Mutual Fund Performance Measurement Research Articles

Evaluation of Performance and Efficiency of Polish Open-End Mutual Funds under High Volatility Environment in Financial Markets

Theoretical background: Retail investors in the financial market have nowadays access to a wide range of investment products. One of the types of such products are open-end investment funds, which by design are asset masses managed by professional entities. Open-end investment funds became one of the more popular financial instruments that retail customers purchase. Purpose of the article: This article aims at determining the efficiency of mutual funds as measured by the rate of return. An important point of the study is to determine whether funds with lower total risk as measured by standard deviation achieved lower losses. Research methods: The research method is an analysis of performance of twenty Polish open-ended mutual funds in three different time horizons, by using classic mutual fund performance measures adjusted for negative returns, i.e. Sharpe, Treynor and Jensen alpha indicators as well as the Israelsen and Treynor ratios adjusted for negative return. Main findings: It has been observed that the high volatility in the financial market had a direct negative impact on the returns of these funds. When comparing the Treynor ratio adjusted for negative returns values it appears that some of the analysed equity funds performed better than, for example, stable growth funds. In case of high volatility in the stock market, both in the long and short term, the analysed stable growth funds did not bring more value to investors in relation to the total risk incurred than balanced or even equity funds, which is particularly noticeable in the case of three-year and annual results. This is because asset diversification did not fully work in the high market volatility seen since the beginning of 2022 mostly due to falling prices of debt securities caused by interest rate increases. The article also contributes to the interpretation of Sharpe and Israelsen ratios in case of similar negative rates of return and different volatility measured, because the Israelsen ratio may not be the best to compare such funds as it prioritizes the funds with lower risk and does not consider relation of risk to return.

Analisa Manajemen Portofolio Investasi Reksadana Syari’ah Ditinjau Dari Strategi Investasi Berdasarkan Resiko Investasi Dan Pengukuran Kinerja

Shari’ah mutual funds has its own charm than other types of mutual funds. Shari’ah mutual funds is not limited only to Muslims as the general public, who are already seeing the benefits of investment that is based on the choice of sector and company specific criteria more promising and minimize risk. Islamic mutual funds are able to contribute the confidence of investors amounted to 3.31% in 2011, 4.31% in 2012 and 3.79% in 2013. So the value of the confidence of investors on mutual fund products sharia to 2013 is still around 3-4% . As for growth on Islamic mutual funds into one of the capital market products showed an increase in value in 2012 amounted to 3.79% later in the year 2013 to 4.32%. Investment portfolio management works based framework (framework) for investment management which covers the planning, implementation, evaluation, and adjustment. In mutual funds, investment managers responsible for investment activities, which includes the analysis and selection of investment types, and perform the necessary actions for the benefit of investors. Form of mutual fund performance measurement Shari’ah assessed net asset value (NAV) of the sub weekly. Showed a good performance rekasadana Shari’ah when in a period of return obtained is more positive.Â

Performance Evaluation of Equity Funds: 2017-2019

Mutual funds are said to perform very well if they can provide a higher rate of return and minimize risk. This study aims to find out if the performance of stock mutual funds has a better performance than the market as a comparison (JCI) using sharpe and jensen methods and to find out if there is a difference in the rating of stock mutual fund performance measurement results between sharpe method and Jensen method.The analysis results using Sharpe method and Jensen method show that 30 mutual funds have a good performance where the performance value of stock mutual funds from sharpe and jensen method is above the average market value (JCI). The result of Sharpe method in 2017 shows that the best performing stock mutual fund is Batavia Stock Fund Optimal Stock Fund. In 2018 and 2019, the best performance was the Sucorivest Maxi Fund, while the result of Jensen's method in 2017, the best performing stock mutual fund was the Sucorivest Maxi Fund. In 2018, it showed the best performance, namely Trim Kapital Equity Fund. The Bahana Trailblazer Fund occupied the highest performance value in 2019. However, each equity fund will experience a change in its rating every year. For the best mutual funds in this research year.

