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This paper develops a pairs trading strategy via unsupervised learning. Unlike conventional pairs trading strategies that identify pairs based on return time series, we identify pairs by incorporating firm characteristics as well as price information. Firm characteristics are revealed to provide important information for pair identification and significantly improve the performance of the pairs trading strategy. Applied to the US stock market from January 1980 to December 2020, the long-short portfolio constructed via the agglomerative clustering earns a statistically significant annualized mean return of 24.8% and a Sharpe ratio of 2.69. The strategy remains profitable after accounting for transaction costs and removing stocks below 20% NYSE-size quantile. A host of robustness tests confirm that the results are not driven by data snooping.

Among the various statistical trading strategies, pairs trading has been widely employed as a market neutral strategy owing to its simple approach and ease of application. In this context, we develop a cointegration-based pairs trading framework with a set of pre-conditions for pair eligibility and apply it to different asset classes. The performance analysis of a portfolio of 45 pairs is considered for the period of January 2007 to January 2021, which covers the period of a full market cycle of adjacent bull and bear periods; it is studied and benchmarked against the SP we observe that this performance does not vary significantly with a change in the transaction cost levels and does not pass below the risk-free return levels with changing market conditions. Further, the strategy is observed to perform better during bear market conditions. Considering the highly liquid trading environment of the strategy, our findings raise a call for a discussion on the semi-strong form market efficiency.

We investigate whether spurious pairs created by multiple hypothesis testing can be minimized when firm characteristics are used to identify pairs. The results show that the portfolios of pairs that have higher fundamental similarity outperform those that are fundamentally less similar by minimizing the non-convergence risk. The cost of trading spurious pairs is significant despite the empirical results that the profitability of pairs trading has continued to decline since 2003 and is not significant any more. Accounting information plays a crucial role in identifying pairs rather than market trading data, and the importance of firm fundamentals in pairs trading increases during market crises.

Pairs trading is a statistical arbitrage strategy that takes advantage of unbalanced financial markets. A common difficulty for quantitative trading participants is the detection of market institutional changes in financial markets. In order to solve this issue, the hidden Markov model (HMM) is applied for status detection. The research objective is to use Kalman filter to predict and the hidden Markov model (HMM) to identify state transitions on the basis of screening transaction pairs with obvious co-integration relationship. This research would prove the profitability of the strategy and the ability to resist risk through the combination of these two methods with real data. The empirical results showed that compared with the traditional cointegration strategy, the holding yield increased from 1.6% to 16.2% and the maximum pullback reduced to 0.02%. Further research is required to improve trading rules.

We examine a discrete–time optimal pair–trade execution problem with generalized cross–impact. This research is an extension of Fukasawa et al. (2020b), which considers the price impact of aggregate random orders posed by small traders with a Markovian dependence. We focus on how a risk–averse large trader optimally executes two correlated assets to maximize his/her expected utility from the terminal wealth over a finite horizon. A Markov decision process modeling constitutes the basis for the formulation of the optimal pair–trade execution problem. Then, under some regularity conditions, the backward induction method of dynamic programming enables us to derive the optimal pair–trade execution strategy and its associated optimal value function. The trading orders of each risky asset posed by small traders do affect the optimal execution volume of both risky assets. Moreover, numerical results with simulation experiments show that the cross–impact affects the optimal execution strategy and a round–trip trade exists for the large trader to utilize a ‘statistical’ arbitrage and to increase his/her expected utility under our model setting of cross–impact.

Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.

