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...

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...

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...