Investing Research Articles

What happens out-of-sample to stock portfolios with weights derived from extreme in-sample fitting? In their February 2016 paper entitled “Stock Portfolio Design and Backtest Overfitting”, David Bailey, Jonathan Borwein and Marcos Lopez de Prado examine backtest overfitting in the context of designing a stock portfolio/fund. Their test approach is: Construct split-adjusted, dividend-reinvested price series for all S&P 500 components as of January 22,… Keep Reading

Where should investors look for methodological edges in 21st century financial markets? In his brief August 2016 paper entitled “Mathematics and Economics: A Reality Check”, Marcos Lopez de Prado advises finance students (and practitioners) what mathematical/analytical expertise to acquire for successful 21st century investing and trading. Based on his experience with what kinds of analysts and mathematics are most successful in… Keep Reading

What happens to the rankings of Guru Grades after weighting each forecast by forecast horizon and specificity? In their March 2017 paper entitled “Evaluation and Ranking of Market Forecasters”, David Bailey, Jonathan Borwein, Amir Salehipour and Marcos Lopez de Prado re-evaluate and re-rank market forecasters covered in Guru Grades after weighting each forecast by these two parameters. They employ… Keep Reading

Why don’t machines rule the financial world? In his September 2017 presentation entitled “The 7 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines fixes for those failure modes. Based on more than two decades of… Keep Reading

Want your machine to excel in investing? In his January 2018 paper entitled “The 10 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado examines common errors made by machine learning experts when tackling financial data and proposes correctives. Based on more than two decades of experience, he concludes that:

How can machine investors beat humans? In the introductory chapter of his January 2018 book entitled “Financial Machine Learning as a Distinct Subject”, Marcos Lopez de Prado prescribes success factors for machine learning as applied to finance. He intends that the book: (1) bridge the divide between academia and industry by sharing experience-based knowledge in a… Keep Reading

Is there a tractable way of estimating the level of data snooping bias in investment strategy studies and thereby correcting for it? In their April 2018 paper entitled “Detection of False Investment Strategies Using Unsupervised Learning Methods”, Marcos Lopez de Prado and Michael Lewis summarize and validate an approach for estimating snooping bias derived from backtesting multiple strategies… Keep Reading

Data snooping bias is pervasive in published research and quantitative investment strategies. Should investors resign themselves to the consequence that investment managers/funds offer products picked mostly on past luck? In his May 2018 presentation package entitled “How the Sharpe Ratio Died, and Came Back to Life”, Marcos Lopez de Prado introduces an approach to Sharpe ratio estimation… Keep Reading

In what areas does machine learning have advantages over conventional financial/investment analysis? In his June 2018 presentation entitled “Nine Financial Applications of Machine Learning”, Marcos Lopez de Prado summarizes investing-related areas in which well-supervised machine learning outperforms conventional methods. Based on relevant research and his experience, he asserts that:

In a series of nine presentation slide sets (Lectures 1-9 of 10) on “Advances in Financial Machine Learning”, Marcos Lopez de Prado provides part of Cornell University’s ORIE 5256 graduate course at the School of Engineering (“Special Topics in Financial Engineering V”). The course description includes: “Machine learning (ML) is changing virtually every aspect of our lives…. Keep Reading