In this paper, an application of the Bayesian classifier for short-term stock trend prediction is presented. In order to use Bayesian classifier effectively, we transform the daily stock price time series object into a data frame format where the dependent variable is the stock trend label and the independent variables are the stock variations of the last few days. Based on the posterior probability density function, we propose a new method for stock selection and then propose a new stock trading strategy. The numerical examples demonstrate the potential of the proposed strategy for application to short-term stock trading.

中文翻译：

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贝叶斯方法的短期股票投资新策略

本文提出了贝叶斯分类器在短期股票趋势预测中的应用。为了有效地使用贝叶斯分类器，我们将每日股票价格时间序列对象转换为数据帧格式，其中因变量是股票趋势标签，而自变量是最近几天的股票变化。基于后验概率密度函数，我们提出了一种新的股票选择方法，然后提出了一种新的股票交易策略。数值例子表明了所提出的策略在短期股票交易中的应用潜力。