Market frictions, investor sophistication, and persistence in mutual fund performance

Dumitrescu, Ariadna; Gil-Bazo, Javier

doi:10.1016/j.finmar.2018.01.001

Cited by 13 publications

(3 citation statements)

References 37 publications

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“…This is especially documented for actively managed U.S. equity funds -that of which are widely recognized in the literature. This is where, in the majority cases, the fund performance is the focus of concern, and the management fee is among the variables that explain its value (Dumitrescu & Gil-Bazo, 2018;Fama & French, 2010;Sharpe, 1966).…”

Section: Literature Reviewmentioning

confidence: 99%

Actual rate of the management fee in mutual funds of different styles

Perez

Szymczyk

2022

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Research background: Exponential growth of passive mutual funds after 2007?2008 global financial crisis put pressure on active fund managers to lower the management fees. The real costs of active fund management are, however, very often higher than the values of management fees reported publicly. Thus it is not easy to decide on the quality of the fund management and estimate the level of management charges optimal for the future fund performance. Purpose of the article: In this study, we propose to utilize an actual rate of the management fee (ARMF) disclosed in the management company financial statements as a measure of the real value of the management costs and investigate its determinants in mutual funds of different styles. Methods: Using a dataset of 21,618 monthly observations for 500 mutual funds from a market of diversified structure and high management fees charged we test the operating model of a mutual fund performance, and derive the formula of a before-fee return with the ARMF as its component. The fund performance is measured by a raw before-fee return and two types of risk-adjusted alphas based on the multifactor model of Carhart (1997) and the fund attributes. Later, using panel data we explain ARMF by mutual fund performance and attributes. We also compare the results to the ones obtained for the total operational cost (TOC) ? a value similar to ARMF that is disclosed in mutual fund financial reports. Findings & value added: We find that the proposed ARMF is related more to the size and not to the performance, age or a cash flow of mutual funds. We observe it among all studied fund styles. The largest deviations of the average ARMF are seen in the management companies that belong to the banks? capital groups. The proposed measure of the management fee included in the operating model of a mutual fund performance can be used for any local mutual fund worldwide, and compared with other fund markets of more or less diversified style structures.

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Section: Literature Reviewmentioning

confidence: 99%

Actual rate of the management fee in mutual funds of different styles

Perez

Szymczyk

2022

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“…Furthermore, the study of the literature is generally based on classical regression methods reflecting how explanatory factors are linked to fund performance (Golec, 1996;Gilbertet al, 2019). However, several empirical studies highlighted that investors tend to select funds with subsequent good performance 1 (Gruber, 1996;Matos et al, 2012;Dumitrescu & Gil-Bazo 2018). Consequently, classical tools may not be directly implemented when we consider the past return-chasing trading behaviors of investors.…”

Section: Research Problemmentioning

confidence: 99%

Investigating key funds characteristics influencing their investment performance in Saudi Arabia: A dynamic panel data approach

Belgacem

Ghardallou

Alshebel

2021

Investment Management and Financial Innovations

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The study examines if specific characteristics of funds influence the performance of Saudi equity mutual funds. Previous research has explored various aspects of mutual funds. However, the Saudi Arabia literature focuses on evaluating the funds’ performance. Hence, this study seeks to close this gap by providing a framework to explain the equity fund performance. Several risks adjusted performance measures are applied such as Jensen’s alpha, lower partial moment alpha, Sharpe ratio, LPM-Sharpe ratio using the dynamic panel specification over the period 2010–2019. Based on the LPM alpha, the risk-adjusted return analysis reveals that the Saudi equity funds outperformed their benchmark over the full sample period. The empirical results show that major fund-specific characteristics such as fund size, past performance, and flow explain future performance. Besides, the evidence confirms that Saudi funds benefit from the economies of scale and expertise, while funds requiring higher levels of initial investment tend to exhibit lower performance levels. These findings provide investors and fund managers with useful information to make the optimal investment decisions in the mutual fund industry.

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“…to appropriately adjust returns for risk, which suggests that investors' ability to judge mutual fund performance is less than assumed by Berk and Green (2004) (Berk and Van Binsbergen, 2016;Barber et al, 2016;Evans and Sun, 2021). In addition, frictions may prevent investors' flows from driving fund performance towards zero (Dumitrescu and Gil-Bazo, 2018;Roussanov et al, 2021). Consequently, whether mutual fund performance is predictable is ultimately an empirical question that has received considerable attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

Can Machine Learning Help to Select Portfolios of Mutual Funds?

et al. 2021

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Identifying outperforming mutual funds ex-ante is a notoriously difficult task. We use machine learning methods to exploit the predictive ability of a large set of mutual fund characteristics that are readily available to investors. Using data on US equity funds in the 1980-2018 period, the methods allow us to construct portfolios of funds that earn positive and significant out-of-sample risk-adjusted after-fee returns as high as 4.2% per year. We further show that such outstanding performance is the joint outcome of both exploiting the information contained in multiple fund characteristics and allowing for flexibility in the relationship between predictors and fund performance. Our results confirm that even retail investors can benefit from investing in actively managed funds. However, we also find that the performance of all our portfolios has declined over time, consistent with increased competition in the asset market and diseconomies of scale at the industry level.

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Market frictions, investor sophistication, and persistence in mutual fund performance

Cited by 13 publications

References 37 publications

Actual rate of the management fee in mutual funds of different styles

Actual rate of the management fee in mutual funds of different styles

Investigating key funds characteristics influencing their investment performance in Saudi Arabia: A dynamic panel data approach

Can Machine Learning Help to Select Portfolios of Mutual Funds?

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