基于机器学习的股市拐点影响因素研究

Research on Factors Affecting Stock Inflection Point Based on Machine Learning Algorithms

股票市场的成交情况可以充分反映投资者的行为特征并影响整个股市的走势。股票成交明细数据作为股市最底层的交易数据,能够全面地体现股票交易的情况,成为至关重要的股票市场走势判断的参考数据,能够为资本市场监管者在风险监测领域进行决策提供有效帮助。文中提出了一种可以快速地在海量股票交易明细数据中提取投资者交易特征的方法,然后基于逻辑回归、决策树和随机森林等机器学习算法找到股市大盘较大拐点产生的主要影响因素,并预测交易特征变量对股市较大拐点产生的时间范围。在沪深股指上进行的实验表明,相较于传统的模型,文中提出的方法可以将股市较大拐点预测的准确度提高约10%,并在6个月的回测实验中准确率依旧保持在70%左右的水准,从而证明了模型的有效性。

Transaction situation in stock market can fully reflect behavior characteristics ofinvestors and affect the trend of entire stock market.As the bottom-level transaction data of stock market,detailed data of stock transaction can comprehensively reflect the situation of stock transactions and become a vital referencefor judgment of stock market trends.It can also provide regulators in capital market with effective information when making decisions in the field of risk monitoring.In this paper,we propose a method that can quickly extract the characteristics of investor transaction from detailed data of stock transaction,based on machine learning algorithms such as logistic regression,decision tree,and random forest,finding the main influencing factors of large inflection points and predictingtime range over which the larger inflection point occurs.The experimental results on the stock indexes of Shanghai and Shenzhen show that the proposed method can highly improve accuracy of prediction of large inflection point instock market by appoximately 10%,compared with a traditional model,and the accuracy rate in six-month backtesting experiment maintains a level of 70%,which demonstrates validity of the model in this paper.