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Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

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  • Chen, Kun
  • Luo, Peng
  • Sun, Bianxia
  • Wang, Huaiqing

Abstract

According to asset pricing theory, a stock’s expected returns are determined by its exposure to systematic risk. In this paper, we propose a new method for analyzing the interaction effects among industries and stocks on stock returns. We construct a complex network based on correlations of abnormal stock returns and use centrality and modularity, two popular measures in social science, to determine the effect of interconnections on industry and stock returns. Supported by previous studies, our findings indicate that a relationship exists between inter-industry closeness and industry returns and between stock centrality and stock returns. The theoretical and practical contributions of these findings are discussed.

Suggested Citation

  • Chen, Kun & Luo, Peng & Sun, Bianxia & Wang, Huaiqing, 2015. "Which stocks are profitable? A network method to investigate the effects of network structure on stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 224-235.
  • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:224-235
    DOI: 10.1016/j.physa.2015.05.047
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    as
    1. Bakker, L. & Hare, W. & Khosravi, H. & Ramadanovic, B., 2010. "A social network model of investment behaviour in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1223-1229.
    2. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    3. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    4. Li, Ming-Xia & Jiang, Zhi-Qiang & Xie, Wen-Jie & Xiong, Xiong & Zhang, Wei & Zhou, Wei-Xing, 2015. "Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 575-584.
    5. Li, Ping & Wang, Bing-Hong, 2007. "Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 519-526.
    6. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    7. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    8. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    9. Lee, Junghoon & Youn, Janghyuk & Chang, Woojin, 2012. "Intraday volatility and network topological properties in the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1354-1360.
    10. repec:bla:jfinan:v:43:y:1988:i:5:p:1161-75 is not listed on IDEAS
    11. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    12. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    13. Kenneth R. Ahern & Jarrad Harford, 2014. "The Importance of Industry Links in Merger Waves," Journal of Finance, American Finance Association, vol. 69(2), pages 527-576, April.
    14. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    15. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    16. Caraiani, Petre, 2012. "Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(13), pages 3629-3637.
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