Beckman Fellow 2013-14

Mao Ye


Using the Strength of Science and Engineering at the University of Illinois to Tackle Important Questions in Finance

Professor Ye is one of the first researchers in academic finance to use the power of supercomputing to analyze high-frequency trading. During his Center appointment, he will investigate abnormal trading activity and suspicious market events associated with computer-based “high-frequency trading.” Computer programs currently generate about 70 percent of market trading, and a substantial number of these trades are canceled within 50 milliseconds. The amount of associated data runs into terabytes.

Professor Ye is particularly interested in seeking evidence for a type of market behavior known as quote stuffing, which involves submitting a large number of orders to the market and quickly canceling them to slow down the trading system and then exploit the slow-down for profit. The Securities and Exchanges Commission is considering a minimum quote life of 50 milliseconds; but regulators and academic researchers are not yet clear about the purposes and consequences of these quick cancellations. These research results should help determine whether the practice has deceptive and manipulative purposes.

Another line of investigation will focus on odd lots (trades under 100 shares), which currently are not reported in the market data known as Trade and Quote (TAQ). High-frequency traders routinely slice and dice large orders into pieces smaller than 100 shares. Using a proprietary dataset from NASDAQ, Professor Ye has found that the percentage of odd-lot trades excluded from TAQ increased from about 12 percent in 2008 to more than 20 percent in 2009. He is now examining whether traders submitting these orders had private information, and whether previously reported research was biased by not considering the odd lots missing from TAQ.

Finally, Professor Ye proposes to digitize historical trading data of the New York Stock Exchange for the years 1818 to 1952. Currently, this trade-by-trade data is not available in digital form, and the one existing copy is in fragile condition. The initial steps of this project involve consulting with experts on the best technological approach and on the sample periods to prioritize for digitization. Fully implemented, this project will create a new and large-scale data resource for the entire academic community.