期刊
MATHEMATICS
卷 10, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/math10224255
关键词
sentiment analysis; market prediction; BeautifulSoup; ARX; VADER
类别
资金
- Lucian Blaga University of Sibiu
This study explores how sentiment analysis can improve the accuracy of regression models for predicting the opening prices of selected stocks. The findings show that sentiment analysis as an exogenous factor improves the goodness of fit in nonlinear autoregression models, and polynomial autoregressions outperform linear ones.
(1) Background: Since the current crises that has inevitably impacted the financial market, market prediction has become more crucial than ever. The question of how risk managers can more accurately predict the evolution of their portfolio, while taking into consideration systemic risks brought on by a systemic crisis, is raised by the low rate of success of portfolio risk-management models. Sentiment analysis on natural language sentences can increase the accuracy of market prediction because financial markets are influenced by investor sentiments. Many investors also base their decisions on information taken from newspapers or on their instincts. (2) Methods: In this paper, we aim to highlight how sentiment analysis can improve the accuracy of regression models when predicting the evolution of the opening prices of some selected stocks. We aim to accomplish this by comparing the results and accuracy of two cases of market prediction using regression models with and without market news sentiment analysis. (3) Results: It is shown that the nonlinear autoregression model improves its goodness of fit when sentiment analysis is used as an exogenous factor. Furthermore, the results show that the polynomial autoregressions fit better than the linear ones. (4) Conclusions: Using the sentiment score for market modelling, significant improvements in the performance of linear autoregressions are showcased.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据