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Alongside technology experts from American Express, Credit Suisse and CIBC, Ascendant’s Jason Morton will speak on developments in regulatory technology at the ‘Fintech Data Day’ at the annual Strata Data Conference on September 26, 2017 in New York. The Strata Data Conference is an annual conference for technology and business professionals who are seeking innovative and cutting-edge ways to leverage data to add value to their businesses, and Jason’s presentation will focus on challenges and opportunities for technologies to help firms and regulators detect manipulation in the markets, specifically focusing on how to leverage data to detect spoofing and layering.
FINRA has defined layering as “entering limit orders with the intended effect of moving the market to obtain a beneficial execution on the other side of the market” and spoofing as the practice of “entering orders to entice other participants to join on the same side of the market at a price at which they would not ordinarily trade, and then trading against the other market participants’ orders.” Ultimately, both of these actions are attempts by traders to manipulate the price of securities by creating false representation of market interest and then directly benefiting from those false impressions.
For example, in a recently-settled CFTC enforcement case, a trader was placing spoof orders on the opposite side of the market as his ‘real’ orders for customers in order to create a false flurry of market activity regarding the securities, in this case gold and silver futures contracts. This false activity was designed to induce other market participants to fill his real orders. Once they did, he cancelled his spoof orders, which he never had any intention of filling anyway. On a number of occasions, he actually did move the market on these gold and silver contracts.
The expectation from regulators is not only that no one in your organization is committing that type of market manipulation or fraud as in this CFTC case, but also that you have adequate training, policies and controls to prevent it and good enough technology to detect it right away. This self-policing, Jason points out, has a critical dependency on a firm’s internal data-handling capabilities, but with the right technology framework, tools can detect patterns in large, structured data sets from fields like time stamps and cancellation messages and flag anomalies to compliance or risk officers to review. Jason is optimistic about the future of technology to help firms and regulators root out fraud and market manipulation in the industry, particularly in light of developments in machine learning and artificial intelligence – both key topics of discussion at Strata and internally with our own engineers: “this has the potential to unlock even higher rates of accuracy in discerning ordinary market behavior, particularly market-making behavior, from intentionally fraudulent behavior.”
Jason advises Ascendant on applied mathematics, statistics and quantitative modeling for the firm’s proprietary post-trade compliance technology suite. He is a tenured professor of Mathematics and Statistics at Pennsylvania State University and a visiting scholar in Computer Science at Harvard University. He has published papers in mathematics, mathematical finance, statistics, machine learning, computational complexity and quantum physics. His research has included factor modelling, credit derivative pricing and the foundations of deep learning. He holds a Ph.D. in Mathematics from U.C. Berkeley, an M.A. in Economics from the University of Michigan, and an A.B. from Harvard University. In addition to his academic experience, Dr. Morton worked for Credit Suisse in Mergers and Acquisitions and Technology Investment Banking, seeded hedge funds and managed an endowment.
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