{"title":"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","authors":"Gaoshan Wang, Xiaomin Wang","doi":"10.1007/s10614-024-10659-5","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50647,"journal":{"name":"Computational Economics","volume":"13 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Effect of News Photo Sentiment on Stock Price Crash Risk Based on Deep Learning Models\",\"authors\":\"Gaoshan Wang, Xiaomin Wang\",\"doi\":\"10.1007/s10614-024-10659-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":50647,\"journal\":{\"name\":\"Computational Economics\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Economics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1007/s10614-024-10659-5\",\"RegionNum\":4,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1007/s10614-024-10659-5","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
The Effect of News Photo Sentiment on Stock Price Crash Risk Based on Deep Learning Models
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.
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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