Stock market prediction with multiple classifiers

Stock market prediction is challenging. According to the efficient market hypothesis, stock price should follow a random walk pattern and thus cannot be predictable much past 50 percent.

In this paper, we examine Dow Jones Industrial Index and show that not all periods are random. We use the Hurst exponent to select a period with great predictability.

Parameters to generate training patterns are determined heuristically by auto-mutual information and false nearest neighbor. Three inductive machine-learning classifiers — artificial neural network, decision tree and k-nearest neighbor are then trained by these generated patterns.

한국 주가지수 등락 예측을 위한 유전자 알고리즘 기반 인공지능 예측기법 결합모형 < 논문상세 < 페이퍼서치

Through appropriate ensemble of these models, we achieve prediction accuracy to 65 percent. Stock market prediction with multiple classifiers Bo Qian , Khaled Rasheed Appl. Showing of 43 references.

Stock market prediction with multiple classifiers - Semantic Scholar

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Stock market prediction with multiple classifiers | SpringerLink

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