recommendations. Mutual funds support management (shareholder) proposals less (more) when prior firm performance has been weak. Furthermore, even when mutual funds deviate from their fund family’s voting policies, they attach importance to prior firm performance, and their voting is, to a certain degree, affected by business ties.     

Australian equity mutual fund size effects

Berk and Green propose a model of a superannuation fund industry, with a limited population of superior fund managers and a competitive investor market. In this market, superior fund managers capture the value they generate, leaving investors with a normal return on their investment. Furthermore, it is argued that previous period returns, age of the fund and management costs explain variation in net cash flow paid into a fund over time. The Berk and Green predictions find some support in empirical tests, reported in the present paper, based on Australian Morningstar retail and wholesale equity fund data over the period 1995–2005.     

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.     

Cross-sectional learning and short-run persistence in mutual fund performance

Using monthly return data of more than 6400 US equity mutual funds we investigate short-run performance persistence over the period 1984–2003. We sort funds into rank portfolios based on past performance, and evaluate the portfolios’ out-of-sample performance. To cope with short ranking periods, we employ an empirical Bayes approach to measure past performance more efficiently. Our main finding is that when funds are sorted into decile portfolios based on 12-month ranking periods, the top decile of funds earns a statistically significant, abnormal return of 0.26 percent per month. This effect persists beyond load fees, and is mainly concentrated in relatively young, small cap/growth funds.     

The value of coskewness in mutual fund performance evaluation

Recent asset pricing studies demonstrate the relevance of incorporating coskewness in asset pricing models, and illustrate how this component helps to explain the time variation of ex-ante market risk premiums. This paper analyzes the role of coskewness in mutual fund performance evaluation and finds evidence that adding a coskewness factor is economically and statistically significant. It documents that coskewness is sometimes managed and shows persistence of the coskewness policy over time. One of the most striking results is that many negative (positive) alpha funds, measured relative to the CAPM risk adjustments, would be reclassified as positive (negative) alpha funds using a model with coskewness. Therefore, performance ranking based on risk-adjusted returns without considering coskewness could generate an erroneous classification. Moreover, some fund characteristics, such as turnover ratio or category, are related to the likelihood of managing coskewness.     

Investor risk evaluation in the determination of management incentives in the mutual fund industry

This paper examines the way in which investors evaluate risk in deciding which mutual funds to invest. New fund investment is found to be positively related to a distributed lag of past fund performance with a strong degree of inertia. The relationship is mostly linear with significant nonlinearities at the upper (and possibly the lower) end of the performance spectrum. Investors appear to use publicly available data in a way that is consistent with the theory, giving equal weight in their decisions to the return and market risk components of the performance measure, while ignoring diversifiable risk. Finally, it is shown that improved performance in any year has a significant impact on the earnings of the management company. Because managers are rewarded on the basis of risk adjusted returns, risk neutral managers have no incentive to manipulate risk, except at very high performance levels.     

Australian retail fund performance persistence

The present study extends the Australian fund performance persistence literature through the use of five performance metrics: raw returns, the Sharpe ratio, the single‐factor model and two multifactor models, the Carhart (1997) model and the Gruber (1996) model, in analysis of Australian retail fund performance over the period 1991–2000. Analysis suggests that performance persistence is sensitive to fund objective and appears to be driven by inadequate adjustment for risk.     

A statistically robust decomposition of mutual fund performance

Previous decompositions of risk-adjusted mutual fund performance might deliver biased results. In this paper, we provide new reliable insights on the drivers of mutual fund performance by decomposing risk-adjusted performance of U.S. equity mutual funds using the Generalized Calendar Time regression model. According to our results, out of all previously considered fund characteristics, only the negative effect of lagged fund size and the positive effects of lagged performance and lagged family size remain highly significant. Our analysis further suggests that much of the variation in previous empirical results can be attributed to methodological issues.     

Eliminating look-ahead bias in evaluating persistence in mutual fund performance

Performance persistence studies typically suffer from ex-post conditioning biases. As stressed by Carhart [Carhart, M.M., 1997. Mutual Fund Survivorship, Working Paper, Marshall School of Business, U.S.C.] and Carpenter and Lynch [J. Financ. Econ. 54 (1999) 337.], standard methods of analysis on a survivorship free sample are subject to look-ahead biases. In this paper, we show how one can easily correct for look-ahead bias using weights based on probit regressions.First, we model how survival probabilities depend upon historical returns, fund age and aggregate economy-wide shocks, using two samples of US based ‘income’ and ‘growth’ funds. Subsequently, we employ a Monte Carlo study to analyze the size and shape of the look-ahead bias in performance persistence that arise when a survivorship free sample is used with standard techniques. In particular, we show that look-ahead bias induces a spurious U-shaped pattern in performance persistence. Finally, we demonstrate how a weighting procedure based upon probit regressions can be used to correct for this bias. In this way, we obtain look-ahead bias-corrected estimates of abnormal performance relative to a one-factor and the Carhart [J. Finan. 52 (1997) 57.] four-factor model, as well as its persistence. The results suggest that in this sample, look-ahead bias is of minor importance and does not seriously affect estimates of persistence. Our bias-corrected results closely correspond to the findings of Carhart [J. Finan. 52 (1997) 57.], implying that there is no evidence on a risk-adjusted basis for persistence in performance.     

New Zealand mutual funds: measuring performance and persistence in performance

The present study investigates the performance of New Zealand mutual funds using a survivorship‐bias controlled sample of 143 funds for the period of 1990–2003. Our overall results suggest that New Zealand mutual funds have not been able to provide out‐performance. Alphas for equity funds, both domestic and international, are insignificantly different from zero, whereas balanced funds underperform significantly. There is no evidence of timing abilities by the fund managers. In the short term, significant evidence of return persistence for all funds is observed. This persistence, however, is driven by ‘icy hands’ rather than ‘hot hands’. Finally, we find the risk‐adjusted performance for equity funds to be positively related to fund size and expense ratio and negatively related to load charges.     

A review of behavioural and management effects in mutual fund performance

This paper surveys and critically evaluates the literature on the role of management effects and fund characteristics in mutual fund performance. First, a brief overview of performance measures is provided. Second, empirical findings on the predictive power of fund characteristics in explaining future returns are discussed. Third, the paper reviews the literature on fund manager behavioural biases and the impact these have on risk taking and returns. Finally, the impact of organizational structure, governance and strategy on both fund risk taking and future performance is examined. While a number of surveys on mutual fund performance are available, these have not focused on the role of manager behavioural biases, manager characteristics and fund management strategic behavior on fund performance and risk taking. This review is an attempt to fill this gap. Empirical results indicate that finding successful funds ex-ante is extremely difficult, if not impossible. In contrast, there is strong evidence that poor performance persists for many of the prior “loser fractile” portfolios of funds. A number of manager behavioural biases are prevalent in the mutual fund industry and they generally detract from returns.     

Starting on the wrong foot: Seasonality in mutual fund performance

We document a systematic seasonal component in the aggregate underperformance of active mutual funds. At the aggregate level, active funds underperform the market and other passive benchmarks only in the first month of a quarter. This intra-quarter performance seasonality holds across fund sizes and investment styles. The pattern is consistent with short-term stock return reversal effects along with aggregate window-dressing and, to a lesser extent, NAV-inflation practices around quarter-ends. We find marginal or no evidence of microstructure biases, fund investor flows, or cash distributions as sources of this seasonality. Our findings highlight new features of the active management underperformance puzzle.