Marcos López de Prado

Overview

Marcos M. López de Prado is a quantitative researcher and practitioner (Abu Dhabi Investment Authority; Cornell University; formerly Tudor, Guggenheim, AQR) known for work on machine learning in finance, backtest overfitting, and performance evaluation, and for the book Advances in Financial Machine Learning.

Key Contributions / Features

• Probabilistic / Deflated Sharpe ratio (Bailey-López de Prado 2012): uncertainty- and multiple-testing-adjusted Sharpe ratio metrics (Probabilistic Sharpe Ratio).
• Probability of backtest overfitting (Bailey et al. 2017; 2014): with Bailey, Borwein and Zhu, the PBO/CSCV framework and the "pseudo-mathematics" critique of data-snooping in quantitative investment (Backtest Overfitting).
• Broad contributions to financial machine learning and its methodology.

Related

• Probabilistic Sharpe Ratio
• Factor Zoo
• Bailey-López de Prado (2012) The Sharpe Ratio Efficient Frontier
• Bailey-López de Prado (2014) The Deflated Sharpe Ratio
• Backtest Overfitting
• Bailey et al. (2017) The Probability of Backtest Overfitting
• Bailey et al. (2014) Pseudo-Mathematics and Financial Charlatanism
• David H. Bailey