Neural network DEA for measuring the efficiency of mutual funds

Payam Hanafizadeh*, Hamid Reza Khedmatgozar, Ali Emrouznejad, Mojtaba Derakhshan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)255-269
Number of pages15
JournalInternational Journal of Applied Decision Sciences
Issue number3
Publication statusPublished - 7 Jul 2014


  • back-ropagation DEA
  • data envelopment analysis
  • DEA
  • large dataset
  • mutual fund
  • neural network


Dive into the research topics of 'Neural network DEA for measuring the efficiency of mutual funds'. Together they form a unique fingerprint.

Cite this