文章基本信息

标题：Machine Learning in Stock Price Forecast
本地全文：下载
作者：Zhen Sun ; Shangmei Zhao
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2020
卷号：214
页码：1-6
DOI：10.1051/e3sconf/202021402050
出版社：EDP Sciences
摘要：This paper analyzed and compared the forecast effect of three machine learning algorithms (multiple linear regression, random forest and LSTM network) in stock price forecast using the closing price data of NASDAQ ETF and data of statistical factors. The test results show that the prediction effect of the closing price data is better than that of statistical factors, but the difference is not significant. Multiple linear regression is most suitable for stock price forecast. The second is random forest, which is prone to overfitting. The forecast effect of LSTM network is the worst and the values of RMSE and MAPE were the highest. The forecast effect of future stock price using closing price of NASDAQ ETF is better than that using statistical factors, but the difference is not significant.