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What is Cost-Sensitive Stock Price Prediction about?

This article investigates the selection of technical indicators (TIs) for stock market forecasting to enhance prediction accuracy and reduce misclassification costs. The study ranks 50 TIs using various feature selection methods and evaluates nine classifiers, concluding that a cost-sensitive fine-tuned naïve Bayes classifier outperforms others in investment performance. Results indicate that using a minimal number of TIs, specifically between five to ten, yields the best balance between accuracy and invest

Author

陳衍丞

Language

EN