基地执行副主任孙宪明教授、研究员任晓航副教授合作论文 “Investor sentiment from images: a few-shot learning investigation”在重要学术期刊Journal of Accounting Literature发表。

摘要：This research aims to extract emotional features from New York Times news images (2018–2023) using few-shot learning approaches. Leveraging machine learning, it offers a systematic investigation into how image-driven emotions affect investor behavior in the US equity market and contribute to the prediction of market movements.Design/methodology/approach:This study employs the DeepEMD model to extract emotional features from 181,233 news images, constructing a daily sentiment index based on visual media. By defining sentiment thresholds, the study develops differentiated strategies for positive and negative emotional signals. In addition, it integrates four machine learning models – AdaBoost, Support Vector Machine, ExtraTrees and Random Forest – alongside a traditional linear regression model to forecast the prices of various US stock market indices.Findings:This study finds that news image sentiment has a significant impact on financial markets. Positive sentiment strategies applied to serious news topics are associated with higher returns, whereas negative sentiment in entertainment-related content signals potential opportunities for contrarian investment. Moreover, the influence of image-based sentiment on the market exhibits a delayed effect of approximately 2–3 days, with particularly strong predictive power for small-cap stocks. Compared with the traditional linear models, machine learning approaches demonstrate superior performance in capturing the nonlinear dynamics between sentiment and market behavior, offering novel analytical tools for behavioral finance research and sentiment-driven anomaly-based investment strategies.Originality/value:This study integrates visual data analysis into the domain of behavioral finance, highlighting the distinctive role of image-based sentiment in uncovering market anomalies and informing investment strategies.

关键词：Few-shot learning, Investor sentiment, Image classification, Financial forecasting

论文链接：https://doi.org/10.1108/JAL-04-2025-0199l

教师简介

孙宪明，中南财经政法大学金融工程系教授、系主任，数字技术与现代金融学科创新引智基地执行副主任。研究方向为金融工程、金融科技及其相关领域，研究成果发表在Journal of Economic Dynamics and Control, Journal of Economic Behavior & Organization，Journal of Futures Markets, Accounting and Finance, Energy Economics等期刊。荣获中南财经政法大学“文澜青年学者”，主持国家自然科学基金、中央高校基本科研业务经费项目等科研项目。相关研究曾获第十六届中国金融工程学年会优秀论文奖。

任晓航，中南大学商学院特聘副教授，Elsevier中国高被引学者，全球前2%顶尖科学家，主要从事能源金融、金融风险、金融计量等方面的研究，在Journal of the American Statistical Association、Transportation Research Part A、Energy Economics、Quantitative Finance、Review of Quantitative Finance and Accounting、International Review of Financial Analysis、Technological Forecasting and Social Change、Business Strategy and the Environment、Pacific-Basin Finance Journal、Applied Energy、Resources Policy、Renewable & Sustainable Energy Review、Energy、管理科学学报（英文版）、系统工程理论与实践、中国管理科学等国内外权威期刊上发表论文一百余篇，其中ESI热点文章/高被引论文三十余篇。担任Sustainable Communities(Taylor & Francis)领域主编，Humanities & Social Sciences Communications（Nature旗下唯一面向人文社会科学的子刊）等期刊副主编，Climate Change Economics, Economic Change and Restructuring等SSCI期刊客座主编。