Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market

Abstract We systematically investigate the features of pairs trading strategy from a network perspective. Based on the cointegration theory, we construct the full graphs (FGs) and minimum spanning trees (MSTs) in terms of the cointegration matrix by using daily stock prices from the Chinese stock market around the launch of the margin-trading and short-selling (MTSS) programme. The static and dynamic features of the networks are analyzed with respect to the formation and evolution of edges. Our results show (i) that the assets allowed MTSS or within the same industry are more inclined to form trading pairs, (ii) that the cointegration relationships among trading pairs are as fragile as only a few number of them survived from one period to the next, (iii) that the movement of cointegration relationships is independent to stock market trends, confirming that pairs trading is a market neutral strategy, and (iv) that over 45% trading pairs are excluded in the re-occurrence circles of edges within 12 months, indicating that a considerable number of trading pairs are unsteady and investors should carefully pick pairs for trading in such short-term speculations.

[1]  A. A. Weiss,et al.  Time series analysis of error-correction models , 2001 .

[2]  George J. Miao.  High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach , 2014 .

[3]  Chi Xie,et al.  Interconnectedness and systemic risk of China's financial institutions , 2017, Emerging Markets Review.

[4]  Huajiao Li,et al.  Features of spillover networks in international financial markets: Evidence from the G20 countries , 2017 .

[5]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[6]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Rand Kwong Yew Low,et al.  The profitability of pairs trading strategies: distance, cointegration and copula methods , 2016 .

[8]  C. Granger.  Some properties of time series data and their use in econometric model specification , 1981 .

[9]  Mingang Wang,et al.  Stock market as temporal network , 2017, Physica A: Statistical Mechanics and its Applications.

[10]  W. P. Malcolm,et al.  Pairs trading , 2005 .

[11]  R. Mantegna.  Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[12]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  G. Caldarelli,et al.  Networks of equities in financial markets , 2004 .

[14]  Bayram Deviren,et al.  Topology of the correlation networks among major currencies using hierarchical structure methods , 2011 .

[15]  William N. Goetzmann,et al.  Pairs Trading: Performance of a Relative Value Arbitrage Rule , 1998 .

[16]  Carol Alexander,et al.  The Cointegration Alpha: Enhanced Index Tracking and Long-Short Equity Market Neutral Strategies , 2002 .

[17]  Philip S. Yu,et al.  Improving stock market prediction via heterogeneous information fusion , 2017, Knowl. Based Syst..

[18]  Boris Podobnik,et al.  The q-dependent detrended cross-correlation analysis of stock market , 2017, 1705.01406.

[19]  Abalfazl Zareei,et al.  A Network Approach to Portfolio Selection , 2016 .

[20]  Robert F. Engle,et al.  Forecasting and testing in co-integrated systems , 1987 .

[21]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[22]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[23]  Chi Xie,et al.  Tail dependence structure of the foreign exchange market: A network view , 2016, Expert Syst. Appl..

[24]  Chi Xie,et al.  Multiscale correlation networks analysis of the US stock market: a wavelet analysis , 2016, Journal of Economic Interaction and Coordination.

[25]  G. Vidyamurthy.  Pairs Trading: Quantitative Methods and Analysis , 2004 .

[26]  Nicolas Huck,et al.  Pairs trading and outranking: The multi-step-ahead forecasting case , 2010, Eur. J. Oper. Res..

[27]  Rosario N. Mantegna,et al.  Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs , 2011 .

[28]  Rosario N. Mantegna,et al.  Evolution of Correlation Structure of Industrial Indices of US Equity Markets , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Brian M. Lucey,et al.  Comovements in government bond markets: A minimum spanning tree analysis , 2010 .

[30]  William T. Shaw,et al.  Correlation structure and dynamics in volatile markets , 2010 .

[31]  Chunxia Yang,et al.  Cointegration analysis and influence rank—A network approach to global stock markets , 2014 .

[32]  K. Kaski,et al.  Dynamics of market correlations: taxonomy and portfolio analysis. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Jurgen Kurths,et al.  Synchronization in complex networks , 2008, 0805.2976.

[34]  Chi Xie,et al.  Extreme risk spillover network: application to financial institutions , 2017 .

[35]  Yan-Xia Lin,et al.  Loss protection in pairs trading through minimum profit bounds: A cointegration approach , 2006, Adv. Decis. Sci..

[36]  Chi Xie,et al.  Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks , 2018 .

[37]  T. Aste,et al.  Spread of risk across financial markets: better to invest in the peripheries , 2013, Scientific Reports.

[38]  Chi Xie,et al.  Correlation structure and dynamics of international real estate securities markets: A network perspective , 2015 .

[39]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[40]  Chengyi Tu,et al.  Cointegration-based financial networks study in Chinese stock market , 2014 .