More specifically, the reinforcement learning agent chooses the optimum level of parameters of pairs trading to maximize the objective function. Results are obtained by applying a combination of the reinforcement learning method and cointegration approach.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Throughout the years, several trading frameworks and methods have been established in order to optimize this strategy. These methods, in particular the stochastic residual spread method, are mainly based on the more traditional estimation techniques, such as the Expectation Maximization algorithm.
Computational intelligence for financial engineering, Introduction In the past decades, due to the inefficacy of traditional statistical approaches, such as regression-based and factor analysis methods for solving difficult financial problems, the methodologies stemming from computational intelligence, including fuzzy theory, artificial neural networks ANNsupport vector machines SVMand evolutionary algorithms EAhave been developed as more effective alternatives to solving the problems in the financial domain [ 12 ].
An Empirical Study Publication Publication Pairs trading is a quantitative trading strategy that exploits financial markets that are out of equilibrium. Since machine learning techniques are becoming more popular in finance, we propose to develop a framework for pairs trading using neural networks. To date, many existing works along this line of research rely on traditional statistical methods such as the cointegration approach [ 19 ], the Kalman filters [ 2021 ], and the principle component analysis [ 18 ].
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For the first category, earlier research works include the fuzzy multiple attribute decision analysis for portfolio construction [ 9 ]. Although there has been a significant amount of CI-based studies in financial applications, reported CI-based research for pairs trading is sparse and lacks serious analysis.
Traditional decision-making for investment typically relies on fundamentals of companies to assess their value and price their stocks, accordingly. We find that boosting pairs trading specifications by using the proposed approach significantly overperform the previous methods.
The price gap of the two stocks, also known as spread, thus acts as a signal to the open and close positions of the pairs of stocks. Pairs trading [ 17 ] is an important research area of computational finance that typically relies on time series data of stock price for investment, in which stocks are bought and sold in pairs for arbitrage opportunities.
In Section 3we describe the research data used in this study and present the experimental results and discussions. J Stat Theory Pract 4 3: In this paper, using reinforcement learning, we examine the optimum level of pairs trading specifications over time.
J Stat Theory Pract 4 3: Throughout the years, several trading frameworks and methods have been established in order to optimize this strategy.
Is cointegration superior? In this study, we also investigate the robustness of our proposed method and the results show that our method is indeed effective in generating robust models for the dynamic environment of the pairs-trading problem. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Industrial electronics, In this paper, we present a novel methodology for pairs trading using genetic algorithms GA.
As the true values of the stocks are rarely known, pairs-trading techniques were developed in order to resolve this by investing stock pairs with similar characteristics e. Other intelligent methods, such as genetically evolved regression models [ 15 ] and inductive fuzzy inference systems [ 16 ], were also available in the literature.
In contrast to the statistical approaches, recent advances in computational intelligence CI are leading to promising opportunities for solving problems in the financial applications more effectively. J Comput Appl Math Lai et al.
Abstract Pairs trading is an important and challenging pairs trading strategy optimization using the reinforcement learning method a cointegration approach area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Materials and Methods In this section, we provide the relevant background and descriptions for the design of our pairs-trading systems using the GA for model optimization.
In that approach, the authors used the nondominated sorting to search for nondominated solutions and showed that the multiobjective method outperformed the single-objective version proposed by Huang [ 5 ]. Rev Financ Stud 19 3: Rahib H.
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J Econ Dyn Control 37 To improve the performance of the single-objective GA-based models, more recently, Chen et al. Another popular study of computational intelligence has been particularly concerning the prediction of financial time series.
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Now consider the two price time series, and. Chapados and Bengio [ 11 ] trained neural networks for estimation and prediction of asset behavior to facilitate decision-making in asset allocation. Therefore, the optimization of pairs trading strategy has gained widespread attention forex ghani high-frequency traders.
As a result, the trading model is usually market-neutral in the sense that it is uncorrelated with the market and may produce a low-volatility investment strategy. Econometrica 55 2: This paper instaforex trader calculator organized into four sections.
Section 2 outlines the method proposed in our study. Among the CI-based techniques studied for finance, the models may be classified as two major areas of applications: This thesis analyzes the performance of neural networks in pairs trading applied to Exchange Traded Funds ETFs both statistically and economically, and compares the performance with the more traditional methods.
J Econ Dyn Control 12 2: Expert Syst Appl 38 5: Appl Econ 47 6: Section 4 concludes this paper. ISIE Quant Finance 1— Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied.
In Lai et al. A typical form of pairs trading of stocks operates by selling the stock with a relatively high price and buying the other with a relatively low price at the inception of the trading period, expecting that the higher one will decline while the lower one will rise in the future.
Computational Intelligence and Neuroscience
During the trading period, position is opened when the spread widens by a certain threshold, and thereafter the positions are closed when spread of the stocks reverts. In EA applications along this line of research, Becker et al.
Saks and Maringer [ 22 ] used genetic programming for various pairs of stocks in Eurostoxx 50 equities and also found good pair-trading strategies. In addition, in these previous studies, the trading models were constructed using only two stocks as a trading pair; here, we propose a generalized approach that uses more than two stocks as a trading group for arbitrage in order to further improve the performance of the models.
Erasmus University Thesis Repository: Pairs Trading Using Machine Learning: An Empirical Study
Motivated by this research work, we thus intend to employ the GA to optimize our intelligent system for pairs trading, and the experimental results will show that our proposed GA-based methodology is promising in outperforming the benchmark.
Quant Finance 14 Zargham and Sayeh [ 10 ] employed a fuzzy rule-based system to evaluate a set of stocks for the same task. In the CI area, Thomaidis et al. Pairs Trading Using Machine Learning: J Deriv Cornflower forex strategy Funds 15 2: Notes Compliance with ethical cornflower forex strategy Conflict of interest The authors declare that there is no conflict of interests regarding the publication of this article.
Furthermore, in contrast to traditional pairs-trading methods that aim at matching pairs of stocks with similar characteristics, we also show that our method is able to construct working trading models for stocks with different characteristics. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression.
A certain amount of research employs network learning techniques, including feed-forward, radial basis function or recurrent NN [ 7 ], and SVM [ 8 ]. The objective of this long-short strategy is to profit from the movement of the spread that is expected to revert to its long-term mean.
By identifying a pair of stocks that historically move together, and assuming that their price difference is mean-reverting, an investor can profit from deviations from the mean by taking a long-short position in the chosen pair.
Consider initial capitalwith an interest rate of per annum and a frequency of compounding in a year; the capital after a year may be expressed as If the frequency of compounding gets arbitrarily large, we have In the case of continuously compounded return, the process of capital growth is defined as Therefore, the continuously compounded rate is calculated by taking the natural logarithm as follows: This mutual mispricing between two stocks is theoretically formulated by the notion of spread, which is used to identify the relative positions when an inefficient market results in the mispricing of stocks [ 1821 ].
Although there exist these previous CI-based studies for pairs trading, they lacked serious analysis such as the method of temporal validation used in [ 523 ] for further evaluation of the robustness of the trading systems. In this study, we also employ the GA for the optimization problems in our proposed arbitrage models. Comput Intell Neurosci Empir Econ Lett 8 5: Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
Phys A J Econ 16 1: In a past study [ 23 ], Huang et al. This is a preview of subscription content, log in to check access.