Intelligent financial market prediction
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Financial market prediction is challenging. According to the efficient market and random walk hypotheses, market prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this dissertation, we investigated the predictability of stock and foreign exchange markets 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, k-nearest neighbor, and naïve Bayesian classifier - were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 71 percent.