Neural network DEA for measuring the efficiency of mutual funds

被引：14

作者：

机构：

[1] School of Management and Accounting, Allameh Tabataba'i University, Tehran, Nezami Ganjavi Street

[2] Department of IT Management, Iranian Research Institute for Information Science and Technology, Tehran, No. 1090, Enghelab Avenue

[3] Aston Business School, Aston University, Aston Triangle, Birmingham

[4] Department of Financial Engineering, University of Science and Culture, Tehran, Asharif Esfahani Blvd.

来源：

Hanafizadeh, P. (hanafizadeh@gmail.com) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 07期

关键词：

Back-ropagation DEA; Data envelopment analysis; DEA; Large dataset; Mutual fund; Neural network;

D O I：

10.1504/IJADS.2014.063229

中图分类号：

学科分类号：

摘要：

Efficiency in the mutual fund (MF), is one of the issues that has attracted many investors in countries with advanced financial market for many years. Due to the need for frequent study of MF's efficiency in short-term periods, investors need a method that not only has high accuracy, but also high speed. Data envelopment analysis (DEA) is proven to be one of the most widely used methods in the measurement of the efficiency and productivity of decision making units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper uses neural network back-ropagation DEA in measurement of mutual funds efficiency and shows the requirements, in the proposed method, for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of a large set of MFs. Copyright © 2014 Inderscience Enterprises Ltd.

引用

页码：255 / 269

页数：14

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