From Man vs. Machine to Man plus Machine: The art and AI of stock analyses

• 成果类型: Article
• 署名作者: Cao, Sean; Jiang, Wei; Wang, Junbo; Yang, Baozhong
• 署名单位: University System of Maryland; University of Maryland College Park; Emory University; Louisiana State University System; Louisiana State University; University System of Georgia; Georgia State University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2024.103910
• 发表日期: 2024
• 关键词: Artificial intelligence; Machine Learning; FinTech; Stock analyst; Alternative data; Disruptive innovation

摘要

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win Man vs. Machinewhen institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in Man + Machine, which also substantially reduces extreme errors. Analysts catch up with machines after alternative databecome available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.