Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market
| Autor: | Mingyue Qiu, Fumio Akagi, Yu Song |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Index (economics) Artificial neural network Computer science General Mathematics Applied Mathematics General Physics and Astronomy Statistical and Nonlinear Physics 02 engineering and technology computer.software_genre Backpropagation Local convergence Nonlinear system 020901 industrial engineering & automation Simulated annealing Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Stock market Data mining computer |
| Zdroj: | Chaos, Solitons & Fractals. 85:1-7 |
| ISSN: | 0960-0779 |
| DOI: | 10.1016/j.chaos.2016.01.004 |
| Popis: | Accurate prediction of stock market returns is a very challenging task because of the highly nonlinear nature of the financial time series. In this study, we apply an artificial neural network (ANN) that can map any nonlinear function without a prior assumption to predict the return of the Japanese Nikkei 225 index. (1) To improve the effectiveness of prediction algorithms, we propose a new set of input variables for ANN models. (2) To verify the prediction ability of the selected input variables, we predict returns for the Nikkei 225 index using the classical back propagation (BP) learning algorithm. (3) Global search techniques, i.e., a genetic algorithm (GA) and simulated annealing (SA), are employed to improve the prediction accuracy of the ANN and overcome the local convergence problem of the BP algorithm. It is observed through empirical experiments that the selected input variables were effective to predict stock market returns. A hybrid approach based on GA and SA improve prediction accuracy significantly and outperform the traditional BP training algorithm. |
| Databáze: | OpenAIRE |
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