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 $\approx$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.

中文翻译：

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p-Hacking 的局限性：一些思想实验

假设 300 多个公布的资产定价因素都是虚假的。产生这些因素需要多少p- hacking？如果 10,000 名研究人员每天生成 8 个因子，则需要数百年。这是因为数十个已发表的t统计量超过 6.0，而相应的p值则无穷小，这意味着在一般模型中存在天文数字的p黑客攻击。更多的结构意味着p -hacking 无法解决$\approx$100 个已发布的超过 4.0 的t统计量，因为它们需要对t统计量甚至更多p黑客的难以置信的非线性偏好。这些结果意味着错误定价、风险和/或摩擦在股票回报中起着关键作用。