NCSA Faculty Fellow and Assistant Professor at the Gies College of Business at the University of Illinois Mao Ye’s research lies at the intersection of big data, high-performance computing and the economics, and finance realm. Using computing resources, Ye tackles large amounts of data currently being collected by companies and finance institutions. “The high-performance computing is more like a tool,” he said, “because we are basically doing big data research.”
As a member of the National Bureau of Economic Research (NBER), Ye—together with his colleagues Alex Chinco and Adam Clark-Joseph—plans to spread the word about HPC tools to researchers in finance.
To illustrate the impact high-performance computing can have on financial research, Ye points to a project he undertook with researchers from the University of Warwick and Cornell University that eventually brought about changes in U.S. trading reporting policies. Mao and his co-researchers were examining odd-lot trades, those that were for less than 100 shares. Originally, odd-lot trades were not reported for the daily Trade and Quote (TAQ) database—which contains transaction data stocks listed on the New York Stock Exchange (NYSE), American Stock Exchange (AMEX), and Nasdaq. It was assumed that such small quantities were made by small-time traders and therefore would not provide enough information about trends and share pricing.