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)


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.


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


  1. 1.
    Chiang W-C, Urban TL, Baldridge GW (1996) A neural network approach to mutual fund net asset value forecasting. Omega 24(2):205–215CrossRefGoogle Scholar
  2. 2.
    Indro DC, Jiang CX, Patuwo BE, Zhang GP (1999) Predicting mutual fund performance using artificial neural networks. Omega 27(3):373–380CrossRefGoogle Scholar
  3. 3.
    GC MP, Shettigar AK, Krishna P, Parappagoudar MB (2017) Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process. Appl Soft Comput 59:418–437CrossRefGoogle Scholar
  4. 4.
    Sutheebanjard P, Premchaiswadi W (2010) Stock exchange of Thailand index prediction using back propagation neural networks. In 2010 second international conference on computer and network technology (ICCNT), IEEE, 2010, pp 377–380Google Scholar
  5. 5.
    Khoa NLD, Sakakibara K, Nishikawa I (2006) Stock price forecasting using back propagation neural networks with time and profit based adjusted weight factors. In: International joint conference SICE-ICASE, 2006, IEEE, 2006, pp 5484–5488Google Scholar
  6. 6.
    Lee C-T, Chen Y-P (2007) The efficacy of neural networks and simple technical indicators in predicting stock markets. In: International conference on convergence information technology, 2007, IEEE, pp. 2292–2297Google Scholar
  7. 7.
    Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Fifteenth annual conference of the international speech communication association, 2014Google Scholar
  8. 8.
    Huang Z, Huang D, Lyu MR, Lok T (2006) Classification based on Gabor filter using RBPNN classification. In: 2006 International conference on computational intelligence and security, IEEE, 2006, vol 1, pp 759–762Google Scholar
  9. 9.
    Han HG, Guo YN, Qiao JF (2017) Self-organization of a recurrent RBF neural network using an information-oriented algorithm. Neurocomputing 225:80–91CrossRefGoogle Scholar
  10. 10.
    Han Hong-Gui, Qiao Jun-Fei (2012) Adaptive computation algorithm for RBF neural network. IEEE Trans Neural Netw Learn Syst 23(2):342–347CrossRefGoogle Scholar
  11. 11.
    Das SR, Mishra D, Rout M (2019) An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies. Phys A Statist Mech Appl 513:339–370CrossRefGoogle Scholar

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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