IDEAS home Printed from https://ideas.repec.org/a/bpj/econoa/v18y2024i1p20n1033.html
   My bibliography  Save this article

Asset Pricing and Portfolio Investment Management Using Machine Learning: Research Trend Analysis Using Scientometrics

Author

Listed:
  • Meng Chao

    (Faculty of Accounting, Hebei Finance University, Baoding, 071000, China)

  • Chen Chen

    (Faculty of Accounting, Hebei Finance University, Baoding, 071000, China)

  • Xu Heng

    (Faculty of Accounting, Hebei Finance University, Baoding, 071000, China)

  • Li Ting

    (Faculty of Intelligent Finance, Henan Institute of Economics and Trade, 450046, Zhengzhou, China)

Abstract

“Asset pricing” in the context of financial economics pertains to the investigation and formulation of two fundamental pricing ideas and the models that go along with them. Various models exist for different scenarios, but they can be traced back to either general equilibrium asset pricing or rational asset pricing. Asset pricing models, as the name suggests, serve as valuable tools to assess the value of assets. The general equilibrium theory states that supply and demand interact to determine market prices. In this context, asset prices collectively satisfy the market clearing condition, which dictates that the supply and demand for each asset are equal at the prevailing price. Another crucial aspect of financial planning is portfolio management (PM), which aims to maximise investment profits while minimising losses. PM involves implementing effective asset allocation strategies to enhance returns and mitigate risks. Numerous studies have been conducted worldwide on various types of asset pricing models and investment portfolios, with some incorporating machine learning and deep learning techniques. In several models, the predictive accuracy has exceeded 90%. To shed light on the current research landscape in the realm of asset pricing and portfolio investment, we conducted a scientometric analysis.

Suggested Citation

  • Meng Chao & Chen Chen & Xu Heng & Li Ting, 2024. "Asset Pricing and Portfolio Investment Management Using Machine Learning: Research Trend Analysis Using Scientometrics," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 18(1), pages 1-20.
    • Handle: RePEc:bpj:econoa:v:18:y:2024:i:1:p:20:n:1033
    DOI: 10.1515/econ-2022-0108
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/econ-2022-0108
    Download Restriction: no
    File URL: https://libkey.io/10.1515/econ-2022-0108?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
    ---><---

    References listed on IDEAS

    as
    1. Nini Johana Marín-Rodríguez & Juan David González-Ruiz & Sergio Botero Botero, 2022. "Dynamic Co-Movements among Oil Prices and Financial Assets: A Scientometric Analysis," Sustainability, MDPI, vol. 14(19), pages 1-26, October.
    2. Baker, H. Kent & Kumar, Satish & Pattnaik, Debidutta, 2021. "Twenty-five years of the Journal of Corporate Finance: A scientometric analysis," Journal of Corporate Finance, Elsevier, vol. 66(C).
    3. Dongmei Jing & Mohsen Imeni & Seyyed Ahmad Edalatpanah & Alhanouf Alburaikan & Hamiden Abd El-Wahed Khalifa, 2023. "Optimal Selection of Stock Portfolios Using Multi-Criteria Decision-Making Methods," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    4. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    6. Lucio Fernandez-Arjona & Damir Filipovi'c, 2020. "A machine learning approach to portfolio pricing and risk management for high-dimensional problems," Papers 2004.14149, arXiv.org, revised May 2022.
    7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    8. Vincent Larivière & Éric Archambault & Yves Gingras & Étienne Vignola‐Gagné, 2006. "The place of serials in referencing practices: Comparing natural sciences and engineering with social sciences and humanities," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 997-1004, June.
    9. Saulo Cardoso Maia & Gideon Carvalho Benedicto & José Willer Prado & David Alastair Robb & Oscar Neto Almeida Bispo & Mozar José Brito, 2019. "Mapping the literature on credit unions: a bibliometric investigation grounded in Scopus and Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 929-960, September.
    10. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    11. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    12. Lucio Fernandez‐Arjona & Damir Filipović, 2022. "A machine learning approach to portfolio pricing and risk management for high‐dimensional problems," Mathematical Finance, Wiley Blackwell, vol. 32(4), pages 982-1019, October.
    13. Dejian Yu, 2015. "A scientometrics review on aggregation operator research," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 115-133, October.
    14. Choong Kwai Fatt & Ephrance Abu Ujum & Kuru Ratnavelu, 2010. "The structure of collaboration in the Journal of Finance," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 849-860, December.
    15. William W. Hood & Concepción S. Wilson, 2001. "The Literature of Bibliometrics, Scientometrics, and Informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 52(2), pages 291-314, October.
    16. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, 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. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    2. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    3. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    4. Adnan Ali & Suresh Ramakrishnan & Faisal Faisal & Tooba Akram & Sidra Salam & Sami Ur Rahman, 2023. "Bibliometric analysis of finance and natural resources: past trend, current development, and future prospects," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 13035-13064, November.
    5. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    6. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
    7. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
    8. Cakici, Nusret & Zaremba, Adam, 2024. "What drives stock returns across countries? Insights from machine learning models," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    9. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    10. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    11. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
    12. Shang, Dawei & Guo, Ziyu & Wang, Hui, 2024. "Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum," Finance Research Letters, Elsevier, vol. 67(PB).
    13. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.
    14. Umar, Zaghum & Zaremba, Adam & Umutlu, Mehmet & Mercik, Aleksander, 2024. "Interaction effects in the cross-section of country and industry returns," Journal of Banking & Finance, Elsevier, vol. 165(C).
    15. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    16. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
    17. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    18. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    19. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    20. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.

    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:bpj:econoa:v:18:y:2024:i:1:p:20:n:1033. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.