PROFITABILITY OF A SIMPLE PAIRS TRADING STRATEGY: RECENT EVIDENCES FROM A GLOBAL CONTEXT

2016 ◽  
Vol 19 (04) ◽  
pp. 1650023
Author(s):  
JIA MIAO ◽  
JASON LAWS
Keyword(s):  
Transaction Costs ◽  
Investment Strategy ◽  
Trading Strategy ◽  
Trading Rule ◽  
Pairs Trading ◽  
Working Paper ◽  
Bear Markets ◽  
Relative Value ◽  
Trading Profits ◽  
Market Indexes

Pairs trading strategy is a popular investment strategy, where traders long one stock and short the other stock. The trading profits are expected to be “immune” to any market conditions: being uptrend, downtrend, or sideways, instead the performance is determined by the relative performance of the pair. Following Gatev et al. [(1999) Pairs Trading: Performance of a Relative-Value Arbitrage Rule. Working Paper, Yale School of Management; (2006) Pairs trading: Performance of a relative-value arbitrage rule, The Review of Financial Study, 19, 797–827] and Do & Faff [(2010) Does simple pairs trading still work? Financial Analyst Journal, 66, 1–12], we examine whether the simple pairs trading rule is also profitable in markets outside of the US. We also examine whether the trading rule performs consistently during bull and bear markets, including the recent period of market turbulence. Our results show that in most countries, the strategy generates positive returns, without evidence of under performance during bear markets. Unlike prior research, we do not find that the trading profits diminish over recent years. The pairs trading strategy generates positive returns even after transaction costs. However, the returns deteriorate significantly at a higher level of transaction costs. It is also found that the correlation between the returns on our pairs trading portfolios and the returns on the corresponding stock market indexes is low, confirming its role as a diversifier to the traditional long only investments.

scholarly journals European Equity Pairs Trading: The Effect of Data Frequency on Risk and Return

2013 ◽  
Vol 1 (2) ◽  
pp. 329 ◽  
Author(s):  
Michael Lucey ◽  
Don Walshe
Keyword(s):  
Minimum Distance ◽  
Mean Reversion ◽  
Trading Strategy ◽  
Share Price ◽  
Trading Rule ◽  
Pairs Trading ◽  
Risk And Return ◽  
Rule Based ◽  
Data Frequency ◽  
European Equity

<p><em>This article examines an equity pairs trading strategy using daily, weekly and monthly European share price data over the period 1998 – 2007. The authors show that when stocks are matched into pairs with minimum distance between normalised historical prices, a simple trading rule based on volatility between these prices yields annualised raw returns of up to 15% for the weekly data frequency. Bootstrap results suggest returns from the strategy are attributable to skill rather than luck, while insignificant beta coefficients provide evidence that this is a market neutral strategy. Resistance of the strategy’s returns to reversal factors suggest pairs trading is fundamentally different to previously documented reversal strategies based on concepts such as mean reversion.</em><em></em></p>

scholarly journals Machine Learning and Algorithmic Pairs Trading in Futures Markets

2020 ◽  
Vol 12 (17) ◽  
pp. 6791
Author(s):  
Seungho Baek ◽  
Mina Glambosky ◽  
Seok Hee Oh ◽  
Jeong Lee
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Investment Strategy ◽  
Trading Strategy ◽  
Support Vector ◽  
Pairs Trading ◽  
Correction Model ◽  
Machine Learning Methods ◽  
Trading Market ◽  
Price Trends

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.

scholarly journals Optimal Pairs Trading Strategy under Geometric Brownian Motion and its Application to the US stocks

2021 ◽  
Vol 9 (5) ◽  
pp. 550-560
Author(s):  
Dong Hoon Shin
Keyword(s):  
Stock Returns ◽  
Price Level ◽  
Investment Strategy ◽  
Trading Strategy ◽  
Theoretical Background ◽  
Pairs Trading ◽  
Optimal Pair ◽  
Average Return ◽  
The Us ◽  
Stop Loss

This study is a study on pair trading, a representative market-neutral investment strategy. A general pair trading strategy uses econometric techniques to select a pair of stocks and calculates the trading price level depending on a single variable called the variance of stock returns without any theoretical background. This study applies the optimal pair trading strategy proposed by Liu et al. (2020) to the top US market cap stocks and examines its performance. This strategy proposes a mathematical background for optimally calculating the trading price level. Since the statistical method for pair selection can be omitted, a pair can be formed only with good stocks with guaranteed liquidity. In addition, strategic risk management is possible because the stop loss set according to the market situation is performed. As the top 10 market cap stocks traded on the US exchange, daily closing price data for 10 years from 2011 to 2020 were applied to optimal pair trading. It was confirmed that the rate of return may differ depending on the adjustment of various parameters including the level of stop loss. In this study, an applicated strategy that properly managed pairs trading and stocks together earned the minimum annual average return 17.88% and the Sharpe ratio reached 1.81. These numbers can be better with the adjustment of the parameters.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

Empirical Investigation of an Equity Pairs Trading Strategy

2019 ◽  
Vol 65 (1) ◽  
pp. 370-389 ◽  
Author(s):  
Huafeng (Jason) Chen ◽  
Shaojun (Jenny) Chen ◽  
Zhuo Chen ◽  
Feng Li
Keyword(s):  
Empirical Investigation ◽  
Trading Strategy ◽  
Pairs Trading

Portfolio revision and optimal diversification strategy choices

2014 ◽  
Vol 10 (4) ◽  
pp. 537-564
Author(s):  
Mourad Mroua ◽  
Fathi Abid
Keyword(s):  
Transaction Costs ◽  
Tracking Error ◽  
Investment Strategy ◽  
Selection Strategy ◽  
Revision Procedure ◽  
Content Type ◽  
Efficient Frontiers ◽  
Statistical Inferences ◽  
Diversification Strategies ◽  
International Portfolios

Purpose – Since equity markets have a dynamic nature, the purpose of this paper is to investigate the performance of a revision procedure for domestic and international portfolios, and provides an empirical selection strategy for optimal diversification from an American investor's point of view. This paper considers the impact of estimation errors on the optimization processes in financial portfolios. Design/methodology/approach – This paper introduces the concept of portfolio resampling using Monte Carlo method. Statistical inferences methodology is applied to construct the sample acceptance regions and confidence regions for the resampled portfolios needing revision. Tracking error variance minimization (TEVM) problem is used to define the tracking error efficient frontiers (TEEF) referring to Roll (1992). This paper employs a computation method of the periodical after revision return performance level of the dynamic diversification strategies considering the transaction cost. Findings – The main finding is that the global portfolio diversification benefits exist for the domestic investors, in both the mean-variance and tracking error analysis. Through TEEF, the dynamic analysis indicates that domestic dynamic diversification outperforms international major and emerging diversification strategies. Portfolio revision appears to be of no systematic benefit. Depending on the revision of the weights of the assets in the portfolio and the transaction costs, the revision policy can negatively affect the performance of an investment strategy. Considering the transaction costs of portfolios revision, the results of the return performance computation suggest the dominance of the global and the international emerging markets diversification over all other strategies. Finally, an assessment between the return and the cost of the portfolios revision strategy is necessary. Originality/value – The innovation of this paper is to introduce a new concept of the dynamic portfolio management by considering the transaction costs. This paper investigates the performance of a revision procedure for domestic and international portfolios and provides an empirical selection strategy for optimal diversification. The originality of the idea consists on the application of a new statistical inferences methodology to define portfolios needing revision and the use of the TEVM algorithm to define the tracking error dynamic efficient frontiers.

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