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<p>Due to the dynamic of stock markets, predicting stock price trends remains a massive challenge when utilizing machine learning models, especially in Bayesian models. Indeed, the distributions of input features extracted from stock datasets are seldom normal, making Gaussian density less discriminative. This paper introduces a novel predictive model, the Bayesian Classifier with Heuristic Likelihood Estimation (BC-HLE), in which heuristic likelihood estimation instead of the Gaussian density was utilized to release the normality assumption in the conventional naïve Bayes classifier. Our model leverages the concept of the <i>p</i>-value, which evaluates how close the testing value is to the expected value on a distribution. This approach yields a more accurate likelihood estimate without normality assumption. When tested on 55 stock datasets from the S&amp;P500 index, the proposed BC-HLE model outperformed conventional Gaussian classifiers and such machine learning models as support vector machines and multi-layer perceptron, regarding prediction accuracy. Additionally, its superiority was verified on the returns on investment when the stock trend prediction was applied to a simulated trading system. The experimental outcomes show that the proposed model is a reliable enhancement of Bayesian classifiers and can contribute to decision for the stock market investment.</p>

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A Novel Bayesian Model Enhanced with Heuristic Likelihood Estimation for the Prediction of Stock Price Trend

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月24日

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  • Van-Truc Vo,
  • Bor-Shen Lin

摘要

Due to the dynamic of stock markets, predicting stock price trends remains a massive challenge when utilizing machine learning models, especially in Bayesian models. Indeed, the distributions of input features extracted from stock datasets are seldom normal, making Gaussian density less discriminative. This paper introduces a novel predictive model, the Bayesian Classifier with Heuristic Likelihood Estimation (BC-HLE), in which heuristic likelihood estimation instead of the Gaussian density was utilized to release the normality assumption in the conventional naïve Bayes classifier. Our model leverages the concept of the p-value, which evaluates how close the testing value is to the expected value on a distribution. This approach yields a more accurate likelihood estimate without normality assumption. When tested on 55 stock datasets from the S&P500 index, the proposed BC-HLE model outperformed conventional Gaussian classifiers and such machine learning models as support vector machines and multi-layer perceptron, regarding prediction accuracy. Additionally, its superiority was verified on the returns on investment when the stock trend prediction was applied to a simulated trading system. The experimental outcomes show that the proposed model is a reliable enhancement of Bayesian classifiers and can contribute to decision for the stock market investment.