The objective of this study was to understand how the shares’ volatility affects the portfolios’ dynamics formed using the model of pairs trading in the Brazilian stock market. This article distinguished itself by bringing new evidence about the effects of volatility in the pairs trading model not covered by previous studies, expanding the sample size analyzed in the Brazilian stock market. The chosen theme’s relevance is that investors can use pairs trading or long-short models to build their portfolios. The use of cointegration concepts probabilistically contributes to portfolios’ formation weakly correlated to the market indexes with superior performance. This article impacts the area by contributing new evidence for better use of the model in the analysis of investments. From January 2016 to December 2018, the 90 most liquid assets of Bolsa, Brasil, Balcão (B3) were analyzed, totaling 5,927,400 possible pairs. The Augmented Dickey-Fuller test and subsequent backtesting of the pairs in the proposed period were used to evaluate the cointegration criteria. Statistical analysis was performed by parametric and non-parametric tests and Pearson and Spearman correlation analyses. The results found indicated that the formation of portfolios by pairs trading with dependent assets with the criterion of higher levels of volatility (20 periods) presented a superior performance. These findings can be justified by a better risk and return ratio for the portfolio, measured by the Sharpe Index of the returns obtained concerning the portfolio’s volatility, compared to a portfolio formation based on a random selection of the pairs. In addition, the results also showed a low correlation of returns concerning the market index. Therefore, the application of the statistical cointegration analysis methodology alone does not guarantee results that are different from the market average. RESUMO O objetivo do trabalho foi compreender de que forma a volatilidade das ações afetam a dinâmica dos portfólios formados com uso do modelo de arbitragem por pares ou pairs trading no mercado acionário brasileiro. Este artigo diferenciou-se por trazer novas evidências acerca dos efeitos da volatilidade no modelo de pairs trading não abrangidos por estudos anteriores, ampliando o tamanho da amostra analisada no mercado acionário brasileiro. A relevância do tema escolhido reside no fato de modelos de arbitragem por pairs trading ou long-short serem utilizados por investidores para construção de suas carteiras. A utilização dos conceitos de cointegração contribui probabilisticamente para a formação de portfólios fracamente correlacionados aos índices de mercado com desempenho superior. Este artigo tem impacto na área por contribuir com novas evidências para uma melhor utilização do modelo na análise de investimentos. Analisou-se, no período de janeiro de 2016 até dezembro de 2018, os 90 ativos mais líquidos da Bolsa, Brasil, Balcão (B3), totalizando 5.927.400 pares possíveis. Para avaliação dos critérios de cointegração utilizou-se o teste Dickey-Fuller Aumentado e posterior backtesting dos pares no período proposto. A análise estatística foi realizada por testes paramétricos e não paramétricos e análises de correlações de Pearson e Spearman. Os resultados encontrados indicaram que a formação de carteiras por pairs trading com ativos dependentes com critério de maiores níveis de volatilidade (20 períodos) apresentaram um desempenho superior. Esses achados podem ser justificados por uma melhor relação de risco e retorno para a carteira, mensurada pelo Índice de Sharpe dos retornos obtidos em relação à volatilidade da carteira, quando comparada a uma formação das carteiras baseada em uma seleção aleatória dos pares. Além disso, os resultados também apresentaram baixa correlação dos retornos em relação ao índice de mercado. Constatou-se, assim, que apenas a aplicação da metodologia de análise por cointegração estatística não garante resultados diferenciados da média de mercado.

We consider a pairs trading stochastic control problem with transaction costs and constraints on the gross market exposure, and propose a new monotone Finite Difference scheme approximating the viscosity solution of the Hamilton–Jacobi–Bellman equation characterizing the optimal trading strategies. Given a fixed time horizon and a portfolio of two cointegrated assets, the agent trades the spread between the two assets and the trading strategy is defined as the possibly negative portfolio weight maximizing the expected exponential utility derived from terminal wealth. Furthermore, trades incur transaction costs comprised of explicit transactions fees and commissions and the implicit cost due to slippage. These costs are modeled as a linear or square root function of the trading rate and respectively added or subtracted from the observable asset price at the time when a buy or a sell order enters the market. Our main contribution is the derivation of a robust approximation for the nonlinear transaction cost term in the Hamilton–Jacobi–Bellman equation. Finally, we combine our monotone Finite Difference scheme with a Monte Carlo sampling method to analyze the effects of transaction fees and slippage on the trading policies’ performance.

Finding Bertram's optimal trading strategy for a pair of cointegrated assets following the Ornstein--Uhlenbeck price difference process can be formulated as an unconstrained convex optimization problem for maximization of expected profit per unit of time. This model is generalized to the form where the riskiness of profit, measured by its per-time-unit volatility, is controlled (e.g. in case of existence of limits on riskiness of trading strategies imposed by regulatory bodies). The resulting optimization problem need not be convex. In spite of this undesirable fact, it is demonstrated that the problem is still efficiently solvable. In addition, the problem that parameters of the price difference process are never known exactly and are imprecisely estimated from an observed finite sample is investigated (recalling that this problem is critical for practice). It is shown how the imprecision affects the optimal trading strategy by quantification of the loss caused by the imprecise estimate compared to a theoretical trader knowing the parameters exactly. The main results focus on the geometric and optimization-theoretic viewpoint of the risk-bounded trading strategy and the imprecision resulting from the statistical estimates.

