Combining PI Sigma Neural Network with Multiple Offspring Genetic Algorithm for Stock Market Price Prediction
Sipra Sahoo1, Saroj Kumar Mohanty2, Sateesh Kumar Pradhan3
1Sipra Sahoo*, Computer Science Department, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India.
2Saroj Kumar Mohanty,Computer Science and Engineering department, Trident Academy of Technology, Bhubaneswar, India.
3Sateesh Kumar Pradhan, Computer Application department,Utkal University, Bhubaneswar, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6934-6939 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2103109119/2019©BEIESP | DOI: 10.35940/ijeat.A2103.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Accurate and precise prediction of pricing of stock market is a very demanding task because of volatile, chaotic nature of time series data. Artificial Neural Networks played a major role for solving diversified problems for its robustness, strong capability for solving non linear problems and generalization ability. It is a popular choice for researchers for foretelling the financial time series data. In the article Pi Sigma Neural Network (PSNN) is developed for foretelling of stock market pricing in different time horizons. Pricing of stock market is predicted for one, fifteen and thirty days in advance. The parameters of the network are interpreted and optimized by Multiple Offspring Genetic Algorithm (MOGA). The motivation of this study is to achieve global optima with faster convergence rate. Bombay stock Exchange (BSE) data set is used for implementing the proposed model. Performance of the proposed model is evaluated using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Median Average Error (MedAE) . The results are compared with Pi Sigma Neural Network with Genetic Algorithm (PSNN-GA) and Pi Sigma Neural Network with Differential Evolution (PSNN-DE). It is concluded that the proposed model outperforms PSNN-GA and PSNN-DE models.
Keywords: Forecast Stock Market, Pi Sigma Neural Network, Genetic Algorithm, Differential Evolution, Multiple Offspring Genetic Algorithm optimization.