Date of Award
Summer 2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics, Statistics and Computer Science
First Advisor
Merrill, Stephen
Second Advisor
Rowe, Daniel
Third Advisor
Maadooliat
Abstract
Many researchers have studied different strategies of statistical arbitrage to provide a steady stream of returns that are unrelated to the market condition. Among different strategies, factor-based mean reverting strategies have been popular and covered by many. This thesis aims to add value by evaluating the generalized pairs trading strategy and suggest enhancements to improve out-of-sample performance. The enhanced strategy generated the daily Sharpe ratio of 6.07% in the out-of-sample period from January 2013 through October 2016 with the correlation of -.03 versus S&P 500. During the same period, S&P 500 generated the Sharpe ratio of 6.03%. This thesis is differentiated from the previous relevant studies in the following three ways. First, the factor selection process in previous statistical arbitrage studies has been often unclear or rather subjective. Second, most literature focus on in-sample results, rather than out-of-sample results of the strategies, which is what the practitioners are mainly interested in. Third, by implementing hidden Markov model, it aims to detect regime change to improve the timing the trade.