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Microstructure in the Machine Age

In: Big Data: Long-Term Implications for Financial Markets and Firms

Author

Listed:
  • David Easley
  • Marcos López de Prado
  • Maureen O’Hara
  • Zhibai Zhang

Abstract

No abstract is available for this item.

Suggested Citation

  • David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang, 2021. "Microstructure in the Machine Age," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3316-3363, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14602
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    Citations

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    Cited by:

    1. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
    2. Kara Karpman & Sumanta Basu & David Easley, 2022. "Learning Financial Networks with High-frequency Trade Data," Papers 2208.03568, arXiv.org.
    3. Zhimeng Yang & Ariah Klages-Mundt & Lewis Gudgeon, 2023. "Oracle Counterpoint: Relationships between On-chain and Off-chain Market Data," Papers 2303.16331, arXiv.org, revised Jul 2023.
    4. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    5. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    6. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    7. Lauter, Tobias & Prokopczuk, Marcel, 2022. "Measuring commodity market quality," Journal of Banking & Finance, Elsevier, vol. 145(C).
    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).

    More about this item

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G19 - Financial Economics - - General Financial Markets - - - Other

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