Abstract: Research of quantitate investment on stock price prediction is effective to help investors increase profits. Recently, technologies of machine learning have been well applied to explore the issue of stock trading. In this paper, Logistic Regression and Support Vector Machines (SVM) were adopted to solve the problem of predicting the trend of stock movements. The experiment showed that these two models could be effectively used in the stock market of China. Returns based on strategies we constructed were significantly better than the HS300 index. We investigated the relationship between stock returns and various models using various models. It found that the SVM model results are optimal. The annual return of the strategy based on SVM reached 17.13% and the maximum Drawdown was 0.32. In the future, we will not only focus on the stock market, but also plan to apply this strategy to other investment fields, such as trading of digital currency. We will also use other algorithms for research and comparison
|
DOI:
10.17148/IJARCCE.2022.11632
