| Título: | Prediction of Stock Market Index Using Neural Networks: An Empirical Study of BSE |
| Autores: |
Naik, R. Lakshman Manjula, B. Ramesh, D. Murthy, B. Sridhara Sarma, SSVN |
| Fecha: | 2012-09-09 |
| Publicador: | European Journal of Business and Management |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article Peer-reviewed Article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | Predicting stock data with traditional time series analysis has become one popular research issue. An artificial neural network may be more suitable for the task, because no assumption about a suitable mathematical model has to be made prior to forecasting. Furthermore, a neural network has the ability to extract useful information from large sets of data, which often is required for a satisfying description of a financial time series. Subsequently an Error Correction Network is defined and implemented for an empirical study. Technical as well as fundamental data are used as input to the network. One-step returns of the BSE stock index and two major stocks of the BSE are predicted using two separate network structures. Daily predictions are performed on a standard Error Correction Network whereas an extension of the Error Correction Network is used for weekly predictions. The results on the stocks are less convincing; nevertheless the network outperforms the naive strategy. Keywords: - Prediction of stock, ECN, Backpropagation, Feedforward Neural Networks, Dynamic system. |
| Idioma: | Inglés |
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