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.
Yasser El Hamoumi is an Algorithmic Trader with State Street Global Market's Agency Lending program. He is responsible for the analytical framework, quantitative development, and implementation of algorithmic pricing. Additionally, Mr. El Hamoumi oversees the technical development of the lending program's market microstructures, which includes but is not limited to the lending program's electronic trading platform, analysis and research on broker trading behavior, intraday price discovery, and implementation of new trading technologies. Prior to his current role, Mr. El Hamoumi worked as a quantitative developer with State Street's Liquidity and Liability Management team. In this role, he was primarily focused with the development of data intensive models used for the management of State Street's liabilities. These projects include the design of the firm's operational deposit model, the quantitative estimation of credit lines with central banks and financial market utilities, and the execution of Federal Reserve Bank mandated stress testing. Mr. El Hamoumi attended Union College on the Posse Foundation Full Tuition Leadership Merit Scholarship and holds a Bachelor of Science in Mechanical Engineering.