Pairs trading is a solidly profitable strategy in the algorithmic trading area, and an important step of this strategy is selecting pairs of stocks. Compared with other existing pairs selection approaches, the Engle-Granger cointegration is more stable and reliable. Nowadays, as trading is becoming faster and faster in stock markets all over the world, it is necessary to accelerate the pairs selection process to increase potential profits. However, intensive computations and complicated data flow in the cointegration approach bring challenges to hardware acceleration. In this paper, for the first time, we propose an efficient and hardware-friendly computing scheme to accelerate the Engle-Granger cointegration. Besides, a novel algorithmic strength reduction strategy and approximation methods are used to significantly reduce the complexity of the proposed scheme. Based on the improved algorithm, both FPGA and ASIC accelerators are developed. The implementation results show that our FPGA and ASIC accelerators perform $36\times $ and $207\times $ faster than GPU, respectively. Thus, our design can significantly reduce the latency of the pairs selection process, and make more profit for investors and traders.

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases.

In recent years, there has been an explosion of research in metaheuristics, which provides efficient solutions that are close to optimal with lower computing times. Applying metaheuristics to finance is reasonable given that many financial decisions must be made within very short time frames, minutes or even seconds such as in the case of High Frequency Trading. In this paper, an algorithm based on Ant Colony Optimization metaheuristics is proposed to dynamically optimize the decision thresholds provided by the Pairs Trading investment strategy.The proposed algorithm is called the Ant Colony Optimization of Pairs Trading (ACO-PT) and is optimized by moving training-trading windows.The model is applied to Forex data at a high frequency, consisting of 38 Foreign Exchanges with a frequency of 15 min from September 22, 2017 until July 6, 2018. It is shown that ACO-PT can be used in deep markets efficiently and is capable of obtaining daily returns of 0.1204 $$\%$$ and a Sharpe ratio of 0.6520, which translates into an improvement over the base case for fixed thresholds of 13.21 $$\%$$ . We conclude statistically that the variation of the algorithm that showed the best performance was also the simplest variation and, therefore, the fastest.

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.

Early forms of statistical arbitrage exploited the mean reversion of a model error extracted from pairs of instruments with a tendency to move together. Pairs trading was extended by Engle and Gran...

We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed.

This study examines pairs trading using a general state space model framework. It models the spread between the prices of two assets as an unobservable state variable assuming that it follows a mea...

Purpose This study aims to validate the “expectancy theory” of asset pricing and explores the price discovery process in metals futures markets. Design/methodology/approach This paper adopts the Johansen cointegration and vector error correction model approach to investigate the potentials of Pairs trading in the metals market during the period 2008–2019. Findings The results find the price movements in metal markets are not random walk and the current “futures” prices are the reasonable estimate of the “spot” metal prices in future. This study does not notice any significant differences in the price efficiency across metals markets, which signal the effects of limited idiosyncratic forces in price transmission. Practical implications The research suggests the covert use of metal futures to make gains from arbitrage trading. Originality/value The study emphasizes the potential of “pair trading” in commodity market context that is seldom discussed in academic papers.

In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.

Very few studies have explored the structure of optimal switching regimes. We extend the existing research on the infinite-horizon multiple-regime switching problem with an arbitrary number of switch options by replacing the linear running reward function with a quadratic function in the objective function. To make our analysis more rigorous, we establish the theoretical basis for the application of the simultaneous multiple-regime switches to the problem with the extended objective function, and provide the sufficient condition under which each certain separated region in the space includes, at most, one single connected optimal switching region, which determines the structure of the optimal switching regions, and we identify the structure of the optimal switching regions for the particular problem.

