Authors: Payam Hanafizadeh; Hamid Reza Khedmatgozar; Ali Emrouznejad; Mojtaba Derakhshan
Addresses: School of Management and Accounting, Allameh Tabataba'i University, Nezami Ganjavi Street, Tavanneer, Valy Asr Avenue, P.O. Box 14155-6476, Tehran, Iran ' Department of IT Management, Iranian Research Institute for Information Science and Technology, No. 1090, Enghelab Avenue, P.O. Box 13185-1371, Tehran, Iran ' Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK ' Department of Financial Engineering, University of Science and Culture, Asharif Esfahani Blvd., Park Street, P.O. Box 13145-871, Tehran, Iran
Abstract: 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-propagation 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.
Keywords: mutual funds; data envelopment analysis; back-propagation DEA; neural networks; large datasets; efficiency.
International Journal of Applied Decision Sciences, 2014 Vol.7 No.3, pp.255 - 269
Available online: 07 Jul 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article