IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v71y2021icp299-315.html
   My bibliography  Save this article

What can cluster analysis offer in investing? - Measuring structural changes in the investment universe

Author

Listed:
  • Sim, Min Kyu
  • Deng, Shijie
  • Huo, Xiaoming

Abstract

The return on assets of the investment universe tends to form a cluster structure. This study quantifies this strength of the clustering tendency as a single econometric measure, referred to as modularity. Through an empirical study of the US equity market, we demonstrate that the strength of the clustering tendency changes over time with market fluctuations. That is, normal markets tend to have a clear cluster structure (high modularity), while stressed markets tend to have a blurry cluster structure (low modularity). Modularity assesses the quality of an investment opportunity set in terms of potential diversification benefits. Modularity is an important pricing variable in the cross-sectional returns of US stocks. From 1992 to 2015, the average return of the stocks with the lowest sensitivity to modularity (low modularity beta) exceeds that of the stocks with the highest sensitivity (high modularity beta) by approximately 10.49% annually, adjusted for the Fama-French five-factor exposures. The inclusion of modularity as an asset pricing factor, therefore, expands the investment opportunity set for factor-based investors.

Suggested Citation

  • Sim, Min Kyu & Deng, Shijie & Huo, Xiaoming, 2021. "What can cluster analysis offer in investing? - Measuring structural changes in the investment universe," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 299-315.
  • Handle: RePEc:eee:reveco:v:71:y:2021:i:c:p:299-315
    DOI: 10.1016/j.iref.2020.09.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056020302069
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2020.09.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    4. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    5. Andrea Buraschi & Paolo Porchia & Fabio Trojani, 2010. "Correlation Risk and Optimal Portfolio Choice," Journal of Finance, American Finance Association, vol. 65(1), pages 393-420, February.
    6. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    7. Eric A Stone & Julien F Ayroles, 2009. "Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference," PLOS Genetics, Public Library of Science, vol. 5(5), pages 1-13, May.
    8. Bruce N. Lehmann & David M. Modest, 2005. "Diversification and the Optimal Construction of Basis Portfolios," Management Science, INFORMS, vol. 51(4), pages 581-598, April.
    9. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    10. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    11. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    12. 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.
    13. Gibbons, Michael R & Ross, Stephen A & Shanken, Jay, 1989. "A Test of the Efficiency of a Given Portfolio," Econometrica, Econometric Society, vol. 57(5), pages 1121-1152, September.
    14. Materassi, Donatello & Innocenti, Giacomo, 2009. "Unveiling the connectivity structure of financial networks via high-frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3866-3878.
    15. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    16. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    17. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    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. Philipp Wirth & Francesca Medda & Thomas Schroder, 2024. "Longitudinal market structure detection using a dynamic modularity-spectral algorithm," Papers 2407.04500, arXiv.org.
    2. Huaxi Yuan & Longhui Zou & Xiangyong Luo & Yidai Feng, 2022. "How Does Manufacturing Agglomeration Affect Green Development? A Spatial and Nonlinear Perspective," IJERPH, MDPI, vol. 19(16), pages 1-23, August.

    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. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    2. Kewei Hou & Chen Xue & Lu Zhang, 2017. "Replicating Anomalies," NBER Working Papers 23394, National Bureau of Economic Research, Inc.
    3. De Moor, Lieven & Dhaene, Geert & Sercu, Piet, 2015. "On comparing zero-alpha tests across multifactor asset pricing models," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 235-240.
    4. Stereńczak, Szymon & Zaremba, Adam & Umar, Zaghum, 2020. "Is there an illiquidity premium in frontier markets?," Emerging Markets Review, Elsevier, vol. 42(C).
    5. Azevedo, Vitor, 2023. "Analysts’ underreaction and momentum strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    6. Ciciretti, Rocco & Dalò, Ambrogio & Dam, Lammertjan, 2023. "The contributions of betas versus characteristics to the ESG premium," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 104-124.
    7. Berggrun, Luis & Cardona, Emilio & Lizarzaburu, Edmundo, 2020. "Firm profitability and expected stock returns: Evidence from Latin America," Research in International Business and Finance, Elsevier, vol. 51(C).
    8. Shiyang Huang & Xin Liu & Dong Lou & Christopher Polk, 2024. "The Booms and Busts of Beta Arbitrage," Management Science, INFORMS, vol. 70(8), pages 5367-5385, August.
    9. Ruanmin Cao & Lajos Horváth & Zhenya Liu & Yuqian Zhao, 2020. "A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 335-358, January.
    10. David Hirshleifer & Kewei Hou & Siew Hong Teoh, 2012. "The Accrual Anomaly: Risk or Mispricing?," Management Science, INFORMS, vol. 58(2), pages 320-335, February.
    11. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    12. Shafiqur Rahman & Matthew J. Schneider, 2019. "Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-34, March.
    13. Cong, Lin William & George, Nathan Darden & Wang, Guojun, 2023. "RIM-based value premium and factor pricing using value-price divergence," Journal of Banking & Finance, Elsevier, vol. 149(C).
    14. Zura Kakushadze & Willie Yu, 2016. "Multifactor Risk Models and Heterotic CAPM," Papers 1602.04902, arXiv.org, revised Mar 2016.
    15. Murtazashvili, Irina & Vozlyublennaia, Nadia, 2012. "The role of data limitations, seasonality and frequency in asset pricing models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 555-574.
    16. Chen, Tsung-Yu & Chao, Ching-Hsiang & Wu, Zhen-Xing, 2021. "Does the turnover effect matter in emerging markets? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    17. Anton Astakhov & Tomas Havranek & Jiri Novak, 2019. "Firm Size And Stock Returns: A Quantitative Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 33(5), pages 1463-1492, December.
    18. Liu, Mengxi (Maggie) & Chan, Kam Fong & Faff, Robert, 2022. "What can we learn from firm-level jump-induced tail risk around earnings announcements?," Journal of Banking & Finance, Elsevier, vol. 138(C).
    19. Fletcher, Jonathan, 2018. "Betas V characteristics: Do stock characteristics enhance the investment opportunity set in U.K. stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 46(C), pages 114-129.
    20. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.

    More about this item

    Keywords

    Cluster analysis; Investment opportunity set; Basis assets; Asset pricing model; Factor model;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:reveco:v:71:y:2021:i:c:p:299-315. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620165 .

    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.