Analisis Pengaruh Fund Longevity, Fund Size, dan Family Size terhadap Kinerja Reksadana pada Reksadana Saham di Indonesia Periode 2016-2019

This study aims 1). To analyze the effect of Fund Longevity on the performance of equity funds, 2). To analyze the effect of Fund Size on the performance of equity funds, 3). To analyze the effect of Family Size on stock mutual funds performance, 4). To analyze the effect of Fund Longevity, Fund Size, and Family Size simultaneously on the performance of equity funds.The method used in this research is quantitative research. Equity mutual fund performance measurement uses the Sharpe method. While the data analysis method used is multiple linear regression using statistical software SPSS 22. The results of this study indicate that 1). Fund Longevity has a significant negative effect on the performance of equity funds, 2). Fund Size has a positive and insignificant effect on the performance of equity funds, 3). Family Size has a significant negative effect on the performance of equity funds, 4). Fund Longevity, Fund Size, and Family Size simultaneously have a positive effect on the performance of equity funds.

A Comparative Study of Mutual Fund Portfolio Performance in Indonesia

A Mutual fund is one of the instruments in the capital markets that have an important role for investors. Through mutual funds, investors can invest their capital to get the returns with the risks that fit into each investor criteria. Asset under Management (AUM) is one of the indicator for investors in choosing a mutual fund. The more capital is deposited by the investor into a mutual fund, the greater the managed fund. This study aims to analyze how the performance of the portfolio held by equity mutual funds with the largest of Asset under Management when the stock market condition tends to be bearish. This research was conducted through three measurements, a comparison of the performance of the mutual funds returns with a market return, mutual fund diversification using coefficient determination and mutual fund performance measurement using risk-adjusted return, which are Sharpe Ratio, Treynor Ratio and Jensen’s Alpha. The results showed that the use of the entire measurement, mutual funds performance with the largest AUM outperform the market, but different results obtained for diversification measurement and Jensen’s Alpha.

Is Target-Date Mutual Fund Underperformance Rational?

This article investigates the performance of target date mutual funds (TDMFs) relative to several passive indexes by using widely accepted mutual fund performance measures—Sharpe, Sortino, and Omega ratios; FF-3; and FF-6—and find that TDMF performance is negative and significant in nearly all performance measures. The authors also find that TDMFs provide other benefits for investors searching for a choice between active and passive investing. They speculate that several investor biases may explain the paradox of the popularity and underperformance of TDMFs. The results of this article are very important for choice architects and individual investors responsible for retirement plan decisions in that TDMFs fill the current void in retirement plan offerings and will most likely continue to rise in popularity. This article also reveals the need to develop measurement tools designed specifically to determine the performance of TDMFs.

Testing models of the measuring performance of mutual fund based on single and dual beta

This study aims to test some models of mutual fund performance measurement with regard to the impact of time-varying beta volatility. Testing of models based on three issues: first, the single beta testing; second, dual beta testing; and third determining which model is the most valid and robust. Tests for each models uses a two-pass regression. Testing of comparison model uses a nested model. The research samples were 30 stock mutual funds in the Indonesian capital market period January 2008–December 2012. The results research showed three finding. The first, the single beta testing indicated that these three models were not valid and were not robust. The second, dual beta testing indicated that Treynor-Mazuy model and Paramita model were valid. The third, test of robustness model showed that Paramita model was the most robust than two other models and proved that the market return variable had explanatory power as a determinant factor of mutual fund returns.

Identifying the Value Premium: A Test of Mutual Fund Performance Measures

This paper extends recent discussion on the effectiveness of mutual fund performance measures. We utilize the well-known value premium to examine the ability of mutual fund performance measures to distinguish between the results of value funds and growth funds. Specifically, we examine the effectiveness of mutual fund performance measures over a sample period where value outperforms growth without question, where such measures ought to identify the superior performance of value funds. We find that the Capital Asset Pricing Model successfully identifies superior performance of value funds. The Fama-French three-factor model, on the other hand, provides an inaccurate comparison. We conclude that measurement using the Fama-French three-factor model is biased against value funds and should not be used to judge the performance of value funds, or in general, funds containing a significant proportion of value securities. We suggest that a similar bias would exist for mutual funds holding small-firm securities.

Mutual Fund Performance and Private Information

This paper examines the use of private information by mutual funds with unconditional and conditional performance models. Using daily data for 35 countries over the 1990-2015 period, we find evidence that the use of conditioning information provides a more accurate estimation of fund performance. The use of conditioning information in mutual fund performance measurement is both statistically and economically significant. We show that unconditional alphas and betas are slightly smaller than the average conditional estimates. However, we do not find a robust evidence of private information in mutual fund performance.

Can Weight-Based Measures Distinguish between Informed and Uninformed Fund Managers?

This paper studies weight-based mutual fund performance measures in a panel predictive regressions framework, where future stock returns are regressed on a fund's portfolio weights. Existing performance measures suffer biases related to benchmark misspecifications and are statistically inefficient. To address these issues, we introduce bias-adjusted and weighted least squares (WLS) measures. Simulations show that new methods can effectively control bias and improve power, compared with existing measures. We also apply the existing and newly introduced measures to empirical examples. Using bias-adjusted measures and efficient measures can lead to different conclusions about managers' abilities.

Neglected Risks in Mutual Fund Performance Measurement: An Additional Cost to Stock-Picking

This paper takes a closer look at utility based performance measurement proposed by Goetzmann et al. (2007) and used in popular Morningstar star ratings. Utility based per- formance measures offer a very intuitive way of risk correction and are hard to manipulate. They require, however, a proper benchmark measure to filter out lucky funds. I propose to use the Daniel et al. (1997) (DGTW) characteristic based benchmark portfolios as bench- marks for the utility based performance measure. I find that the DGTW selection measure consistently overestimates the manager’s selection skills in certainty equivalent terms, and that this overestimation can be decomposed into an idiosyncratic and a systematic compo- nent. In diversified fund portfolios, the certainty equivalent selection measure is, on average, 87bps higher than in undiversified fund portfolios. The remaining undiversifiable risks cost 47bps per year in certainty equivalent terms and can be explained in part be imprecise correction for known systematic risk factors, in part by unknown but undiversifiable risk factors. The certainty equivalent measure captures risk particularly well in years with high moment realizations of the CRSP value weighted index.

A Statistical Analysis of Mutual Fund Performance Measures: The Relevance of IRs, Betas, and Sharpe Ratios

Since Markowitz [1958] and Sharpe [1966], the increasing number of criteria and performance indicators have made mutual fund analysis more complex and sometimes risky. In this study, the author attempts to identify the most relevant indicators for classifying mutual funds based on their statistical properties. This study focuses on 15 indicators of performance relative to 210 equity mutual funds calculated monthly on three subperiods between 2000 and 2006. A comparison of statistical distributions, correlations, and a principal component analysis has not only confirmed the relevance of information ratios, betas, and Sharpe ratios, but also highlights the importance of a globally integrated approach based both on different calculation periods (short term, medium, and long term) and three dimensions on the performance analysis and mutual fund rankings (manager skill, market exposure, and relative performance). <b>TOPICS:</b>Statistical methods, mutual fund performance, passive strategies, developed

Selection, Timing and Total Performance of Equity Funds: Wasting Time Measuring Timing

Existing literature states that timing activities bias traditional mutual fund performance measures like Jensen’s alpha. In contrast, we show analytically and via a comprehensive empirical analysis that measures of total performance are virtually identical for Jensen’s alpha and popular unconditional timing models. Furthermore, we find similar relations for conditional timing measures. Finally, we show that all measures result in very similar statistical significance. Since measured selection and timing performance yield no informational benefit with respect to total performance, we conclude that Jensen’s alpha is an accurate total performance measure even in the presence of significant timing ability.

