MCFAM Seminar Series: “Why financial research is prone to false statistical discoveries"
Please join us for our next seminar! This seminar will feature David H. Bailey from the University of California, Davis who will discuss why financial research is prone to false statistical discoveries.
About the lecture
It is a sad fact that few investment funds, models, or strategies actually beat the overall market averages over, say, a 10-year window. Even in academic research work, care must be taken to avoid statistical pitfalls because (a) the chances of finding a truly profitable investment design or strategy is very low, due to intense competition; (b) true findings are mostly short-lived, as a result of the non-stationary nature of most financial systems; and (c) it is often difficult to debunk a false claim. Backtest overfitting is a particularly acute problem in finance, both in academic research and commercial development, since it is a simple matter to use a computer program to search thousands, millions, or even billions of parameter or weighting variations to find an “optimal” setting. In this talk, David H. Bailey will summarize many of these pitfalls, explore why they are so prevalent, and present some tools that can be used to avoid them, including the “false strategy theorem.”
About the speaker
David H. Bailey (recently retired from the Lawrence Berkeley National Laboratory) is a research associate in the Department of Computer Science at the University of California, Davis. He has published studies in computational mathematics, high-performance computing, and mathematical finance. He has received the Chauvenet and Merten Hesse Prizes from the Mathematical Association of America, the Levi Conant Prize from the American Mathematical Society, the Sidney Fernbach Award from the IEEE Computer Society, and the Gordon Bell Prize from the Association for Computing Machinery. He and his colleague Marcos López de Prado (Cornell University and Abu Dhabi Investment Authority) have published several studies highlighting the dangers of backtest overfitting and other statistical difficulties in mathematical finance. Bailey is also the editor of the Mathematical Investor blog.