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. Results are derived using 87 liquid futures contracts across all asset classes.
O’Hara is co-author of “Microstructure in the Machine Age“. 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. Zhibai Zhang of New York University’s Tandon School of Engineering, also a quantitative researcher at Bank of America, joined in the research.
“Trying to estimate these sorts of things using standard techniques gets very tricky, because the databases are so big. The beauty of machine learning is that it’s a different way to analyze the data,” O’Hara said. “The key thing we show in this paper is that in some cases, these microstructure features that attach to one contract are so powerful, they can predict the movements of other contracts. So we can pick up the patterns of how markets affect other markets, which is very difficult to do using standard tools.”