Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Journal of Management Information Systems - Special section: Realizing value from information technology investment
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Forecasting financial condition of Chinese listed companies based on support vector machine
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
International Journal of Business Intelligence and Data Mining
Hi-index | 12.05 |
The study of financial markets has been addressed in many works during the last years. Different methods have been used in order to capture the non-linear behavior which is characteristic of these complex systems. The development of profitable strategies has been associated with the predictive character of the market movement, and special attention has been devoted to forecast the trends of financial markets. This work performs a predictive study of the principal index of the Brazilian stock market through artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is to compare the forecasting performance of both methods on this market index, and in particular, to evaluate the accuracy of both methods to predict the sign of the market returns. Also the influence on the results of some parameters associated to both methods is studied. Our results show that both methods produce similar results regarding the prediction of the index returns. On the contrary, the neural networks outperform the adaptive exponential smoothing method in the forecasting of the market movement, with relative hit rates similar to the ones found in other developed markets.