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The Limits of p-Hacking: Some Thought Experiments

机译:p-hacking的极限:一些思想实验

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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.
机译:假设300+公布的资产定价因素都是虚假的。 生产这些因素需要多少p-hacking? 如果10,000名研究人员每天产生八个因素,需要数百年。 这是因为几十个已发布的T统计值超过6.0,而相应的p值是无限的,这意味着在一般模型中的p-hacking的天文数量。 更多的结构意味着P-Hacking不能解决超过4.0的100个发布的T统计数据,因为它们需要对T型统计或更具p-hacking的可令人信心的非线性偏好。 这些结果意味着错误的,风险和/或摩擦在股票回报中具有关键作用。

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