Investigating Post-Earnings-Announcement Drift Using Principal Component Analysis and Association Rule Mining

• Main Author: Schweickart, Ian R. W.
• Format: Others
• Published: Scholarship @ Claremont 2017
• Subjects: 64P20; 91B84; Applied Statistics; Longitudinal Data Analysis and Time Series; Other Applied Mathematics
• Online Access: https://scholarship.claremont.edu/hmc_theses/94; https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1111&context=hmc_theses

Post-Earnings-Announcement Drift (PEAD) is commonly accepted in the fields of accounting and finance as evidence for stock market inefficiency. Less accepted are the numerous explanations for this anomaly. This project aims to investigate the cause for PEAD by harnessing the power of machine learning algorithms such as Principle Component Analysis (PCA) and a rule-based learning technique, applied to large stock market data sets. Based on the notion that the market is consumer driven, repeated occurrences of irrational behavior exhibited by traders in response to news events such as earnings reports are uncovered. The project produces findings in support of the PEAD anomaly using non-accounting nor financial methods. In particular, this project finds evidence for delayed price response exhibited in trader behavior, a common manifestation of the PEAD phenomenon.