Can Machines Understand Human Decisions? Dissecting Stock Forecasting Skill

Date
2021
Authors
Cao, Sean
Guo, Xuxi
Xiao, Houping
Yang, Baozhong
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Human decisions are important but difficult to understand or predict. This paper uses machine learning models, which are adept at capturing nonlinear and complex relations, to analyze analysts’ forecasts and determine their skill. Machine-identified skilled analysts persistently outperform expert-picked star analysts. Machines rely on nonlinear interactions of analyst characteristics, such as past skill and efforts, to make predictions, in contrast with human experts, who lean more on relation-based information such as brokerage size. The puzzle of post analyst-revision drifts can be explained by our model in that such drifts are concentrated in machine-picked skilled analysts. Our approach also allows the formation of a “smart” analyst consensus that aggregates the forecasts of machine-picked skilled analysts. Investment strategies based on revisions of machine-identified skilled analysts and the smart analyst consensus both generate significant abnormal returns. Overall, we propose an interpretable machine learning framework that can be used to analyze and predict human decisions. We also provide a new, improved way to obtain the wisdom of the crowd applicable to other settings such as online forums, political opinions, and macroeconomic outlooks.
Description
Keywords
Machine Learning, Artificial Intelligence, Analyst Forecast, Analyst Skill, Crowd Wisdom
Citation
Extent
Format
Geographic Location
Time Period
Related To
Rights
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.