Application and Comparison of Multiple Machine Learning Models in Finance

Accurate and effective financial data analysis is very important for investors to avoid risks and formulate profitable investment strategies. Therefore, the analysis of financial data has important research significance. However, the financial market is a complex nonlinear dynamic system affected by many factors. It is very challenging to analyze the financial data according to the obtained information. Among them, stock selection is the most typical financial data mining problem. The core of stock selection is to design a systematic scoring mechanism to quantitatively score stocks so as to more intuitively reflect the investment value of stocks. The scoring mechanism is based on the assumption that stocks with higher scores have higher investment value and stocks with lower scores have lower investment value. The stock selection model proposed in this paper mainly includes two steps: stock prediction and stock scoring. First, construct stock predictors and use machine learning forecasting methods to predict the future price of each stock. Second, construct a stock scoring mechanism to evaluate each stock through the predictive factors and financial factors in the previous step. Finally, select high-scoring stocks and make equal-weight investments. This paper applies the model to the empirical study of the A-share market, verifies its feasibility and effectiveness, and makes a systematic comparison with other benchmark models.