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>

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 Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 179
Author(s):  
Karen Balladares ◽  
José Pedro Ramos-Requena ◽  
Juan Evangelista Trinidad-Segovia ◽  
Miguel Angel Sánchez-Granero
Keyword(s):  
Emerging Markets ◽  
Market Efficiency ◽  
Hurst Exponent ◽  
Mean Reversion ◽  
Trading Strategy ◽  
Emerging Countries ◽  
Pairs Trading ◽  
Global Test ◽  
Statistical Arbitrage

In this paper, we use a statistical arbitrage method in different developed and emerging countries to show that the profitability of the strategy is based on the degree of market efficiency. We will show that our strategy is more profitable in emerging ones and in periods with greater uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of 39 countries, and the performance of the strategy is analyzed during the period from 1 January 2000 to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and different portfolios are also considered.

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

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

scholarly journals Some Notes on the Formation of a Pair in Pairs Trading

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 348 ◽  
Author(s):  
José Pedro Ramos-Requena ◽  
Juan Evangelista Trinidad-Segovia ◽  
Miguel Ángel Sánchez-Granero
Keyword(s):  
Hurst Exponent ◽  
Optimal Allocation ◽  
Mean Reversion ◽  
Equal Weight ◽  
Allocation Strategy ◽  
Pairs Trading ◽  
Distance Correlation ◽  
Statistical Arbitrage ◽  
New Methodologies ◽  
Set Up

The main goal of the paper is to introduce different models to calculate the amount of money that must be allocated to each stock in a statistical arbitrage technique known as pairs trading. The traditional allocation strategy is based on an equal weight methodology. However, we will show how, with an optimal allocation, the performance of pairs trading increases significantly. Four methodologies are proposed to set up the optimal allocation. These methodologies are based on distance, correlation, cointegration and Hurst exponent (mean reversion). It is showed that the new methodologies provide an improvement in the obtained results with respect to an equal weighted strategy.

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