IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v34y2021i7p3213-3225..html
   My bibliography  Save this article

Big Data in Finance
[Institutional order handling and broker-affiliated trading venues]

Author

Listed:
  • Itay Goldstein
  • Chester S Spatt
  • Mao Ye

Abstract

Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains papers following the 2019 NBER-RFS Conference on Big Data. In this introduction to the special issue, we define the “big data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the papers in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance—including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.

Suggested Citation

  • Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance [Institutional order handling and broker-affiliated trading venues]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3213-3225.
    • Handle: RePEc:oup:rfinst:v:34:y:2021:i:7:p:3213-3225.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhab038
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    2. Wang, Sai & Wen, Wen & Niu, Yuhao & Li, Xin, 2024. "Digital transformation and corporate labor investment efficiency," Emerging Markets Review, Elsevier, vol. 59(C).
    3. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    4. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    5. Edmans, Alex & Fernandez-Perez, Adrian & Garel, Alexandre & Indriawan, Ivan, 2022. "Music sentiment and stock returns around the world," Journal of Financial Economics, Elsevier, vol. 145(2), pages 234-254.
    6. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    7. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    8. Day, Min-Yuh & Ni, Yensen, 2023. "Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes," International Review of Financial Analysis, Elsevier, vol. 90(C).
    9. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers 2306.10224, arXiv.org, revised Feb 2024.
    10. Arnold, Lutz G. & Russ, David, 2024. "Listening to the noise: On price efficiency with dynamic trading," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 103-120.
    11. Wang, Yichen & Hu, Jun & Chen, Jia, 2023. "Does Fintech facilitate cross-border M&As? Evidence from Chinese A-share listed firms," International Review of Financial Analysis, Elsevier, vol. 85(C).
    12. Niu, Yuhao & Wang, Sai & Wen, Wen & Li, Sifei, 2023. "Does digital transformation speed up dynamic capital structure adjustment? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    13. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
    14. Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    15. Nam, Rachel J., 2022. "Open banking and customer data sharing: Implications for FinTech borrowers," SAFE Working Paper Series 364, Leibniz Institute for Financial Research SAFE.
    16. Hałaj, Grzegorz & Martinez-Jaramillo, Serafin & Battiston, Stefano, 2024. "Financial stability through the lens of complex systems," Journal of Financial Stability, Elsevier, vol. 71(C).
    17. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    18. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.

    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G3 - Financial Economics - - Corporate Finance and Governance

    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:oup:rfinst:v:34:y:2021:i:7:p:3213-3225.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.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.