Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/196425
Authors: 
Myšková, Renáta
Hájek, Petr
Olej, Vladimír
Year of Publication: 
2018
Citation: 
[Journal:] Amfiteatru Economic Journal [ISSN:] 2247-9104 [Volume:] 20 [Year:] 2018 [Issue:] 47 [Pages:] 185-201
Abstract: 
Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal stock price behavior subsequent to their dissemination. However, previous studies to date have tended to focus on linear regression methods in predicting volatility. Here, we show that non-linear models can be effectively employed to explain the residual variance of the stock price. Moreover, we use meta-learning approach to simulate the decision-making process of various investors. The results suggest that this approach significantly improves the prediction accuracy of abnormal stock return volatility. The fact that the length of news articles is more important than news sentiment in predicting stock return volatility is another important finding. Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.
Subjects: 
stock return volatility
prediction
textual analysis
sentiment
meta-learning
JEL: 
G12
G17
C45
C53
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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