Pairs trading - A quantitative approach

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Pairs trading - A quantitative approach

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Title: Pairs trading - A quantitative approach
Author: Wong, Kevin Manhau
Abstract: Pairs trading is an old and widely known investment strategy that focuses on profiting from the relative mispricing between assets. In pairs trading there are generally two steps to constructing a model. The first step determines which pairs of stocks are suited for pairs trading. The second step concerns how to trade the candidate pairs. In this thesis we vary various types of parameters to see which are optimal. We differentiate between the pairs trading strategies and the pairs trend strategies. The pairs trading strategies are based on a spread series calculated from a pair of stocks to signal trades, while the pairs trend strategies rely on a trend indicator to signal for trades. We use the variance ratio test as a trend indicator. To determine the candidate pairs we use the squared minimum distance, correlation and cointegration to rank pairs of stock and select the attractive pairs. We also modify the pairs trading strategies in different ways to enhance their performance. We find the best performing models in an in-sample period and use them in an out-of-sample period. We find the best performing models to be the minimum distance strategy filtered for volatility and the cointegration strategy, using only the entry type settings BEYOND and OUTWARDS.1 Before constructing the pairs trading and pairs trend strategies for analysis we first test for the persistence of cointegration in the years from 1988 to 2013 and investigate if there are statistical reasons for pairs trading to work. We find that their are statistical evidence in favor of persistence of cointegration, varies through time. The analysis of the pairs trading and pairs trend models consisted of analyzing colored scale tables of average monthly returns and Sharpe ratios, varying the different parameter settings maximum holding period, trade entry type and standard deviation threshold. We also used graphs displaying the average equity curve after each trade signal to gauge the robustness of the strategies. We find that the out-of-sample model struggles to generate a positive Sharpe ratio after transaction costs. The profits are modest at best and it is reasonable to infer that it could only be considered from the standpoint of an institutional investor. The Fama-French 3 factor regressions show no significant abnormal return before transaction costs, but not after transaction costs.
URI: http://hdl.handle.net/10417/6172
Date: 2017-01-04
Pages: 86 s.
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