Mutual Fund Investment Method Using Recurrent Back Propagation Neural Network
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.
KeywordsMutual fund Back propagation neural network (BPNN) Recurrent back propagation neural network (RBPNN) Recurrent radial basis function neural network (RRBFNN)
- 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.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.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.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.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