2019/02/24 by Quanxi Wang, Wang, Quanxi
Decision Sciences · Engineering · #Computational Finance (q-fin.CP) #Energy Load and Power Forecasting #FOS: Economics and business #Forecasting Techniques and Applications #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.1902.08938
openalex publication_date 2019/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the widespread engineering applications ranging from artificial\nintelligence and big data decision-making, originally a lot of tedious\nfinancial data processing, processing and analysis have become more and more\nconvenient and effective. This paper aims to improve the accuracy of stock\nprice forecasting. It improves the support vector machine regression algorithm\nby using grey correlation analysis (GCA) and improves the accuracy of stock\nprediction. This article first divides the factors affecting the stock price\nmovement into behavioral factors and technical factors. The behavioral factors\nmainly include weather indicators and emotional indicators. The technical\nfactors mainly include the daily closing data and the HS 300 Index, and then\nmeasure relation through the method of grey correlation analysis. The\nrelationship between the stock price and its impact factors during the trading\nday, and this relationship is transformed into the characteristic weight of\neach impact factor. The weight of the impact factors of all trading days is\nweighted by the feature weight, and finally the support vector regression (SVR)\nis used. The forecast of the revised stock trading data was compared based on\nthe forecast results of technical indicators (MSE, MAE, SCC, and DS) and\nunmodified transaction data, and it was found that the forecast results were\nsignificantly improved.\n