Applications of Academic Theory and Quant Techniques in Securities Lending
Stocks that are heavily shorted and go “special” in the securities lending market can exhibit interesting behavior that is different from stocks in more “normal” regimes. However, it can be difficult to identify and model events when stocks might be subject to these short-market pressures in the securities lending space. In this presentation, we discuss a research project that draws from academic literature, market insights, and quantitative techniques to arrive at differentiated insights on these “special” events. We discuss our approach to event classification predictive models, such as linear regression, time series forecasting, and k-nearest neighbors classifications, and the implications of the predicted cross-sectional behavior in our universe.
Stephanie Lo is the Head of Quantitative Driven Research (QDRSF) for State Street's Securities Finance business. She is part of State Street Associates, State Street's academic arm. In this capacity, Ms. Lo oversees multiple research projects related to Securities Finance. These projects include the use of alternative data, integration of academic insights and methods, and quantitative techniques such as machine learning and artificial intelligence. Prior to joining State Street, Ms. Lo worked as a natural gas trader at a quantitative trading firm and as a management consultant at the Boston Consulting Group. She has expertise in quantitative methods and economics. She has produced multiple academic papers in economics. Ms. Lo holds a PhD in Economics, a Masters in Economics, and a Bachelor of Arts in Economics from Harvard University.