Dissertation/Thesis Abstract

Factor Based Statistical Arbitrage in the U.S. Equity Market with a Model Breakdown Detection Process
by Park, Seoungbyung, M.S., Marquette University, 2017, 55; 10280168
Abstract (Summary)

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.

Indexing (document details)
Advisor: Merrill, Stephen
Commitee: Maadooliat, Mehdi, Rowe, Daniel
School: Marquette University
Department: Mathematics, Statistics & Computer Science
School Location: United States -- Wisconsin
Source: MAI 56/04M(E), Masters Abstracts International
Subjects: Mathematics, Economics, Finance, Computer science
Keywords: Equity factor model, Markov, PCA, Pairs trading, Statistical arbitrage, Trading strategy
Publication Number: 10280168
ISBN: 978-1-369-85517-3
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