A team from the Computational Finance Lab at UVM made a first-of-its-kind comprehensive study of the U.S. stock market. They found billions of opportunities over the course of one year for some traders to get price information sooner than others allowing so called high frequency traders to buy stocks at slightly better prices, and then, in far less than the blink of an eye, turn around and sell them at a profit.

This fall Threat Stack and UVM began a relationship with the potential to offer significant benefits to both the company and the university. Skalka, fellow Computer Science faculty member Joe Near and doctoral candidate John Ring have begun a project designed to enhance Threat Stack’s threat assessment process with artificial intelligence that could make cloud-based cybersecurity more efficient and accurate and lengthen the company’s lead in the marketplace.

A federally funded study provides new evidence of momentary pricing discrepancies that researchers say can be exploited by high-speed traders looking to make a quick profit.