Market practitioners and speculators attempt to make benefits from the existence of market price gaps and profit opportunities by arbitrage strategies Although some investors trade stocks based on the available financial and fundamental information of a particular share, there are others who make profits by risk hedging and swing trading opportunities One of these strategies is pairs trading, which is a sub-category of statistical arbitrage Pairs trading can assure reasonably a risk-free profit gaining This paper aims to make a hypothetical portfolio composed of pairs of stocks by exploring a significant association between their prices in the Toronto Stock Exchange, TSX We compare the profitability of distance, co-integration, and copula functions as the pair's selection and trading strategy devices in TSX over January 2017 to June 2020 Our results show that the highest profitability comes from trading by the copula method Our time frame includes two heterogeneous pre and post COVID-19 periods Although the financial markets are struggling with a hard situation over the COVID-19 days, the performance of the methodologies is not affected by the crisis © 2021 John Wiley & Sons, Ltd

We investigate the profitability of a pairs trading strategy using Chinese and international commodity futures contracts covering the period January 2004 to February 2018. We use a time-series approach where the commodity pairs share a similar underlying. An out-of-sample test is employed to infer the performance of these pairs, allowing us to determine the optimal open and close positions for the pairs trading strategy. Applying this strategy to a portfolio of commodities yields an excess return of 2.08% per annum and a Sharpe ratio of 0.79. For a portfolio of metal futures, this strategy yields 5.32% excess returns and a Sharpe ratio of 1.47, whereas for gold-only futures, this strategy yields 7.39% excess returns and 1.95 Sharpe ratio. This performance is superior to traditional strategies based on term structure, momentum, and value portfolios. Arbitrage opportunities in these commodity pairs remain even after accounting for transaction costs and are robust to data-snooping bias.

We uncover idiosyncratic cash flow risk as a dominant driver for pairs trading performance. The convergence probability and pairs payoff are negatively associated with pairwise idiosyncratic cash flow volatility. Further, pairs portfolio returns load negatively on market-wide idiosyncratic cash flow volatility. This latter time-series evidence helps explain a substantial part of the decline in pairs trading profitability in the US equity market since the 1990s. Our results are consistent with idiosyncratic risk representing a major holding cost for arbitrageurs when substitutes are close but imperfect.

This paper analyses the profitability of pairs trading strategy in Latin American stock markets through a PCA approach with a multi‐factorial model. We propose two criteria for selecting the optimal thresholds by using moving training‐trading windows. By applying the methodology in six countries with 338 stocks in total for the period 2013–2017, we found that this strategy outperforms the markets' Sharpe ratio by 1.55 points on average. In addition, by using a correlation matrix, we found that the largest eigenvalue is clearly dominant and that its associated eigenportfolio presents a co‐movement with the market while the number of dominant components is contrary to market volatility.

Statistical arbitrage is a trading strategy that employs time series methods to identify relative mispricing between securities based on the expected values of these assets. The Pairs Trading, one of the techniques of statistical arbitrage, is a market neutral trading strategy. The main objective of this paper is to investigate the profitability and risks of pairs trading strategy for various stocks. The daily future prices of stocks traded and listed on NSE over 2011–2017 are used on rolling basis to compute the performance based on the selection of pairs through minimizing the sum of squared deviation (distance method) and the selection based on cointegration tests (cointegration method) for identifying stocks suited for pairs trading strategies. The pairs trading strategy is performed in two stages: the formation period and the trading period. The strategy is created by long position in one stock and short position in other stock of the pair identified. To examine the risk of pairs trading and the drivers of returns, the portfolio returns are risk-adjusted using Fama and French (J Financ Econ 33:3–56, 1993) three factor asset pricing model. The study reveals that pairs trading in related stocks is significantly profitable with average annualized profitability of up to 34% including transaction costs. The evidence of pairs trading profits in stock futures supports the view that these profits reflect compensation to arbitrageurs for enforcing the law of one price in similarly related markets to ensure market efficiency. Indian financial markets are maturing and are attracting sizable retail and institutional investments. Advanced applications like the one presented in this study are of significance for the investors and investment consultants so that they can benefit from such trading strategies.

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.

Statistical arbitrage refers to a suite of quantitative investment strategies employed chiefly by hedge funds and proprietary trading firms. The arbitrageur can draw on a number of different approaches to identify and exploit an arbitrage opportunity, though the literature is broadly segmented by the canonical distance, cointegration and time series perspectives. Since the initial academic investigation of statistical arbitrage, its profitability has continued to diminish thanks largely to the increasing proportion of non-convergent opportunities. This paper surveys the existing literature, with particular emphasis given to evidence of statistical arbitrage failure, before unifying the distance, cointegration and time series perspectives under a single explicit model. The failure of statistical arbitrage opportunities is shown to be the direct consequence of implicit model assumptions that are inconsistent with the empirical literature. An alternative model is proposed, and evidence of its relative performance discussed.

