An ensemble model for stock index prediction based on media attention and emotional causal inference

Wang, Juanjuan; Zhou, Shujie; Liu, Wentong and Jiang, Lin. 2024. An ensemble model for stock index prediction based on media attention and emotional causal inference. Journal of Forecasting, ISSN 0277-6693 [Article] (In Press)

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Abstract or Description

Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality-based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.

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Research data are not shared.


causality analysis, direct transfer entropy, GDELT dataset, multivariate time series, stock price prediction

Departments, Centres and Research Units:

Institute of Management Studies


31 January 2024Accepted
8 March 2024Published Online

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Date Deposited:

21 May 2024 14:21

Last Modified:

22 May 2024 01:55

Peer Reviewed:

Yes, this version has been peer-reviewed.


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