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

Fallahpour, Saeid; Hakimian, Hasan; Taheri, Khalil; Ramezanifar, Ehsan

doi:10.1007/s00500-016-2298-4

Cited by 18 publications

(25 citation statements)

References 20 publications

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“…e Cointegration Test. There are many approaches for pair selection such as the discrete approach [11,[25][26][27], the cointegration approach [10,16,27], and the stochastic approach [7,8]. In this study, we use the cointegration approach to choose pairs which have long-term equilibrium.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…However, as many investors including hedge funds sought these arbitrage opportunities by executing the pairs-trading strategy, its profitability began to deteriorate [5,6]. To overcome these shortcomings, significant research has been conducted to improve the pairs-trading strategy [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…They used OLS regression to create spreads from the pairs. Furthermore, [10] applied a cointegration test to assorted pairs of stocks and a vector error-correction model to create a trading signal.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the action-augmentation technique yielded more profit than an epsilon-greedy policy. Reference [10] used an N-armed bandit problem to optimize the pairs-trading strategy. They took the spread using an error-correction model and found the parameters using a grid-search algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Pairs‐Trading Strategy Using Deep Reinforcement Learning with Trading and Stop‐Loss Boundaries

Kim

2019

Complexity

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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.

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Section: Technical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing the Pairs‐Trading Strategy Using Deep Reinforcement Learning with Trading and Stop‐Loss Boundaries

Kim

2019

Complexity

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“…This method is simple, but it lacks rigorous theoretical arguments and is easily biased, thus affecting the empirical results of arbitrage. Some studies have also proposed improvements to apply the statistical arbitrage model to empirical research on crush arbitrage [4][5][6][7][8]. However, most of the theoretical premise of the study is that the cointegration regression residual obeys the normal distribution.…”

Section: Introductionmentioning

confidence: 99%

The Profitability of Crush Spread: Statistical Arbitrage Method

Gu¹,

Lei²

2018

Proceedings of the 2018 6th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2018)

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This paper examines the crush arbitrage of soybean and rapeseed futures, which is based on inter-commodity arbitrage in the oil processing process. The paper takes soybean, soybean meal and soybean oil futures listed on the Dalian Commodity Exchange, rapeseed, rapeseed meal and rapeseed oil futures listed on the Zhengzhou Commodity Exchange as research objects. Based on the statistical arbitrage model, empirical studies on two kinds of crush arbitrage are conducted. The improvement of this paper is to examine the characteristics of price volatility from the perspective of time-varying fluctuations, and then improve the setting of trading thresholds. The empirical results show that the use of GARCH model to solve the time-varying variance and set the feasibility of the threshold improvement, especially, rapeseed crushing arbitrage strategy portfolio returns better.

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Many-to-Many Pair Trading

Wang

et al. 2023

Lecture Notes in Computer Science

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

Cited by 18 publications

References 20 publications

Optimizing the Pairs‐Trading Strategy Using Deep Reinforcement Learning with Trading and Stop‐Loss Boundaries

Optimizing the Pairs‐Trading Strategy Using Deep Reinforcement Learning with Trading and Stop‐Loss Boundaries

The Profitability of Crush Spread: Statistical Arbitrage Method

Many-to-Many Pair Trading

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