NDLI: From an artificial neural network to a stock market day-trading system: A case study on the BM&F BOVESPA

Content Provider	IEEE Xplore Digital Library
Author	Martinez, L.C. da Hora, D.N. de M. Palotti, J.R. Meira, W. Pappa, G.L.
Copyright Year	2009
Description	Author affiliation: Computer Science Department, Federal University of Minas Gerais - Brazil (Martinez, L.C.; da Hora, D.N.; de M. Palotti, J.R.; Meira, W.; Pappa, G.L.)
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.
Starting Page	2006
Ending Page	2013
File Size	1780037
Page Count	8
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5179050
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Stock markets Chaos Economic forecasting System testing Statistical analysis Uncertainty Benchmark testing Computer networks Genetic algorithms
Content Type	Text
Resource Type	Article

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