The Effect of News Photo Sentiment on Stock Price Crash Risk Based on Deep Learning Models

Gaoshan Wang () and Xiaomin Wang
Additional contact information
Gaoshan Wang: Shandong University of Finance and Economics
Xiaomin Wang: Shandong University of Finance and Economics

Computational Economics, 2025, vol. 65, issue 5, No 9, 2679-2706

Abstract: Abstract This study examines the impact of investor sentiment on stock price crash risk from the perspective of news photo sentiment. First, the paper derives investor sentiment from news photos based on deep learning models. Second, we develop regression models analyzing the relationship between investor sentiment and stock price crash risk. The empirical analysis results show that news photo sentiment has a significantly positive effect on stock price crash risk and exhibits a stronger predictive power than sentiment embedded in news text. In addition, our study shows that positive news photo sentiment has a stronger impact on stock price crash risk in bull markets than in bearish markets. Our findings have great implications for investors, market analysts, and policy makers.

Keywords: News photo sentiment; Stock price crash risk; Deep learning; Investor sentiment (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10659-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10659-5

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10659-5

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().