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

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-06-23 DOI:10.1007/s10614-024-10659-5
Gaoshan Wang, Xiaomin Wang
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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.

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基于深度学习模型的新闻图片情绪对股价暴跌风险的影响
本研究从新闻照片情绪的角度研究了投资者情绪对股价暴跌风险的影响。首先,本文基于深度学习模型从新闻图片中得出投资者情绪。其次,建立回归模型分析投资者情绪与股价暴跌风险之间的关系。实证分析结果表明,新闻照片情感对股价暴跌风险有显著的正向影响,并且比新闻文本中的情感表现出更强的预测能力。此外,我们的研究还表明,与熊市相比,牛市中正面新闻图片情绪对股价暴跌风险的影响更大。我们的研究结果对投资者、市场分析师和政策制定者具有重大意义。
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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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