Empirical Finance is in crisis: Our most important "discovery" tool is historical simulation, and yet, most backtests published in leading Financial journals are flawed.The problem is well-known to professional organizations of Statisticians and Mathematicians, who have publicly criticized the...

Quantitative Meta-Strategies (QMS) are quantitative strategies designed to manage investment strategies. As a field, QMS is the mathematical study of the decisions made by the supervisor of a team of investment managers, regardless of whether their investment style is systematic or discretionary

The proliferation of false discoveries is a pressing issue in Financial research. For a large enough number of trials on a given dataset, it is guaranteed that a model specification will be found to deliver sufficiently low p-values, even if the dataset is random.Most academic papers and...

In mathematical finance, backtest overfitting relates to the usage of historical market data (a backtest) to develop an investment strategy, where the strategy profits from random patterns rather than variables' signals. Backtest overfitting is now thought to be a primary reason why quantitative...

Generalized dynamic portfolio optimization problems have no known closed-form solution. These problems are particularly relevant to large asset managers, as the costs from excessive turnover and implementation shortfall may critically erode the profitability of their investment strategies.In...

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...