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The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund’s ESG score.

The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied.

The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model.

Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds.

To the best of the authors’ knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology.

Outlines previous research on measuring the performance of investment funds, suggesting that data envelopment analysis (DEA) techniques can overcome some of the problems of the capital asset pricing model and give pointers for improvement. Uses DEA to assess the relative performance of 257 Australian mutual funds 1995‐1999 and logistic regression to investigate the characteristics which affect it. Describes the methodology and presents the results, which suggest that scale efficiency is the main source of overall technical efficiency and that both are higher for risk‐averse funds with high positive net asset flows. Explains the ASSIRT rating system for managed funds and finds the ratings strongly associated with DEA relative efficiency scores. Believes the findings are useful to analysts, investors and managers.

Mutual fund ratings are extremely popular among mutual fund investors, with over 8,000 mutual funds currently available to them and a huge increase in privately managed retirement accounts. Morningstar introduced the star-rating system to mutual funds, grading them on a range of one to five stars, with one star being the lowest and five stars being the highest. Because of its simplicity and resemblance to the ratings of so many other products we buy, the star-rating system has become an intrinsic element of mutual fund jargon. Morningstar experts award 5-star funds a gold, silver, or bronze medal ranking based on their instinctual analysis. This research investigates whether all gold-medal winning five-star mutual funds are equally efficient in terms of risk-adjusted performance. When total return, adjusted expense ratio, standard deviation, tax cost ratio, Sharpe ratio, and fund alpha are all considered, we discovered that not all “gold medal” mutual funds are equally efficient. Investors should take care even among “gold medal” funds since some are more efficient than others.

The purpose of this paper is to estimate the performance of 188 mutual funds relative to the risk/return frontier accounting for the transaction costs of producing a portfolio of investments.

The directional output distance function is used to estimate mutual fund performance. The method allows the data to define a frontier of return and risk accounting for the transaction costs associated with securities management and production of risky returns. Proxies for the transaction costs of producing a portfolio of securities include the turnover ratio, load, expense ratio, and net asset value. The estimates of mutual fund performance are bootstrapped to account for the unknown data generating process. By comparing each mutual fund’s performance relative to the capital market line the authors determine how the fund should adjust their portfolio in regard to risk and return in order to maximize the inefficiency adjusted Sharpe ratio.

The bootstrapped estimates indicate that the average mutual fund could simultaneously expand return and contract risk by 3.2 percent if it were to operate on the efficient frontier. After projecting each mutual fund’s return and risk to the efficient frontier the authors find that a majority of the mutual funds should reduce risk to be consistent with the capital market line.

Many researchers have used data envelopment analysis to estimate a piecewise linear frontier of risk and return to measure mutual fund performance. To the authors’ knowledge the research is the first to use a twice-differentiable quadratic directional distance function to measure the managerial performance and risk/return tradeoff of mutual funds.

The main purpose of this study is to use the Sharpe Ratio to test the efficient market hypothesis for different market capitalization and investment styles of mutual funds. The results of the study for the entire period of 1994‐2007 as well as the two subperiods (1994‐1999 and 2000‐2007) indicate that small cap funds have provided the highest risk‐adjusted return for the entire period whereas growth funds have exhibited lower returns. The findings, therefore, suggest that the mutual funds market is not always efficient, which makes it possible for an investor or a mutual fund manager to earn excess return on a risk‐adjusted basis.

This study groups mutual funds using k-means clustering analysis and compares the k-means clustering process with existing clustering techniques using mutual fund data for equity funds, general fixed-income funds, and balanced open-end mutual funds rated by the Association of Investment Management Companies. Data are from January 2016 to December 2020 for 60 months and includes information on prices, risks, and investment policies. The sample for this study comprises 173 funds from 10 asset management companies with the highest net assets. The tool used for analysis is the k-means technique using a statistical package set for k = 3. The funds can be divided into three groups: Group 1 has 5 mutual funds (2.89%), Group 2 has 24 mutual funds (13.87%), and Group 3 has a total of 144 mutual funds (83.24%). In Group 1, four of the five mutual funds are equity funds with a track record of beating the market, and fund managers have good market timing skills. Moreover, the efficiency of fund grouping using the k-means technique was compared with the existing grouping with close results at 57.23%. This work provides a methodology to obtain a better categorization of mutual funds by using k-means clustering, allowing the investors to know how mutual funds are. This categorization is very useful for improving the formulation of mutual funds, with the goal of further optimizing investment.