Blending News Text and Economic Policy Uncertainty to Forecast the Company’s Unexpected Earnings

Employing Chinese A-share market data, this study explores how news text and economic policy uncertainty (EPU) can be combined to predict a company’s unanticipated earnings using the XL (extra long) Transformer and long short term memory (LSTM) models. The results show that adding news text features...

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Bibliographic Details
Published inJournal of advanced computational intelligence and intelligent informatics Vol. 28; no. 4; pp. 776 - 782
Main Authors Guan, Yixin, Hu, Jinhao, Wang, Yutong, Gu, Wentao, Xi, Houjiao
Format Journal Article
LanguageEnglish
Published Tokyo Fuji Technology Press Co. Ltd 20.07.2024
Subjects
Data mining
Decision making
Deep learning
Earnings
Economic crisis
Economic forecasting
Economic policy
Global economy
International finance
Investments
Investors
Machine learning
Macroeconomics
New stock market listings
News
Performance prediction
Profits
Securities markets
Stock exchanges
Stock market delistings
Stock prices
Transformers
Uncertainty
Volatility
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Summary:Employing Chinese A-share market data, this study explores how news text and economic policy uncertainty (EPU) can be combined to predict a company’s unanticipated earnings using the XL (extra long) Transformer and long short term memory (LSTM) models. The results show that adding news text features or the EPU index can improve the model’s predictive performance. However, adding the EPU index improves the model prediction performance by a tiny amount. Next, news headlines have better predictive performance relative to news content. Meanwhile, as a supplement to news headlines, news content can further improve predictive performance. Finally, the XL-Transformer model has better predictive performance than the LSTM model, but the improvement in the effect is limited.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2024.p0776