Abstract:

It has always been regarded as the emphasis of research to reveal the operation law of stock market. In recent years, machine learning method has made good progress in stock forecasting, and it has shown unique advantages over traditional methods such as fundamental analysis and technical analysis. This paper focuses on collecting the key references in the field of stock prediction that uses machine learning methods in recent years, and analyzing as well as summarizing feature engineering, the application of machine learning algorithms and the main problems in stock prediction research. The characteristics and shortcomings of each algorithm in application are reviewed, and future development direction of this field is made a thorough analysis and forecasted from the aspects of transfer learning, feature engineering and deep learning model fusion.

Key words: stock forecast, machine learning, support vector machine, deep learning, intergrated learning

摘要：

揭示股票市场运行规律一直是研究的热点，近些年机器学习方法在股票预测方面取得了不错的进展，相较于传统的基本面分析、技术分析等方法，显示了独特的优势。从股票预测研究的主要问题、特征工程和机器学习算法应用等三个方面，对近年来该领域的主要文献进行总结，并针对每种算法在应用中的特点与不足进行评述。围绕目前机器学习在股票预测上遇到的主要问题，从迁移学习、特征工程、深度学习模型融合等方面进行了深入的分析与展望。

关键词: 股票预测, 机器学习, 支持向量机, 深度学习, 集成学习