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IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
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A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
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Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
An empirical methodology for developing stockmarket trading systems using artificial neural networks
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Hybrid methods for stock index modeling
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Trading team composition for the intraday multistock market
Decision Support Systems
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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.