Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new 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.

«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.»

Markets generate vast amounts of data, and billions of dollars are at stake in mining that data for patterns to shed light on future market behavior. Companies on Wall Street and elsewhere employ various algorithms, examining different variables and factors, to find such patterns and predict the future.

Story Source: Materials provided by Cornell University. Original written by Melanie Lefkowitz. Note: Content may be edited for style and length.