IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2201.08283.html
   My bibliography  Save this paper

Lead-lag detection and network clustering for multivariate time series with an application to the US equity market

Author

Listed:
  • Stefanos Bennett
  • Mihai Cucuringu
  • Gesine Reinert

Abstract

In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead-lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead-lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead-lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead-lag metric and directed network clustering components. Our framework is validated on both a synthetic generative model for multivariate lead-lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead-lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead-lag relations and demonstrate how these can be used for the construction of predictive financial signals.

Suggested Citation

  • Stefanos Bennett & Mihai Cucuringu & Gesine Reinert, 2022. "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market," Papers 2201.08283, arXiv.org.
    • Handle: RePEc:arx:papers:2201.08283
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2201.08283
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chester Curme & Michele Tumminello & Rosario N. Mantegna & H. Eugene Stanley & Dror Y. Kenett, 2015. "Emergence of statistically validated financial intraday lead-lag relationships," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1375-1386, August.
    2. Fogel, Fajwel & d'Aspremont, Alexandre & Vojnovic, Milan, 2016. "Spectral ranking using seriation," LSE Research Online Documents on Economics 68987, London School of Economics and Political Science, LSE Library.
    3. Tarun Chordia & Bhaskaran Swaminathan, 2000. "Trading Volume and Cross‐Autocorrelations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(2), pages 913-935, April.
    4. Paweł Fiedor, 2014. "Information-theoretic approach to lead-lag effect on financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(8), pages 1-9, August.
    5. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    6. Edward O. Thorp, 2011. "The Kelly Criterion in Blackjack Sports Betting, and the Stock Market," World Scientific Book Chapters, in: Leonard C MacLean & Edward O Thorp & William T Ziemba (ed.), THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 54, pages 789-832, World Scientific Publishing Co. Pte. Ltd..
    7. 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.
    8. Lior Menzly & Oguzhan Ozbas, 2010. "Market Segmentation and Cross‐predictability of Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1555-1580, August.
    9. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    10. Daniel L. Sussman & Minh Tang & Donniell E. Fishkind & Carey E. Priebe, 2012. "A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1119-1128, September.
    11. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    12. Christoly Biely & Stefan Thurner, 2008. "Random matrix ensembles of time-lagged correlation matrices: derivation of eigenvalue spectra and analysis of financial time-series," Quantitative Finance, Taylor & Francis Journals, vol. 8(7), pages 705-722.
    13. Chester Curme & Michele Tumminello & Rosario N. Mantegna & H. Eugene Stanley & Dror Y. Kenett, 2015. "How Lead-Lag Correlations Affect the Intraday Pattern of Collective Stock Dynamics," Working Papers 15-15, Office of Financial Research, US Department of the Treasury.
    14. Camilleri, Silvio John & Scicluna, Nicolanne & Bai, Ye, 2019. "Do stock markets lead or lag macroeconomic variables? Evidence from select European countries," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 170-186.
    15. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    16. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    17. Conrad, Jennifer & Gultekin, Mustafa N & Kaul, Gautam, 1991. "Asymmetric Predictability of Conditional Variances," The Review of Financial Studies, Society for Financial Studies, vol. 4(4), pages 597-622.
    18. Daniel Levin & Terry Lyons & Hao Ni, 2013. "Learning from the past, predicting the statistics for the future, learning an evolving system," Papers 1309.0260, arXiv.org, revised Mar 2016.
    19. Jegadeesh, Narasimhan & Titman, Sheridan, 1995. "Overreaction, Delayed Reaction, and Contrarian Profits," The Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 973-993.
    20. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    21. Farrell, James L, Jr, 1974. "Analyzing Covariation of Returns to Determine Homogeneous Stock Groupings," The Journal of Business, University of Chicago Press, vol. 47(2), pages 186-207, April.
