Investing in mutual funds: are you paying for performance or for the ties of the manager?

Abstract

This study analyses the performance of US Mutual Funds, from the perspective of Long Memory (LM), exploring if the returns of MFs are systematic due to their active management or they are random. The sample was 200 US equity MFs, from four categories, Large Cap, Middle Cap, Small Cap and World Stock, both 1- and 5-stars rating funds according to Morning Star rating. The time period was starting between 1981 and 2006 and ending 2016. Rescaled Range Analysis (R/S) employed for the Hurst exponent estimation, so to detect LM. Using Surrogate Data Analysis (SDA), the study was extended to Hurst exponent estimation for surrogate time series. The findings suggest that the selection of a MF presents a lot of complexity for investors. The 5-star MFs, with high qualified, and so expensive managers, tend to achieve random returns, while the returns of 1-star MFs, are more systematic. These MFs have higher fees than the 5-star MFs, but the management fees paid are quite inferior. This leads to the conclusion, that it might be preferable to pay for gaining an almost the same, but systematic return than to pay for the ties of the manager.

Fractality of Borsa Istanbul during the COVID-19 Pandemic

“…Comparable approaches were implemented by [58][59][60][61]. Furthermore, in [62], authors analyzed the performance of American mutual funds from the perspective of long memory using R/S and surrogate data analysis.…”

Economic Decision-Making Using Rough Topological Structures

“…As fnancial complexity rises, so do transactional and operational costs, and ML enables analysts to handle a greater volume of data and mine information previously unattainable through automated transaction processes. Although predicting stock price direction has been studied for years by individuals and fnancial frms, there is a large body of the literature on the subject (for instance see [1][2][3][4][5][6][7]) that did not use mathematical methods for predicting stock price direction, and thus identifying economic growth. However, empirical research focusing on fxed-income market direction prediction, particularly using machine learning methodologies, is scarce, and such literature is rarely repeatable.…”

“…However, empirical research focusing on fxed-income market direction prediction, particularly using machine learning methodologies, is scarce, and such literature is rarely repeatable. Tere are earlier studies of the use of R/S analysis and Hurst exponent in the stock market (see [1][2][3]) and the mutual fund industry (see [4]), while applications in air pollution are discussed in paper [5]. On the other hand, Pavlidis, et al [6] introduced and discussed some methods for fnancial forecasting.…”