Execution of Pairs Trading Strategy: Some Propositions

2015 ◽  
Author(s):  
Tamal Datta Chaudhuri ◽  
Priyam Singh
Keyword(s):  
Trading Strategy ◽  
Pairs Trading

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 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 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.

Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach

2016 ◽  
Vol 20 (12) ◽  
pp. 5051-5066 ◽  
Author(s):  
Saeid Fallahpour ◽  
Hasan Hakimian ◽  
Khalil Taheri ◽  
Ehsan Ramezanifar
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Learning Method ◽  
Pairs Trading

Pairs Trading with Opportunity Cost

2014 ◽  
Vol 51 (01) ◽  
pp. 282-286 ◽  
Author(s):  
Carl Lindberg
Keyword(s):  
Opportunity Cost ◽  
Trading Strategy ◽  
The Other ◽  
Recent History ◽  
Investment Opportunity ◽  
Financial Industry ◽  
Pairs Trading ◽  
Uhlenbeck Process ◽  
Optimal Liquidation ◽  
Ornstein Uhlenbeck Process

Pairs trading is a trading strategy which is used very frequently in the financial industry. An investment opportunity arises when the spread between two assets, which historically have exhibited autoregressive behavior, deviates from its recent history. In this case, the investor takes a long position in the asset which is expected to outperform going forward and finances this by taking a short position in the other one. If the spread converges, the investor can close both positions to generate a profit. We model the spread between two assets as an Ornstein-Uhlenbeck process and assume a constant opportunity cost. We then study the optimal liquidation strategy for an investor who wants to optimize profit in excess of the opportunity cost. Including this cost is important from an applied perspective, as the performance of any investment is always evaluated relative to the performance of the opportunity set.

scholarly journals Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Masood Tadi ◽  
Irina Kortchemski
Keyword(s):  
Trading Strategy ◽  
Significant Finding ◽  
Pairs Trading ◽  
Content Type ◽  
Arbitrage Opportunities ◽  
Cointegration Tests ◽  
Look Back ◽  
Trading Period ◽  
Asset Selection ◽  
Binned Data

Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

scholarly journals Do momentum and reversal strategies work in commodity futures? A comprehensive study

2020 ◽  
Vol 12 (4) ◽  
pp. 375-409 ◽  
Author(s):  
Hanxiong Zhang ◽  
Andrew Urquhart
Keyword(s):  
Futures Market ◽  
Trading Strategy ◽  
Commodity Futures ◽  
High Momentum ◽  
Pairs Trading ◽  
Momentum Strategy ◽  
Partial Explanation ◽  
Content Type ◽  
Momentum Profits ◽  
Significant Reversal

PurposeMotivated by the debate on the patterns and sources of commodity futures returns, this paper investigates the performance of three investment trading strategies, namely, the momentum strategy of Jegadeesh and Titman (1993), the 52-week high momentum strategy of George and Hwang (2004) and the pairs trading strategy of Gatev et al. (2006) in the commodity futures market.Design/methodology/approachThe three strategies are those given by Jegadeesh and Titman (1993), George and Hwang (2004) and Gatev et al. (2006), respectively.FindingsThe authors find that there is no significant reversal profit across 189 formation-holding windows for all the three strategies. However, there are statistical and economically significant momentum profits, and the profitability increases with the rising of formation-holding periods. Momentum returns are quite sensitive to market conditions but the crash of momentum returns is partly predictable. Return seasonality, risk and herding also provide partial explanation of the momentum profits.Originality/valueThe authors are the first to compare the performances of the pairs trading strategy of Gatev et al. (2006), the conventional momentum of Jegadeesh and Titman (1993), and the 52-week high momentum of George and Hwang (2004) under 189 formation-holding windows. Also, the authors are the first to investigate the association between herding behaviour and momentum returns in the commodity futures market.

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