Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&amp;P 100

European Journal of Operational Research

Anh

Huck

2017

Self Cite

460

263

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.

Section: Datamentioning

confidence: 99%

“…As in Krauss and Stübinger (2015), our choice is motivated by computational feasibility, market efficiency, and liquidity. The S&P 500 consists of the leading 500 companies in the U.S. stock market, accounting for approximately 80 percent of available market capitalization (S&P Dow Jones Indices, 2015).…”

Section: Datamentioning

confidence: 99%

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

European Journal of Operational Research

Anh

Huck

2017

Self Cite

460

263

“…Given intense analyst coverage and high investor attention, this market segment serves as a true acid test for any potential capital market anomaly. We follow Krauss and Stübinger (2015) in order to eliminate survivor bias from our data base. First, using…”

Section: Data and Softwarementioning

confidence: 99%

“…Key representatives are Liew and Wu (2013); Xie and Wu (2013); Stander et al (2013); Xie et al (2014); Krauss and Stübinger (2015); Rad et al (2016). These studies use bivariate copulas to model the dependence structure between two stock return time series, and to identify mispricings that can potentially be exploited in a pairs trading application.…”

Section: Introductionmentioning

confidence: 99%

Statistical arbitrage with vine copulas

Stübinger

Mangold

2018

Quantitative Finance

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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 AbstractWe develop a multivariate statistical arbitrage strategy based on vine copulas -a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25 percent p.a. and a Sharpe ratio of 1.12 after transaction costs for the period from 1992 until 2015. Tail risk is limited, with maximum drawdown at 6.57 percent. The high returns can only partially be explained by common sources of systematic risk. We benchmark the vine copula strategy against other variants relying on the multivariate Gaussian and t-distribution and we find its results to be superior in terms of risk and return characteristics. The multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modeling upper and lower tail dependence increases well over 90 percent at times of high market turmoil.

“…Table 12 presents exposures to common sources of systematic risk for the PCI strategy after transaction costs. We perform three regressions, as in Krauss and Stübinger (2015).…”

Section: Performance Evaluationmentioning

confidence: 99%

Pairs trading with partial cointegration

Clegg

2017

Quantitative Finance

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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 AbstractPartial cointegration is a weakening of cointegration that allows for the "cointegrating" process to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood based estimation routine, and a suitable likelihood ratio test. Then, we explore the use of partial cointegration as a means for identifying promising pairs and for generating buy and sell signals. Specifically, we benchmark partial cointegration against several classical pairs trading variants from 1990 until 2015, on a survivor bias free data set of the S&P 500 constituents. We find annualized returns of more than 12 percent after transaction costs. These results can only partially be explained by common sources of systematic risk and are well superior to classical distance-based or cointegration-based pairs trading variants on our data set.