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Big Data in Finance

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 articles 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 articles 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," NBER Working Papers 28615, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28615
    Note: AP CF
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    1. 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.
    2. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Wang, Sai & Wen, Wen & Niu, Yuhao & Li, Xin, 2024. "Digital transformation and corporate labor investment efficiency," Emerging Markets Review, Elsevier, vol. 59(C).
    4. Hałaj, Grzegorz & Martinez-Jaramillo, Serafin & Battiston, Stefano, 2024. "Financial stability through the lens of complex systems," Journal of Financial Stability, Elsevier, vol. 71(C).
    5. 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.
    6. 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).
    7. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    8. 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).
    9. 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).
    10. 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.
    11. 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.
    12. 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).
    13. 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).

    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

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