Open AccessCombining PI Sigma Neural Network with Multiple Offspring Genetic Algorithm for Stock Market Price Prediction
Author(s) -
Sudarsan Sahoo,
Saroj Kumar Mohanty,
Sateesh Kumar Pradhan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a2103.109119
Subject(s) - artificial neural network , mean squared error , stock market , computer science , sigma , robustness (evolution) , algorithm , genetic algorithm , mathematical optimization , econometrics , mathematics , artificial intelligence , machine learning , statistics , biology , paleontology , biochemistry , physics , horse , quantum mechanics , gene
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.