Full paper is available at: "https://ssrn.com/abstract=3193697" https://ssrn.com/abstract=3193697Most papers in the financial literature control for Type I errors (false positive rate), while ignoring Type II errors (false negative rate). This is a mistake, because a low Type I error can only be...

Most papers in the financial literature estimate the p-value associated with an investment strategy, without reporting the power of the test used to make that discovery. In this paper we provide analytic estimates to Type I and Type II errors for the Sharpe ratios of investments, and derive...

Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative hedge funds (smart beta, factor investing, stat-arb, CTAs, etc.) In this paper we examine why false positives are so prevalent in...

Finance cannot become a rigorous science (in the Popperian or Lakatosian sense), however it can still operate as an “industrial science”. This article describes the scientific method by which industrial finance discovers through experimentation, and avoids false discoveries

Economics (and by extension finance) is arguably one of the most mathematical fields of research. However, economists' choice of math may be inadequate to model the complexity of social institutions.In a constructive spirit, this note offers some advice on how students could increase their...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for...