The Limits of p-Hacking: Some Thought Experiments

• 成果类型: Article
• 署名作者: Chen, Andrew Y.
• 署名单位: Federal Reserve System - USA; Federal Reserve System Board of Governors
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13036
• 发表日期: 2021
• 页码: 2447-2480
• 关键词: FALSE DISCOVERY RATES; cross-section; tests; variables; anomalies

摘要

Suppose that the 300+ published asset pricing factors are all spurious. How much p-hacking is required to produce these factors? If 10,000 researchers generate eight factors every day, it takes hundreds of years. This is because dozens of published t-statistics exceed 6.0, while the corresponding p-value is infinitesimal, implying an astronomical amount of p-hacking in a general model. More structure implies that p-hacking cannot address approximate to 100 published t-statistics that exceed 4.0, as they require an implausibly nonlinear preference for t-statistics or even more p-hacking. These results imply that mispricing, risk, and/or frictions have a key role in stock returns.