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Market efficiency in the age of big data

Citations

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Cited by:

  1. Dohyun Chun & Jongho Kang & Jihun Kim, 2024. "Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
  2. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
  3. Melina & Sukono & Herlina Napitupulu & Norizan Mohamed, 2023. "A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review," Risks, MDPI, vol. 11(3), pages 1-24, March.
  4. Olivier Dessaint & Thierry Foucault & Laurent Fresard, 2024. "Does Alternative Data Improve Financial Forecasting? The Horizon Effect," Journal of Finance, American Finance Association, vol. 79(3), pages 2237-2287, June.
  5. Zhang, Junsheng & Peng, Zezhi & Zeng, Yamin & Yang, Haisheng, 2023. "Do big data mutual funds outperform?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
  6. Wang, Jing & Yu, Huaying & Ren, Daowen & Zhang, Jocelyn, 2023. "Promoting mineral resources consumption efficiency: Evidence from technology of big data," Resources Policy, Elsevier, vol. 86(PB).
  7. 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).
  8. Jérôme Dugast & Thierry Foucault, 2020. "Equilibrium Data Mining and Data Abundance," Working Papers hal-03053967, HAL.
  9. 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.
  10. Bo Yan & Mengru Liang & Yinxin Zhao, 2024. "Market sentiment and price dynamics in weak markets: A comprehensive empirical analysis of the soybean meal option market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(5), pages 744-766, May.
  11. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
  12. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  13. Bryan Kelly & Semyon Malamud & Kangying Zhou, 2024. "The Virtue of Complexity in Return Prediction," