Resampling DEA estimates of investment fund performance

Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds' returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds' returns. © 2012 Elsevier B.V. All rights reserved

Data envelopment analysis models of investment funds

This paper develops theory missing in the sizable literature that uses data envelopment analysis to construct return-risk ratios for investment funds. It explores the production possibility set of the investment funds to identify an appropriate form of returns to scale. It discusses what risk and return measures can justifiably be combined and how to deal with negative risks, and identifies suitable sets of measures. It identifies the problems of failing to deal with diversification and develops an iterative approximation procedure to deal with it. It identifies relationships between diversification, coherent measures of risk and stochastic dominance. It shows how the iterative procedure makes a practical difference using monthly returns of 30 hedge funds over the same time period. It discusses possible shortcomings of the procedure and offers directions for future research. © 2011 Elsevier B.V. All rights reserved

A Hybrid Model for Portfolio Optimization Based on Stock Clustering and Different Investment Strategies

In today's dynamic business environment, in order to compete in the market, financial institutes are trying to find the best portfolio policy that in turn leads to an increase in the return and a decrease in the risk for the investors. The objective of this study is to develop a portfolio considering the behavior of investors in risk taking. This research aims to support investors, experts and intermediate managers in establishing optimized portfolio of stocks according to investment strategy. The proposed model has used the five indexes of risk, return, skewness, liquidity and current ratio of 66 companies that enlisted in Tehran Stock Exchange Market and then clustered different companies using the hybrid method of clustering algorithm. After that, the clusters ranked using Topsis method. Ultimately, using genetic algorithm, the portfolio is established for different classes of investors with respect to their risk-taking level. The results show that the proposed model in comparison to general index, the industry index and the index of 50 more active companies are better in Tehran Stock Exchange.  Keywords: portfolio optimization, clustering, neural network, genetic algorithm JEL Classifications: C880, C61

Bibliometric overview and retrospective analysis of fund performance research between 1966 and 2019

Fund performance has been a hot topic in the financial research area, fair and correct evaluation of fund performance is of great significance for fund investors and companies. However, most of the relevant publications do not have any retrospective analysis of this topic in terms of knowledge domain to show its development trends and research concerns. To address this issue, two effective bibliometric tools namely Citespace II (The 5.3.R4 Edition) and SciMat are used to analyze the knowledge domain of this field in this paper. We have analyzed 979 articles related to fund performance from Web of Science between 1966 and 2019 (July), the analysis content includes the current status, collaboration network, co-citation network, and emerging trends of fund performance research, then we have derived the following desired conclusions: (1) In the last twenty years, there was a significant increase in the publication and citation numbers of fund performance research; especially, the relative research has become interdisciplinary and internationalized. (2) “Mutual Fund Performance”, “Fund Return”, “Investment Performance”, and “Portfolio Selection” are the hottest topics in the fund performance research. (3) “Small Fund” and “Investor Reaction” are the two emerging trends in the fund performance research. To sum up, there are two main contributions in this paper: First, we provide a full bibliometric analysis about the fund performance research. Second, we make the further development of fund performance research easier and more clearly to show the directions to learn and study for beginners

Mutual Fund Performance Evaluation using Data Envelopment Analysis with Higher Moments

Abstract The mutual fund industry has experienced huge growth internationally, becoming one of the primary vehicles through which individuals and most institutions invest in capital markets. Thus, the evaluation of the performance of mutual funds has become a very interesting research topic both for academic researchers for managers of financial, banking and investment institutions. This paper proposes Data Envelopment Analysis, a nonparametric approach, for the evaluation of mutual fund performance. This method is applied in both mean-variance and higher moment's framework on data of Greek mutual funds over the period 2007-2010 with encouraging results. JEL classification numbers: C61, G11, G2

