Study: Machine learning can predict market behavior

Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area. The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility. "What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business. O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies. Other Cornell co-authors are: David Easley, the Henry Scarborough Professor of Social Science in the College of Arts and Sciences and professor of information science in Computing and Information Science; and Marcos Lopez de Prado, professor of practice in Operations Research and Information Engineering in the College of Engineering and chief information officer of True Positive Technologies.