Performance measurement and best-practice benchmarking of mutual funds: combining stochastic dominance criteria with data envelopment analysis

We propose a method for mutual fund performance measurement and best-practice benchmarking, which endogenously identifies a dominating benchmark portfolio for each evaluated mutual fund. Dominating benchmarks provide information about efficiency improvement potential as well as portfolio strategies for achieving them. Portfolio diversification possibilities are accounts for by using Data Envelopment Analysis (DEA). Portfolio risk is accounted for in terms of the full return distribution by utilizing Stochastic Dominance (SD) criteria. The approach is illustrated by an application to US based environmentally responsible mutual funds.

Do Model and Benchmark Specification Error Affect Inference in Measuring Mutual Fund Performance?

This paper examines the implications for mutual fund performance measurement of two likely sources of specification error. We compare three well-known models, those of Jensen (1968), Treynor and Mazuy (1966), and Henriksson and Merton (1981), and two commonly-used timing benchmarks, the SP (2) benchmark misspecification results in qualitatively similar inferences, although statistical significance is not as strong; and (3) the power of detecting ability for an individual fund or for distinguishing between a good fund from a bad fund is typically quite low and such power is not appreciably altered by model and benchmark misspecification. These results are robust to alternative asset pricing specifications (CAPM versus Carhart 4-factor) and the periodicity of the simulation (calibrated to daily versus monthly data).

Mutual funds: risk and performance analysis for decision making

Preface. 1. Understanding Mutual Funds. 2. Mutual Fund Service Advantages. 3. Mutual Fund Service Advantages: Professional Management. 4. Mutual Fund Service Disadvantages. 5. Mutual Fund Service Disadvantages: Expenses. 6. Long-Term Investing in Mutual Funds. 7. Portfolio Diversification Risk and Equity Style Allocation. 8. Mutual Fund Performance Measures and Buy/Sell Decision Criteria. 9. Mutual Fund Performance and Portfolio Investment Style Allocations. 10. Actively Managed versus Passively Managed Funds: The Search for Managers. 11. Mutual Funds: The Next Generation. 12. Economic and Market Indicators: Knowledge, Not Faith. Index.

Evaluating Mutual Fund Performance

ABSTRACTWe study standard mutual fund performance measures, using simulated funds whose characteristics mimic actual funds. We find that performance measures used in previous mutual fund research have little ability to detect economically large magnitudes (e.g., three percent per year) of abnormal fund performance, particularly if a fund's style characteristics differ from those of the value‐weighted market portfolio. Power can be substantially improved, however, using event‐study procedures that analyze a fund's stock trades. These procedures are feasible using time‐series data sets on mutual fund portfolio holdings.

Conditioning Information and Variance Bounds on Pricing Kernels

We show how to use conditioning information optimally to construct a sharper unconditional Hansen-Jagannathan (1991) bound. The approach in this paper is different from that of Gallant, Hansen and Tauchen (1990), but both approaches yield the same bound when the conditional moments are known. Unlike Gallant, Hansen and Tauchen, our approach is robust to misspecification of the first and second conditional moments. Potential applications include testing dynamic asset pricing models, studying the predictability of asset returns, diagnosing the accuracy of competing models for the first and second conditional moments of asset returns, dynamic asset allocation and mutual fund performance measurement. The illustration in this article starts with the familiar Hansen-Singleton (1983) setup of an autoregressive model for consumption growth and bond and stock returns. Our innovation is to add time-varying volatility to the model. Both an unconstrained version and a version with the restrictions of the standard consumption-based asset pricing model imposed serve as the data-generating processes to illustrate the behavior of the bounds. In the process, we discover and explore an interesting empirical phenomenon: asymmetric volatility in consumption growth.

Evaluating Mutual Fund Performance

We study standard mutual fund performance measures, using simulation procedures combined with random and random-stratified samples of NYSE and AMEX securities. We track simulated fund portfolios over time. These portfolios? performance is ordinary, and well-specified performance measures should not indicate abnormal performance. Our main result, however, is that the performance measures are badly misspecified. Regardless of the performance measure, there are indications of abnormal fund performance, including market-timing ability, when none exists.