Average Performance of U.S. Politicians’ Trades; Study on Possible Presence and Key Determinants of Abnormal Returns in Common Stock Transactions Made by Members of U.S. House of Representatives.

Abstract

This thesis analyses the trading behaviours of U.S. House of Representatives Members from 2020 to 2023, focusing on their common stock investments to determine if they achieved abnormal returns, suggesting trading on non-public information. Using a dataset from Periodic Transaction Reports filed by politicians and the calendar-time portfolio methodology, the study finds no significant abnormal returns, indicating that House Members did not utilize non-public information to achieve positive significant long-term gains. In certain samples, the study finds the trades done by Members underperformed the market by statistically significant margin, hinting at the fact that Members didn’t posses or act on information that would allow them to time the trades better. The analysis includes subsamples differentiated by political party affiliation, experience, education, and committee assignments, all showing no superior stock performance. These results support the effectiveness of the STOCK Act in preventing insider trading among politicians and align with the strong form of the Efficient Market Hypothesis. The study also reveals different investment strategies between Democrats and Republicans, but no significant abnormal returns for either party members. Despite the lack of positive abnormal returns, the research highlights the need for improved transparency in transaction reporting and auditing. This is crucial due to public perception of political trades and the practice of data vendors of selling refined trade data to investors at high prices. Enhanced transparency and auditing would even further deter unethical trading and increase public trust in political financial activities. It would also eliminate the gatekeeping of aggregated information, which is currently obscured by technical barriers, reducing the incentive for external data providers to sell this dataset to individual investors.