L1-regularized logistic regression for event-driven stock market prediction

This paper presents a machine learning method for event-driven stock prediction, using L1 regularized Logistic regression model. It studies the stock price movement after listed companies make announcements. The model uses specific events extracted from these announcements and combine with financial indicators of listed companies, macro indicators, and technical indicators as dependent variables. The listed companies are divided into sample sets based on market value size and industry. Experiments show that this model can be a good predictor of stock within one week after events occur. In addition, compared with commonly used machine learning methods, our model has a better overall ability.

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