IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i23p4449-d984000.html
   My bibliography  Save this article

Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach

Author

Listed:
  • Ana Lorena Jiménez-Preciado

    (Escuela Superior de Economía, Instituto Politécnico Nacional, Mexico City 11350, Mexico)

  • Francisco Venegas-Martínez

    (Escuela Superior de Economía, Instituto Politécnico Nacional, Mexico City 11350, Mexico)

  • Abraham Ramírez-García

    (Escuela Superior de Economía, Instituto Politécnico Nacional, Mexico City 11350, Mexico)

Abstract

This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks’ moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors’ knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks’ moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading.

Suggested Citation

  • Ana Lorena Jiménez-Preciado & Francisco Venegas-Martínez & Abraham Ramírez-García, 2022. "Stock Portfolio Optimization with Competitive Advantages (MOAT): A Machine Learning Approach," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4449-:d:984000
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/23/4449/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/23/4449/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. E. Raguseo & Pigni, F. & Claudio Vitari, 2021. "Streams of digital data and competitive advantage: The mediation effects of process efficiency and product effectiveness," Post-Print hal-03323663, HAL.
    2. Ruiz-Moreno, Felipe & Mas-Ruiz, Francisco J. & Sancho-Esper, Franco M., 2021. "Strategic groups and product differentiation: Evidence from the Spanish airline market deregulation," Research in Transportation Economics, Elsevier, vol. 90(C).
    3. Seo, Young-Joon & Park, Jin Suk, 2016. "The estimation of minimum efficient scale of the port industry," Transport Policy, Elsevier, vol. 49(C), pages 168-175.
    4. Berendt, Johannes & Uhrich, Sebastian & Thompson, Scott A., 2018. "Marketing, get ready to rumble—How rivalry promotes distinctiveness for brands and consumers," Journal of Business Research, Elsevier, vol. 88(C), pages 161-172.
    5. Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    6. E. Raguseo & Pigni, F. & Claudio Vitari, 2021. "Streams of digital data and competitive advantage: The mediation effects of process efficiency and product effectiveness," Grenoble Ecole de Management (Post-Print) hal-03323663, HAL.
    7. Bet, Germán, 2021. "Product specification under a threat of entry: Evidence from Airlines’ departure times," International Journal of Industrial Organization, Elsevier, vol. 75(C).
    8. Azeem, Muhammad & Ahmed, Munir & Haider, Sajid & Sajjad, Muhammad, 2021. "Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation," Technology in Society, Elsevier, vol. 66(C).
    9. Guthrie, Graeme, 2020. "Investment flexibility as a barrier to entry," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    10. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    11. Lim, Steve C. & Macias, Antonio J. & Moeller, Thomas, 2020. "Intangible assets and capital structure," Journal of Banking & Finance, Elsevier, vol. 118(C).
    12. Rizkiah, Siti K. & Disli, Mustafa & Salim, Kinan & Razak, Lutfi A., 2021. "Switching costs and bank competition: Evidence from dual banking economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    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. He, Yi & You, Ya & Chen, Qimei, 2020. "Our conditional love for the underdog: The effect of brand positioning and the lay theory of achievement on WOM," Journal of Business Research, Elsevier, vol. 118(C), pages 210-222.
    2. Ha, Min-Ho & Yang, Zaili & Lam, Jasmine Siu Lee, 2019. "Port performance in container transport logistics: A multi-stakeholder perspective," Transport Policy, Elsevier, vol. 73(C), pages 25-40.
    3. Chau Le & Bach Nguyen & Vinh Vo, 2024. "Do intangible assets help SMEs in underdeveloped markets gain access to external finance?—the case of Vietnam," Small Business Economics, Springer, vol. 62(2), pages 833-855, February.
    4. repec:zib:zbmbmj:v:1:y:2022:i:2:p:53-58 is not listed on IDEAS