This paper studies the performance of pairs trading strategy under a specific spread model. Based on the empirical evidence of mean reversion and jumps in the spread between pairs of stocks, we ass...

In this paper, we study the classical problem of the first hitting time density to a moving boundary for a diffusion process, which satisfies the Cherkasov condition, and hence, can be reduced to a...

We investigate the optimal investment among the money market account, a liquid risky asset (e.g. stock index) and an illiquid risky asset (e.g. individual stock), where the two risky assets are cointegrated. The illiquid risky asset is subject to a proportional transaction cost and the portfolio of the three assets faces certain position limits. We develop the optimal investment strategy to maximize the gain function, which is realized through an expected sum of discounted utilities given transaction costs and position limits. The problem formulation uses a singular control framework with cointegration that determines optimal trading boundaries among holding, selling and no-trading regions. We conduct comprehensive numerical analysis on the optimal investment strategy and features of the optimal trading boundaries given various levels of position limits.

The statistical arbitrage strategy is one of the most traditional investment strategies. There are many theoretical and empirical studies until now. However, almost all of the statistical arbitrage strategies focus on the price difference (spread) between two similar assets in the same asset class and exploit the mean reversion of spreads, i.e. pairs trading. In this study, we extend the strategy to multiple assets in the multi-asset market. Although mean-reverting portfolios were derived based on a single criterion in related researches, we derive a mean-reverting portfolio by optimizing multiple mean-reversion criteria. We expect that a mean-reverting portfolio based on multiple indicators leads to a higher return/risk. We perform an empirical analysis in multi-asset market and show the profitability of our strategy.

We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD $6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable.

We investigated a comprehensive analysis of the mutual exciting mechanism for the dynamic of stock price trends. A multi-dimensional Hawkes-model-based approach was proposed to capture the mutual exciting activities, which take the form of point processes induced by dual moving average crossovers. We first performed statistical measurements for the crossover event sequence, introducing the distribution of the inter-event times of dual moving average crossovers and the correlations of local variation (LV), which is often used in spike train analysis. It was demonstrated that the crossover dynamics in most stock sectors are generally more regular than a standard Poisson process, and the correlation between variations is ubiquitous. In this sense, the proposed model allowed us to identify some asymmetric cross-excitations, and a mutually exciting structure of stock sectors could be characterized by mutual excitation correlations obtained from the kernel matrix of our model. Using simulations, we were able to substantiate that a burst of the dual moving average crossovers in one sector increases the intensity of burst both in the same sector (self-excitation) as well as in other sectors (cross-excitation), generating episodes of highly clustered burst across the market. Furthermore, based on our finding, an algorithmic pair trading strategy was developed and backtesting results on real market data showed that the mutual excitation mechanism might be profitable for stock trading.

This research 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. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate 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. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement 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. 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, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take 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., 28 pages with 7 figures and 6 tables. Studies in Economics and Finance, Vol. ahead-of-print No. ahead-of-print (2021)

In the light of current boom in the stock market and deviation from the fundamentals, every investor is worried about formation of a bubble and about the dire consequences that will follow when the bubble bursts. One solution is to avoid capital markets and park money in safe instruments like bank fixed deposits. However, given the low interest rate environment we are in, this option would not be financially savvy. Secondly, one can opt for portfolio management services but that would incur heavy management fees depending on the type of fund one chooses. Efficient management of wealth is very crucial in order to fulfil one's financial goals. All strategies of trading require a significant amount of time and research to determine a trade that would be worth the risk. Sometimes, even after spending hours on research, one might not be able to find an opportunity with an optimum risk-reward ratio. However, it might not be possible for working professionals to devote so much time every day to identify the right trades. With a view to solving this problem, we have built a model using reinforcement learning which will provide users with buy-sell signals based on the theme of pairs trading. Pairs Trading is a relatively safer strategy that tries to create a market neutral position, thereby providing a hedge to some extent against violent market movements.

Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy (SAPT), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.

The development of algorithmic trading has been one of the most prominent trends in finance and its applications. Hidden Markov Models (HMMs) help enhance the predictive power of statistical models and improve trading strategies for data scientists and algorithmic traders. In recent years there has been growing interest in investigating the pairs trading and multiple trading based on robust Kalman filtering (KF) using data-driven innovation volatility forecasts (DDIVF). KF algorithms were successfully applied in pairs trading with two cointegrated assets using DDIVF as a method for forecasting non-normal innovation volatility. In this paper a novel combined pairwise trading strategy is proposed by combining HMM and DDIVF to further optimize trading signals in different market regimes. The results of the numerical experiments on two cointegrated stocks show that the proposed profitable trading strategy using DDIVF-HMM outperforms the recently studied robust trading strategy using DDIVF alone.

Algorithmic trading uses a computer program that follows a defined set of instructions (an algorithm) to place a trade and can generate profits at a speed and frequency that is impossible for a human trader. Current state-of-the-art in algorithmic trading uses Kalman filtering (KF) and maximum informative resilient filtering (MIRF) that allow traders to enhance the predictive power of statistical models and improve trading strategies. There has been a growing interest in using MIRF in pairs trading, and a major drawback is that a threshold value (assumed to be one) is selected in an ad hoc way rather than maximizing the Sharpe ratio. In this paper, a novel random weights innovation volatility forecasting (RWIVF) algorithm is introduced to obtain the optimal data-driven weights of past observed volatilities instead of the equal weights by extending the Bollinger bands algo-tradinng strategy. RWIVF algorithm is also compared with the commonly used KF algorithm. Autocorrelations of the absolute values of the innovations in multiple trading are used to demonstrate that the innovations are non-normal with time-varying volatility. Performance of the RWIVF algorithm is shown using the experiments on cointegrated exchange-traded funds (ETFs). Analyses also explain how our approach can improve the performance of the trading strategies. The proposed novel resilient (robustness to initial values) trading strategies to the volatile stock market are also discussed in some detail.

In this article, the authors examine fundamental linkages between geolocation (human movement) data and financial market equity price behavior. The geolocation positions were recorded intraday and cover the period from January 1, 2018 to July 17, 2018. The authors concentrate their study on a popular hedge fund trading strategy known as pairs trading. First, the authors collect data regarding Under Armour (UA) and Nike’s stock market price and volume. Second, they investigate the volume of people visiting each physical store of UA and Nike, as proxied from anonymous cell phone geolocation traffic. Third, they monitor the relative activities for tweets related to UA and Nike. Fourth, after combining all the data, the authors glean the following fascinating results: (1) Geolocation information is proven to be an important factor in a pairs trading strategy between UA and Nike, according to the results of feature selection and prediction accuracy for price ratio change; (2) both ensemble methods of machine learning and rolling analysis could significantly raise the prediction accuracy; and (3) a pairs trading strategy incorporating geolocation information can have a cumulative return of 13.72% from January 2018 to June 2018, with an annualized Sharpe ratio of 3.88. TOPICS:Statistical methods, simulations, big data/machine learning Key Findings • Geolocation data can encapsulate the relative influx and outflux of consumers from department stores. This is an extremely useful feature in training machine learning models that can be used in lucrative pairs-trading strategies. • In the era of big data, it is tempting to throw every feature into a model and assume that the model will always figure out which features are most important. However, the model will usually perform better when trained on a subset of distinct features. • Ensemble methods are an effective means of eliminating bias in machine learning models because they use the output from a series of independently trained submodels to generate one balanced result.

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.

Forming combinations of comoving assets is a critical step in pairs trading that has only been addressed either manually or through enumerative procedures. Both approaches fail in the multivariate case and do not consider conflicting objectives in the problem structure. This paper is the first attempt to address these novel problems by presenting an intelligent system that recommends profitable pair combinations through a Mixed Integer Programming (MIP) formulation and solving the NP-Hard optimization problem with a multi-objective genetic algorithm (NSGA-II) containing problem specific modifications. Combinations of assets are optimized on two conflicting objectives of risk (mean-reversion) and return (spread variance) to form sets of profitable multivariate pairs trading opportunities. Promising results support the superiority of multi-objective and multivariate pairs trading strategies over their traditional single objective and univariate counterparts. The findings should motivate new directions for pairs trading research and also expand the applications of evolutionary multi-objective optimization for hard problems in finance and other industries.

