10.71932/ijm.2025.1184552
Evaluation of Fixed Income Mutual Funds' Performance with Data Envelopment Analysis
- Saeid Mehrabian*
1 - Ali Hadi2
- Fatemeh Fattahi1
Received: 20-09-2024
Accepted: 24-04-2025
Published in Issue 25-04-2025
Copyright (c) 2025 International Journal of Mathematical Modeling & Computations
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
In this paper we apply a model to evaluate the performance of mutual funds with fixed income. This model has an input based on a risk measure and two type returns as outputs. One of these outputs is expected return and other is excess return for funds in Iranian business mutual funds. The aim of the model is maximizing return for moderate customers who have chosen this mutual fund because of risk averse behavior. We evaluated the efficiency scores of all proposed mutual funds, on the other hand, this model presents benchmark of mutual funds which use different portfolio to pay guarantee return close to deposited banking rate or bonds and extra return to support investment faced on high inflation. Also, this model evaluates power mutual funds to use free-risk market and optimize portfolio management. Finally, we represent a numerical example include an application of the model by considering risk of 15 Iranian mutual funds during the period from 2011 to 2020 that obtained from a real dataset.to demonstrate the model useful to measure efficient mutual funds which guarantee a high potential return rate close to annual banking rate and pay best exceed return.
Keywords
- Data Envelopment Analysis,
- Mutual Funds,
- Risk Measure,
- Return,
- efficiency
References
- A.Basso, and S.Funari, (2001), A Data Envelopment Analysis Approach to Measure the Mutual Fund Performance, European Journal of Operation Research, 135(3), 477-492.
- R. D.Banker, A.Charnes and W. W. Cooper, (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, 30(9), 1078–1092.
- J.Beasley, (1995), Determining Teaching and Research Efficiencies. Journal of the Operational Research Society, 46, 441-452.
- L. Bogetoft Peter, Otto. (2011). International Series in Operations Research & Management Science: Benchmarking with DEA, SFA, and R, Springer Science & Business Media, 157.
- A. Charnes ,W. W. Cooper, and E. Rhodes, (1978), Measuring the Efficiency of Decision Making Units, European Journal of Operation Research, 2(6), 429–444.
- FIPIRAN. (n.d.). Retrieved from http://www.fipiran.com/Home/IndexEN.
- G. N. Gregoriou, and J. Zhu,(2005), Evaluating Hedge Fund and CTA Performance: Data Envelopment Analysis Approach, John Wiley & Sons Inc. New York, USA.
- M. R. Morey, and R. C. Morey, (1999), Mutual Fund Performance Appraisals: A Multi-horizon Perspective with Endogenous Benchmarking, OMEGA, 27, pp. 241-258.
- H. Markowitz, (1952), Portfolio Selection, Journal of Finance, 7(1), 77-91.
- B. P. S. Murthi, Y. K. Choi, and P. Desai, (1997), Efficiency of Mutual Funds and Portfolio Performance Measurement: A Non-parametric Approach, European Journal of Operation Research, 98(2), 408-418.
- J. K. Sengupta, (1989), Nonparametric Tests of Efficiency of Portfolio Investment, Journal of Economics, 50(1), 1-15.
- W. F. Sharpe, (1992), Asset Allocation: Management Style and Performance Measurement, Journal of Portfolio Management, 18(2),18, 7-19.
- A. Stawowy and J. Duda,(2017), A Study of the Efficiency of Polish Foundries Using Data Envelopment Analysis, Archives of Foundry Engineering, 17(1), 223 – 227.
- A. C. Tarnaud and H. Leleu (2018), Portfolio Analysis with DEA: Prior to Choosing a Model, OMEGA, 75, 57-76.
- K. Tran, A. Bhaskar, J. Bunker and B. Lee, (2017), Data Envelopment Analysis (DEA) Based Transit Route Temporal Performance Assessment: A Pilot Study, In Proceedings of the Transportation Research Board (TRB) 96th Annual Meeting, United States of America, 1-23.