We carry out several test cases to illustrate how the Probability of Backtest Overfitting (PBO) performs under different scenarios. We also assess the accuracy of PBO using two alternative approaches (Monte Carlo Methods and Extreme Value Theory).The paper "The Probability of Backtest...

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...

Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. This may explain why so many hedge funds fail to perform as advertised or as expected, particularly in the quantitative space. These false discoveries may have been prevented if academic...

Most publications in Financial ML seem concerned with forecasting prices. While these are worthy endeavors, Financial ML can offer so much more. In this presentation, we review a few important applications that go beyond price forecasting:1. Portfolio construction2. Structural breaks3. Bet...

Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. In this paper, we present a real example of how multiple testing information can be reported. We use that information to estimate the Deflated Sharpe Ratio of an investment strategy.A...

Selection bias under multiple backtesting makes it impossible to assess the probability that a strategy is false (Bailey et al. [2014]). This has two implications:1) “Most claimed research findings in empirical Finance are likely false” (Harvey et al. [2016])2) Most quantitative firms invest...

In recent years, Machine Learning (ML) has been able to master tasks that until now only a few human experts could perform.Some of the most successful hedge funds in history apply ML every day. However, myths about Financial ML have proliferated:a) The Sisyphus paradigm is applicable to ML,b) ML...

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...

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become...