    22. John Douglas (J.D.) Opdyke, 2007. "Comparing Sharpe ratios: So where are the p-values?," Journal of Asset Management, Palgrave Macmillan, vol. 8(5), pages 308-336, December.
    23. Xia, Lisi & You, Daming & Jiang, Xin & Chen, Wei, 2018. "Emergence and temporal structure of Lead–Lag correlations in collective stock dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 545-553.
    24. Brennan, Michael J & Jegadeesh, Narasimhan & Swaminathan, Bhaskaran, 1993. "Investment Analysis and the Adjustment of Stock Prices to Common Information," The Review of Financial Studies, Society for Financial Studies, vol. 6(4), pages 799-824.
    25. Lajos Gergely Gyurk'o & Terry Lyons & Mark Kontkowski & Jonathan Field, 2013. "Extracting information from the signature of a financial data stream," Papers 1307.7244, arXiv.org, revised Jul 2014.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Deborah Sulem & Henry Kenlay & Mihai Cucuringu & Xiaowen Dong, 2022. "Graph similarity learning for change-point detection in dynamic networks," Papers 2203.15470, arXiv.org.
    2. Yutong Lu & Gesine Reinert & Mihai Cucuringu, 2023. "Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets," Papers 2302.09382, arXiv.org, revised May 2024.
    3. Yichi Zhang & Mihai Cucuringu & Alexander Y. Shestopaloff & Stefan Zohren, 2023. "Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models," Papers 2309.08800, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Sharifkhani, Ali & Simutin, Mikhail, 2021. "Feedback loops in industry trade networks and the term structure of momentum profits," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1171-1187.
    3. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    4. Chordia, Tarun & Sarkar, Asani & Subrahmanyam, Avanidhar, 2005. "The Joint Dynamics of Liquidity, Returns, and Volatility Across Small and Large Firms," University of California at Los Angeles, Anderson Graduate School of Management qt6z81z2wc, Anderson Graduate School of Management, UCLA.
    5. Ling Cen & Kalok Chan & Sudipto Dasgupta & Ning Gao, 2013. "When the Tail Wags the Dog: Industry Leaders, Limited Attention, and Spurious Cross-Industry Information Diffusion," Management Science, INFORMS, vol. 59(11), pages 2566-2585, November.
    6. Gao, George P. & Moulton, Pamela C. & Ng, David T., 2017. "Institutional ownership and return predictability across economically unrelated stocks," Journal of Financial Intermediation, Elsevier, vol. 31(C), pages 45-63.
    7. Chi Dong & Hooi Hooi Lean & Zamri Ahmad & Wing-Keung Wong, 2019. "The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    8. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
    9. Chenglu Jin & Thomas Conlon & John Cotter, 2023. "Co-Skewness across Return Horizons," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1483-1518.
    10. Francis, Bill B. & Mougoué, Mbodja & Panchenko, Valentyn, 2010. "Is there a symmetric nonlinear causal relationship between large and small firms?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 23-38, January.
    11. Chi Dong & Hooi Hooi Lean & Zamri Ahmad, 2017. "Intra-industry information diffusion in China's stock market," Economics Bulletin, AccessEcon, vol. 37(1), pages 1-11.
    12. Masaki Mori, 2015. "Information Diffusion in the U.S. Real Estate Investment Trust Market," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 190-214, August.
    13. Gebka, Bartosz, 2008. "Volume- and size-related lead-lag effects in stock returns and volatility: An empirical investigation of the Warsaw Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 134-155.
    14. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    15. Judge, Amrit & Reancharoen, Tipprapa, 2014. "An empirical examination of the lead–lag relationship between spot and futures markets: Evidence from Thailand," Pacific-Basin Finance Journal, Elsevier, vol. 29(C), pages 335-358.
    16. Chen, Zilin & Guo, Li & Tu, Jun, 2021. "Media connection and return comovement," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    17. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2020. "Lead–lag relationships in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    18. Sandrine Jacob Leal, 2015. "Fundamentalists, Chartists and Asset pricing anomalies," Post-Print hal-01508002, HAL.
    19. DePenya, Francisco J. & Gil-Alana, Luis A., 2007. "Serial correlation in the Spanish Stock Market," Global Finance Journal, Elsevier, vol. 18(1), pages 84-103.
    20. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2201.08283. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.