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Mutual Fund Investment Method Using Recurrent Back Propagation Neural Network

  • Smruti Rekha DasEmail author
  • Debahuti Mishra
  • Pournamasi Parhi
  • Prajna Paramita Debata
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)

Abstract

Mutual fund is an ideal investment basically for those who do not know how to invest money in the security market. Several research methods have been published by the researcher for predicting mutual fund. This study has explored on three prediction models such as back propagation neural network (BPNN), recurrent back propagation neural network (RBPNN) and recurrent radial basis function neural network (RRBFNN), and the prediction models are validated using SBI Magnum Equity and UTI Equity mutual fund dataset. These two mutual funds are outperformed over market cycles since 2010. The dataset has trained and tested in 7:3 ratio and the performance is validated using MSE during the training phase. The study has explored on 1, 5 and 7 days ahead prediction, and the performance of prediction methods are evaluated by RMSE and MAPE during testing. The simulation result shows that RBPNN is outperformed over the rest two prediction methods.

Keywords

Mutual fund Back propagation neural network (BPNN) Recurrent back propagation neural network (RBPNN) Recurrent radial basis function neural network (RRBFNN) 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Smruti Rekha Das
    • 1
    Email author
  • Debahuti Mishra
    • 2
  • Pournamasi Parhi
    • 2
  • Prajna Paramita Debata
    • 3
  1. 1.Department of Computer Science and EngineeringGandhi Institute for Education and TechnologyBhubaneswarIndia
  2. 2.Department of Computer Science and EngineeringSiksha ‘O’Anusandhan Deemed to be UniversityBhubaneswarIndia
  3. 3.International Institute of Information TechnologyBhubaneswarIndia

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