IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.07944.html
   My bibliography  Save this paper

Portfolio Selection via Topological Data Analysis

Author

Listed:
  • Petr Sokerin
  • Kristian Kuznetsov
  • Elizaveta Makhneva
  • Alexey Zaytsev

Abstract

Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inability of these methods to account for the unique characteristics of multivariate time series data from stock markets. We present a two-stage method for constructing an investment portfolio of common stocks. The method involves the generation of time series representations followed by their subsequent clustering. Our approach utilizes features based on Topological Data Analysis (TDA) for the generation of representations, allowing us to elucidate the topological structure within the data. Experimental results show that our proposed system outperforms other methods. This superior performance is consistent over different time frames, suggesting the viability of TDA as a powerful tool for portfolio selection.

Suggested Citation

  • Petr Sokerin & Kristian Kuznetsov & Elizaveta Makhneva & Alexey Zaytsev, 2023. "Portfolio Selection via Topological Data Analysis," Papers 2308.07944, arXiv.org.
  • Handle: RePEc:arx:papers:2308.07944
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2308.07944
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Ľuboš Pástor & Pietro Veronesi, 2020. "Political Cycles and Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4011-4045.
    4. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    Full references (including those not matched with items on IDEAS)

    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. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    2. Ben Craig & Martín Saldías, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers 2016/184, International Monetary Fund.
    3. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    4. Christophe Chorro & Emmanuelle Jay & Philippe De Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Documents de travail du Centre d'Economie de la Sorbonne 21013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    6. Lu, Shan & Zhao, Jichang & Wang, Huiwen & Ren, Ruoen, 2018. "Herding boosts too-connected-to-fail risk in stock market of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 945-964.
    7. Zhang, Weiping & Zhuang, Xintian & Wang, Jian & Lu, Yang, 2020. "Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    8. Irena Vodenska & Alexander P. Becker & Di Zhou & Dror Y. Kenett & H. Eugene Stanley & Shlomo Havlin, 2016. "Community Analysis of Global Financial Markets," Risks, MDPI, vol. 4(2), pages 1-15, May.
    9. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    10. Guo, Hongfeng & Zhao, Xinyao & Yu, Hang & Zhang, Xin, 2021. "Analysis of global stock markets’ connections with emphasis on the impact of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    11. Costantini, Mauro & Maaitah, Ahmad & Mishra, Tapas & Sousa, Ricardo M., 2023. "Bitcoin market networks and cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    12. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    13. Danau, Daniel, 2020. "Prudence and preference for flexibility gain," European Journal of Operational Research, Elsevier, vol. 287(2), pages 776-785.
    14. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    15. Tan T. M. Le & Franck Martin & Duc K. Nguyen, 2018. "Dynamic connectedness of global currencies: a conditional Granger-causality approach," Economics Working Paper Archive (University of Rennes & University of Caen) 2018-04, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    16. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    17. 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.
    18. Gustavo Peralta, 2015. "Network-based Measures as Leading Indicators of Market Instability: The case of the Spanish Stock," CNMV Working Papers CNMV Working Papers no 59, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    19. Výrost, Tomas & Lyócsa, Štefan & Baumöhl, Eduard, 2019. "Network-based asset allocation strategies," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 516-536.
    20. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).

    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:2308.07944. 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.