IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v4y2025i2p14-d1626403.html
   My bibliography  Save this article

Automated Ledger or Fintech Analytics Platform?

Author

Listed:
  • Andrew Kumiega

    (Stuart School of Business, Illinois Institute of Technology, Chicago, IL 60616, USA)

Abstract

Initially designed as an automated ledger tool, Excel swiftly evolved into a data analytics platform for financial analysts to execute intricate financial analyses. Excel is so commonplace in the financial industry that many do not even consider it a fintech tool. The transformation of Excel from a simple ledger tool to a low-code machine learning (mL) platform is not a traditional focus for fintech. The transformation of Excel into an mL platform will let financial analysts and quantitative analyses quickly evolve financial models in Excel to use advanced mL techniques. The low-code interface lets analysts quickly build predictive models. This paper explores how Excel has evolved into a low-code machine platform for financial applications along with the risks associated with Excel’s new functionality.

Suggested Citation

  • Andrew Kumiega, 2025. "Automated Ledger or Fintech Analytics Platform?," FinTech, MDPI, vol. 4(2), pages 1-12, April.
    • Handle: RePEc:gam:jfinte:v:4:y:2025:i:2:p:14-:d:1626403
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/4/2/14/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2674-1032/4/2/14/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Kumiega & Ben Van Vliet, 2008. "Optimal Trading of ETFs: Spreadsheet Prototypes and Applications to Client-Server Applications," Interfaces, INFORMS, vol. 38(4), pages 289-299, August.
    2. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    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. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    3. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    4. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    5. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    6. N. C. Suganya & G. A. Vijayalakshmi Pai, 2010. "Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(2), pages 59-90, April.
    7. Jyotirmayee Behera & Pankaj Kumar, 2024. "Implementation of machine learning in $$\ell _{\infty }$$ ℓ ∞ -based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market," Operational Research, Springer, vol. 24(4), pages 1-26, December.
    8. 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.
    9. Thomas Trier Bjerring & Omri Ross & Alex Weissensteiner, 2017. "Feature selection for portfolio optimization," Annals of Operations Research, Springer, vol. 256(1), pages 21-40, September.
    10. Pinar OKAN GOKTEN & Furkan BASER & Soner GOKTEN, 2017. "Using fuzzy c-means clustering algorithm in financial health scoring," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 385-385.
    11. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621, arXiv.org.
    12. Marie Brière & Ariane Szafarz, 2021. "When it rains, it pours: Multifactor asset management in good and bad times," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(3), pages 641-669, September.
    13. Ahmet Sensoy & Duc Khuong Nguyen & Ahmed Rostom & Erk Hacihasanoglu, 2019. "Dynamic integration and network structure of the EMU sovereign bond markets," Annals of Operations Research, Springer, vol. 281(1), pages 297-314, October.
    14. Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.
    15. Paolo Giudici & Gloria Polinesi, 2021. "Crypto price discovery through correlation networks," Annals of Operations Research, Springer, vol. 299(1), pages 443-457, April.
    16. Xing, Xin & Hu, Jinjin & Yang, Yaning, 2014. "Robust minimum variance portfolio with L-infinity constraints," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 107-117.
    17. Nikolaus Hautsch & Lada M. Kyj & Roel C. A. Oomen, 2012. "A blocking and regularization approach to high‐dimensional realized covariance estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(4), pages 625-645, June.
    18. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    19. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    20. Eom, Cheoljun & Park, Jong Won, 2018. "A new method for better portfolio investment: A case of the Korean stock market," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 213-231.

    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:jfinte:v:4:y:2025:i:2:p:14-:d:1626403. 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.