Limiting False Positives in Empirical Asset Pricing Tests

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The project aims to address the issue of data mining in asset pricing tests using innovative interdisciplinary approaches that mitigate the occurrence of false positives. The expected outcomes include extended options in finance for alleviating data mining, as well as new guidelines for rigorously evaluating the explanatory power of risk factors on expected returns. The project findings are expected to significantly advance our understanding of the pricing of risk. Additionally, the proposed tools are anticipated to have broad applications, such as corporate finance and fraud detection, and offer significant value to finance research and its stakeholders, such as the Australian asset management industry and government regulatory bodies.