Financial pricing and prediction of stock markets is a specific and relatively narrow field, which have been mainly explored by mathematicians, economists and financial engineers. Prediction with the purpose of making profits in a martingale domain is a hard task. Pairs trading, a market neutral arbitrage strategy, attempts to resolve the drawback of unpredictability and yield market independent returns using relative pricing idea. If two securities have similar characteristics, so should their prices. Deviation from the acceptable similarity range in prices is considered an anomaly, and whenever noticed, trading is executed assuming the anomaly will correct itself.This work proposes a fuzzy inference model for the market-neutral pairs trading strategy. Fuzzy logic lets mimicking human decision-making in a complex trading environment and taking advantage of arbitrage opportunities that the crisp models may miss to acquire for the trade decision-making. Spread between two co-integrated stocks and volatility of the spread is used as decision-making inputs. Spread is a measure of the distance between two stocks and volatility is an indicator of how soon the spread would disappear. We conclude that fuzzy engine contributes to the profitability and efficiency of pairs trading type of strategies.

This study investigates the extent of time-varying volatility and correlations between crude oil, natural gas, and stock prices in India using various multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) models with and without asymmetry. Our empirical results reveal that there is no long-run cointegration between crude oil, natural gas, and stock prices in India. We find that the VARMA-DCC-GARCH model is more efficient compared to the CCC model with asymmetry in estimating time-varying correlations. We also analyze optimal portfolio weights and hedging ratios through pair trading between stocks and energy commodity futures. Our results have several implications for portfolio investors dealing with the Indian stock market and energy commodity futures for forecasting potential market risk exposure and determining the existence of portfolio diversification benefits Refereed/Peer-reviewed

We build CPS-index (Common Preferred Spread Index) using the spread return between common and preferred stock pairs, and show that CPS-index has explanatory power for long term market return. Common stocks are more sensitive to the market condition than preferred stocks so that CPS-index tends to oscillate according to market condition. We observe that the future realized market return becomes high when CPS-index is low and vice versa. There is an inverse relationship between CPS-index and the future market return of S&P500 index. The statistical analysis of the regression between CPS-index and future market return shows that CPS-index has a significant power to explain the future realized market return in 21 months or up to 48 months ahead of time. Multivariate regression analysis confirms that the inclusion of CPS-index as an explanatory variable enhances the market predictability. We apply neural network to predict the future market return and observe that CPS-index provides better prediction results in any time horizon longer than twenty seven months.

While an extensive amount of literature exists on the role of futures markets in influencing various dynamics of spot markets, the question whether they stabilize or destabilize the underlying spot market is unresolved. This study addresses this concern and investigates the impact of SSFs (particularly in terms of their destabilizing ability) on the underlying stocks. This study contributes to the literature of financial economics by modifying the famous Sentanan & Wadhwani (1992) model and adding trading volume as a control variable along with Generalized Error Distribution (GED) to capture leptokurtic nature of financial time series data for introduction episode of SSFs in Pakistan. The results of CAPM augmented GJR-GARCH process suggest an insignificant change in coefficients used to gauge market inefficiencies, feedback trading, trading volume and volatility. The findings do not support the hypothesis that the introduction of futures markets significantly impacts positive feedback trading and volatility dynamics of underlying stocks. The results are consistent with some of the earlier studies that futures markets have, at least, no destabilizing effect on the underlying stock market.

This work aimed to solve the problem of Markowitz portfolio optimization for a long-term horizon investment, through the pairs trading cointegrated strategy. Such a strategy allowed us to identify the prices and returns of each stock on the basis of a cointegration relationship estimated by means of the Vector Error Correction Model (VECM). Once the returns had been established, the Markowitz allocation problem among the pairs was solved by minimizing the portfolio risk. We proposed to determine the optimal allocation for each stock as a linear combination of the allocation coefficients calculated for each pair and the cointegration coefficients estimated by means of the VECM model. The proposed strategy was applied to three pairs of real cointegrated stocks belonging to the European financial sector. The results obtained were compared with those from five methods, proposed in the scientific literature, by means of a bootstrap simulation experiment.