    5. Oliveira, Bruno F. & Oliveira, Alessandro V.M., 2022. "An empirical analysis of the determinants of network construction for Azul Airlines," Journal of Air Transport Management, Elsevier, vol. 101(C).
    6. Ebrahim A. A. Ghaleb & P. D. D. Dominic & Narinderjit Singh Sawaran Singh & Gehad Mohammed Ahmed Naji, 2023. "Assessing the Big Data Adoption Readiness Role in Healthcare between Technology Impact Factors and Intention to Adopt Big Data," Sustainability, MDPI, vol. 15(15), pages 1-25, July.
    7. Ebina, Takeshi & Matsushima, Noriaki & Nishide, Katsumasa, 2022. "Demand uncertainty, product differentiation, and entry timing under spatial competition," European Journal of Operational Research, Elsevier, vol. 303(1), pages 286-297.
    8. Guangchun Jin & Jian Xu, 2022. "Does Intellectual Capital Affect Financial Leverage of Chinese Agricultural Companies? Exploring the Role of Firm Profitability," Sustainability, MDPI, vol. 14(5), pages 1-14, February.
    9. Zixia Cao & Kehan Xu, 2022. "CEO narcissism, brand acquisition and disposal, and stock returns," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 777-799, July.
    10. Liao, Junyun & Chen, Jiawen & Zhao, Haichuan & Li, Muhua, 2023. "Fanning the flames: Transmitting negative word of mouth of rival brands," Journal of Business Research, Elsevier, vol. 154(C).
    11. Enkeleda Lulaj & Blerta Dragusha & Eglantina Hysa, 2023. "Investigating Accounting Factors through Audited Financial Statements in Businesses toward a Circular Economy: Why a Sustainable Profit through Qualified Staff and Investment in Technology?," Administrative Sciences, MDPI, vol. 13(3), pages 1-28, March.
    12. Chen-I Huang & Yung-Fu Huang & Manh-Hoang Do & Thi-Lien-Huong Nguyen, 2023. "Sustainable Competitive Advantage for Multi-Unit Franchising: From the Taiwanese Franchise Market Perspectives," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    13. Lorena Espina-Romero & José Gregorio Noroño Sánchez & Humberto Gutiérrez Hurtado & Helga Dworaczek Conde & Yessenia Solier Castro & Luz Emérita Cervera Cajo & Jose Rio Corredoira, 2023. "Which Industrial Sectors Are Affected by Artificial Intelligence? A Bibliometric Analysis of Trends and Perspectives," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    14. Michael Machokoto & Daniel Gyimah & Boulis Maher Ibrahim, 2022. "The evolution of trade credit: new evidence from developed versus developing countries," Review of Quantitative Finance and Accounting, Springer, vol. 59(3), pages 857-912, October.
    15. Dong, Gang & Zheng, Shiyuan & Lee, Paul Tae-Woo, 2018. "The effects of regional port integration: The case of Ningbo-Zhoushan Port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 1-15.
    16. Kristina Astike & Viktorija Skvarciany, 2024. "Measuring the Level of Performance of Country’s Cultural Economics: The Case of EU11," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 312-312, February.
    17. Chen, Tai-Yueh & Chang, Wei-Chen & Hsieh, Kuo-Jung & Chang, Ching-Ter, 2022. "Advancing Taiwan's traditional craft products: A modular product design model of manufacturing technologies," Technology in Society, Elsevier, vol. 71(C).
    18. Morlacco, Monica & Zeke, David, 2021. "Monetary policy, customer capital, and market power," Journal of Monetary Economics, Elsevier, vol. 121(C), pages 116-134.
    19. Guadalupe del Carmen Briano Turrent & Jannine Poletti-Hughes & Jonathan Williams, 2023. "Transparency on Corporate Governance and board of directors' strategies," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(2), pages 1-22, Abril - J.
    20. Shelly-Ann Julien & Jonathan Cowie & Jason Monios, 2020. "Efficiency, productivity and returns to scale in ports: a comparison of data envelopment analysis and econometric estimation with application to Caribbean Small Island Developing States," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 22(2), pages 239-264, June.
    21. Wajahat Azmi & Zaheer Anwer & Mohsin Ali & Shamsher Mohamad, 2024. "Competition, stability, and institutional environment: The case of dual banking economies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 609-631, January.

    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:gam:jmathe:v:10:y:2022:i:23:p:4449-:d:984000. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.