Limiting False Positives in Empirical Asset Pricing Tests

该项目旨在解决资产定价测试中的数据挖掘问题，采用创新的跨学科方法，减少误报的发生。预期成果包括在金融方面扩大选择，以减轻数据挖掘，以及严格评估风险因素对预期回报的解释力的新准则。该项目的研究结果预计将大大提高我们对风险定价的理解。此外，拟议的工具预计将有广泛的应用，如公司融资和欺诈检测，并提供重大价值的金融研究及其利益相关者，如澳大利亚资产管理行业和政府监管机构。

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.