Pairs trading: does volatility timing matter?

Huck, Nicolas

doi:10.1080/00036846.2015.1068923

Cited by 24 publications

(13 citation statements)

References 34 publications

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“…We see that all our long-short strategies perform particularly well at such times of high market turmoil. This is in line with Do and Faff (2010);Bogomolov (2013); Huck (2015), showing that liquidity-providing pairs trading strategies fare well in light of the financial crisis. Specifically, for the ensemble strategy, we observe annualized returns of more than 400 percent after transaction costs at a Sharpe ratio of 4.5.…”

Section: Sub-period Analysissupporting

confidence: 62%

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Krauß

Anh

Huck

2017

European Journal of Operational Research

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493

263

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods can be applied to various fields, including finance. This article implements and analyses the effectiveness of deep neural networks (DNN), gradientboosted-trees (GBT), random forests (RAF), and a combination (ENS) of these methods in the context of statistical arbitrage. Each model is trained on lagged returns of all stocks in the S&P 500, after elimination of survivor bias. From 1992 to 2015, daily one-day-ahead trading signals are generated based on the probability forecast of a stock to outperform the general market. The highest k probabilities are converted into long and the lowest k probabilities into short positions, thus censoring the less certain middle part of the ranking. Empirical findings are promising. A simple ensemble consisting of one deep neural network, one gradient-boosted tree, and one random forest produces out-of-sample returns exceeding 0.45 percent per day for k = 10, prior to transaction costs. Irrespective of the fact that profits are declining in recent years, our findings pose a severe challenge to the semi-strong form of market efficiency.

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Section: Sub-period Analysissupporting

confidence: 62%

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Krauß

Anh

Huck

2017

European Journal of Operational Research

Self Cite

493

263

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“…Do and Faff () confirm that 32% of all identified distance pairs do not converge. Huck and Afawubo () show that pairs selected based on cointegration more frequently exhibit mean‐reverting behavior compared to distance pairs, even if they do not necessarily converge until the end of the trading period (see share of nonconvergent profitable trades in Huck and Afawubo (, table 3, p. 606)).…”

Section: Distance Approachmentioning

confidence: 99%

Statistical Arbitrage Pairs Trading Strategies: Review and Outlook

Krauß

2016

Journal of Economic Surveys

168

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This survey reviews the growing literature on pairs trading frameworks, i.e., relative-value arbitrage strategies involving two or more securities. The available research is categorized into five groups: The distance approach uses nonparametric distance metrics to identify pairs trading opportunities. The cointegration approach relies on formal cointegration testing to unveil stationary spread time series. The time series approach focuses on finding optimal trading rules for mean-reverting spreads. The stochastic control approach aims at identifying optimal portfolio holdings in the legs of a pairs trade relative to other available securities. The category "other approaches" contains further relevant pairs trading frameworks with only a limited set of supporting literature. Drawing from this large set of research consisting of more than 90 papers, an in-depth assessment of each approach is performed, ultimately revealing strengths and weaknesses relevant for further research and for implementation. Terms of use: Documents in

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“…announcement of quarterly results (Papadakis and Wysocki, 2008) (ii) different recommendations regarding the stocks of the traded pair (Yu, 2011); (iii) considering different market volatility (Huck, 2015); (iv) relationship between return of the strategy and systematic risk factors using Fama and French models of three and five factors, which leads to the conclusion that market, size, value, momentum and reversion factors do not explain returns of pairs trading strategy (Gatev et al, 2006;Bowen and Hutchinson, 2014).…”

Section: Variablesmentioning

confidence: 99%

Pairs Trading Strategy and Idiosyncratic Risk. Evidence in Spain and Europe.

2016

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(max 100 words)Pairs trading strategy's return depends on the divergence/convergence movements of a selected pair of stocks' prices. However, if the stable long term relationship of the stocks changes, price will not converge and the trade opened after divergence will close with losses. We propose a new model that, including companies' fundamental variables that measure idiosyncratic factors, anticipates the changes in this relationship and rejects those trades triggered by a divergence produced by fundamental changes in one of the companies. The model is tested on European stocks and the results obtained outperform those of the base distance model.

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Pairs trading: does volatility timing matter?

Cited by 24 publications

References 34 publications

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Statistical Arbitrage Pairs Trading Strategies: Review and Outlook

Pairs Trading Strategy and Idiosyncratic Risk. Evidence in Spain and Europe.

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