IDEAS home Printed from https://ideas.repec.org/p/spa/wpaper/2018wpecon23.html
   My bibliography  Save this paper

Machine learning applied to accounting variables yields the risk-return metrics of private company portfolios

Author

Listed:
  • Elias Cavalcante-Filho
  • Flavio Abdenur, Rodrigo De Losso

Abstract

Constructing optimal Markowitz Mean-Variance portfolios of publicly-traded stock is a straighforward and well-known task. Doing the same for portfolios of privately-owned firms, given the lack of historical price data, is a challenge. We apply machine learning models to historical accounting variable data to estimate risk-return metrics – specifically, expected excess returns, price volatility and (pairwise) price correlation – of private companies, which should allow the construction of Mean-Variance optimized portfolios consisting of private companies. We attain out-of-sample 𠑅2 s around 45%, while linear regressions yield 𠑅2 s of only about 10%. This short paper is the result of a real-world consulting project on behalf of Votorantim S.A (“VSA†), a multinational holding company. To the authors’ best knowledge this is a novel application of machine learning in the finance literature.

Suggested Citation

  • Elias Cavalcante-Filho & Flavio Abdenur, Rodrigo De Losso, 2018. "Machine learning applied to accounting variables yields the risk-return metrics of private company portfolios," Working Papers, Department of Economics 2018_23, University of São Paulo (FEA-USP).
    • Handle: RePEc:spa:wpaper:2018wpecon23
    as

    Download full text from publisher

    File URL: http://www.repec.eae.fea.usp.br/documentos/Cavalcante_Abdenur_DeLosso_23WP.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. LAVEREN, Eddy & DURINCK, Eduard & DE CEUSTER, Marc & LYBAERT, Nadine, 1997. "Can accounting variables explain any beta? The empirical association between various betas and nine accounting variables in Belgian listed firms," Business Economics Working Papers 1997006, University of Antwerp, Faculty of Business and Economics.
    3. Bowman, Robert G, 1979. "The Theoretical Relationship between Systematic Risk and Financial (Accounting) Variables," Journal of Finance, American Finance Association, vol. 34(3), pages 617-630, June.
    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. Tong, Jianfeng & Liu, Zhenxing & Zhang, Yong & Zheng, Xiujuan & Jin, Junyang, 2023. "Improved multi-gate mixture-of-experts framework for multi-step prediction of gas load," Energy, Elsevier, vol. 282(C).
    2. J. Sarmiento-Sabogal & M. Sadeghi, 2015. "Estimating the cost of equity for private firms using accounting fundamentals," Applied Economics, Taylor & Francis Journals, vol. 47(3), pages 288-301, January.
    3. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    4. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    5. Choi, Jong-Seo & Choe, Chongwoo, 1998. "Explanatory factors for trading volume responses to annual earnings announcements: Evidence from the Korean stock market," Pacific-Basin Finance Journal, Elsevier, vol. 6(1-2), pages 193-212, May.
    6. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    7. Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
    8. Hsieh, Su-Jane & Liu, Shuming, 2021. "The cost-of-equity implications of off-balance sheet pension liabilities," Journal of Contemporary Accounting and Economics, Elsevier, vol. 17(1).
    9. Zhao-Yue Chen & Hervé Petetin & Raúl Fernando Méndez Turrubiates & Hicham Achebak & Carlos Pérez García-Pando & Joan Ballester, 2024. "Population exposure to multiple air pollutants and its compound episodes in Europe," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    10. Vicente Lima Crisóstomo & Priscila Azevedo Prudêncio & Isac Freitas Brandão, 2023. "Corporate Reputation in Brazil: The Effects of the Shareholding Control Configuration, Corporate Governance, and Corporate Social Responsibility," Corporate Reputation Review, Palgrave Macmillan, vol. 26(4), pages 243-263, November.
    11. Piotr Pomorski & Denise Gorse, 2023. "Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes," Papers 2310.04536, arXiv.org.
    12. Caperna, Giulio & Colagrossi, Marco & Geraci, Andrea & Mazzarella, Gianluca, 2022. "A babel of web-searches: Googling unemployment during the pandemic," Labour Economics, Elsevier, vol. 74(C).
    13. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    14. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    15. Kusano, Masaki, 2020. "Does recognition versus disclosure affect risk relevance? Evidence from finance leases in Japan," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 38(C).
    16. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    17. Andrea Albergoni & Florentina J. Hettinga & Wim Stut & Francesco Sartor, 2020. "Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
    18. Franck M. Ramaharo & Michael Fitiavana Randriamifidy, 2023. "Determinants of renewable energy consumption in Madagascar: Evidence from feature selection algorithms," Working Papers hal-04262240, HAL.
    19. Nikolay Osadchiy & Vishal Gaur & Sridhar Seshadri, 2016. "Systematic Risk in Supply Chain Networks," Management Science, INFORMS, vol. 62(6), pages 1755-1777, June.
    20. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).

    More about this item

    Keywords

    assent pricing; Machine Learning; Portfolio Theory;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:spa:wpaper:2018wpecon23. 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: Pedro Garcia Duarte (email available below). General contact details of provider: https://edirc.repec.org/data/deuspbr.html .

    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.