Abstract:

Managing high-frequency market data has been a challenging task in finance. A limit order book is a collection of orders that a trader intends to place, either to buy or sell at a certain price. Traditional approaches often fall short in forecasting future limit orders because of their high frequency and volume. In this study, we propose a modified attention algorithm to analyze the movement patterns in a limit order book. The enormous amount of data with millisecond time stamps are efficiently examined and processed using an attention module, which highlights important aspects of limit orders. We demonstrate that our modified attention algorithm improves the forecasting accuracy of limit orders. (Joint work with Ms. Jiwon Jung)

Short Bio:
Professor Kiseop Lee is the director of Data Science in Finance professional masters program at Purdue University. Prof. Lee got a bechalor's degree in mathematics from Seoul National University in Korea, and masters and Ph.D degrees from Purdue University. He taught at University of Louisville before moved back to Purdue as a faculty. He has been a consultant of the financial company Invest QQQ and AI consulting firm Vivity AI. He has more than 40 peer-reviewed research papers in professional journals and has served as an associate editor for several mathematics, statistics, and financial engineering journals.

Zoom:

https://us02web.zoom.us/j/86114707362?pwd=ZDFWcnRkbTgzWTJiekhEcGxaa0dHZz09
ID: 861 1470 7362
PW: 148105