This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting model was found capable of consistently outperforming this benchmark strategy.
@article{urn:eudml:doc:40855,
title = {A heuristic forecasting model for stock decision making.},
journal = {Mathware and Soft Computing},
volume = {12},
year = {2005},
pages = {33-39},
zbl = {1103.91019},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40855}
}
Zhang, D.; Jiang, Q.; Li, X. A heuristic forecasting model for stock decision making.. Mathware and Soft Computing, Tome 12 (2005) pp. 33-39. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40855/