Abstract: 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.

Key words: Machine learning, Risk monitoring, Stock inflection point, Stock market, Trend judgement