Short-term stock price prediction based on echo state networks
Expert Systems with Applications: An International Journal
The application of echo state network in stock data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Bayesian forecaster using class-based optimization
Applied Intelligence
An interactive tool for the stock market research using recursive neural networks
International Journal of Advanced Intelligence Paradigms
Information Systems Frontiers
Using a case-based reasoning approach for trading in sports betting markets
Applied Intelligence
Proceedings of the 5th Annual ACM Web Science Conference
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Stock market prediction is attractive and challenging. According to the efficient market hypothesis, stock prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this paper, we investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest neighbor methods. Some inductive machine-learning classifiers--artificial neural network, decision tree, and k-nearest neighbor were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 65 percent.