Performance Evaluation of Portfolios with Margin Requirements

In financial markets, short sellers will be required to post margin to cover possible losses in case the prices of the risky assets go up. Only a few studies focus on the optimization and performance evaluation of portfolios in the presence of margin requirements. In this paper, we investigate the theoretical foundation of DEA (data envelopment analysis) approach to evaluate the performance of portfolios with margin requirements from a different perspective. Under the mean-variance framework, we construct the optimization model and portfolio possibility set on considering margin requirements. The convexity of the portfolio possibility set is proved and the concept of efficiency in classical economics is extended to the portfolio case. The DEA models are then developed to evaluate the performance of portfolios with margin requirements. Through the simulations carried out in the end, we show that, with adequate portfolios, DEA can be used as an effective tool in computing the efficiencies of portfolios with margin requirements for the performance evaluation purpose. This study can be viewed as a justification of DEA into performance evaluation of portfolios with margin requirements

Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds

Development and application of the data envelopment analysis (DEA) method, have been the subject of numerous reviews. In this paper, we consider the papers that apply DEA methods specifically to financial services, or which use financial services data to experiment with a newly introduced DEA model. We examine 620 papers published in journals indexed in the Web of Science database, from 1985 to April 2016. We analyse the sample applying citations network analysis. This paper investigates the DEA method and its applications in financial services. We analyse the diffusion of DEA in three sub-samples: (1) banking groups, (2) money market funds, and (3) insurance groups by identifying the main paths, that is, the main flows of the ideas underlying each area of research. This allows us to highlight the main approaches, models and efficiency types used in each research areas. No unique methodological preference emerges within these areas. Innovations in the DEA methodologies (network models, slacks based models, directional distance models and Nash bargaining game) clearly dominate recent research. For each subsample, we describe the geographical distribution of these studies, and provide some basic statistics related to the most active journals and scholars

On the informativeness of persistence for evaluating mutual fund performance using partial frontiers

The last few years have witnessed a rapid evolution in the literature evaluating mutual fund performance using frontier techniques. The instruments applied, mostly DEA (Data Envelopment Analysis) and, to a lesser extent, FDH (Free Disposal Hull), are able to encompass several dimensions of performance, but they also have some disadvantages that might be preventing a wider acceptance. The recently developed order-m and order-α partial frontiers overcome some of the disadvantages (they are robust with respect to extreme values and noise, and do not suffer from the well-known curse of dimensionality) while keeping the main virtues of DEA and FDH (they are fully nonparametric). In this article we apply not only the non-convex counterpart of DEA (FDH) but also order-m and order-α partial frontiers to a sample of US mutual funds. The results obtained for both order-m and order-α are useful, since a full ranking of the mutual funds' performance can be obtained. We merge these methods with the literature on mutual fund performance persistence. By combining the two literatures we derive an algorithm which establishes how the choice of m and α parameters intrinsic to order-m and order-α (respectively) relate to the existence of performance persistence and the contrarian effect

Resampling DEA estimates of investment fund performance

This article was submitted for publication in the serial European Journal of Operational Research [© Elsevier]. The definitive version is available at:http://dx.doi.org/10.1016/j.ejor.2012.07.015Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds' returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds' returns. © 2012 Elsevier B.V. All rights reserved

Portfolio optimization with asset preselection using data envelopment analysis

This paper uses data envelopment analysis (DEA) approach as a nonparametric efficiency analysis tool to preselect efficient assets in large-scale portfolio problems. Thus, we reduce the dimensionality of portfolio problems, considering multiple asset performance criteria in a linear DEA model. We first introduce several reward/risk criteria that are typically used in portfolio literature to identify features of financial returns. Secondly, we suggest some DEA input/output sets for preselecting efficient assets in a large-scale portfolio framework. Then, we evaluate the impact of the preselected assets in different portfolio optimization strategies. In particular, we propose an ex-post empirical analysis based on two alternative datasets: the components of S &P500 and the Fama and French 100 portfolio formed on size and book to market. According to this empirical analysis we observe better performances of the DEA preselection than the classic PCA factor models for large scale portfolio selection problems. Moreover, the proposed model outperform the S &P500 index and the strategy based on the fully diversified portfolio.Web of Scienc