The Community for Technology Leaders
Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2009)
Atlanta, Ga, USA
June 14, 2009 to June 19, 2009
ISBN: 978-1-4244-3548-7
pp: 2006-2013
Leonardo C. Martinez , Computer Science Department, Federal University of Minas Gerais - Brazil
Diego N. da Hora , Computer Science Department, Federal University of Minas Gerais - Brazil
Joao R. de M. Palotti , Computer Science Department, Federal University of Minas Gerais - Brazil
Wagner Meira , Computer Science Department, Federal University of Minas Gerais - Brazil
Gisele L. Pappa , Computer Science Department, Federal University of Minas Gerais - Brazil
ABSTRACT
Predicting trends in the stock market is a subject of major interest for both scholars and financial analysts. The main difficulties of this problem are related to the dynamic, complex, evolutive and chaotic nature of the markets. In order to tackle these problems, this work proposes a day-trading system that “translates” the outputs of an artificial neural network into business decisions, pointing out to the investors the best times to trade and make profits. The ANN forecasts the lowest and highest stock prices of the current trading day. The system was tested with the two main stocks of the BM&FBOVESPA, an important and understudied market. A series of experiments were performed using different data input configurations, and compared with four benchmarks. The results were evaluated using both classical evaluation metrics, such as the ANN generalization error, and more general metrics, such as the annualized return. The ANN showed to be more accurate and give more return to the investor than the four benchmarks. The best results obtained by the ANN had an mean absolute percentage error around 50% smaller than the best benchmark, and doubled the capital of the investor.
INDEX TERMS
CITATION

D. N. da Hora, G. L. Pappa, J. R. de M. Palotti, L. C. Martinez and W. Meira, "From an artificial neural network to a stock market day-trading system: A case study on the BM&F BOVESPA," Neural Networks, IEEE - INNS - ENNS International Joint Conference on(IJCNN), Atlanta, Ga, USA, 2009, pp. 2006-2013.
doi:10.1109/IJCNN.2009.5179050
95 ms
(Ver 3.3 (11022016))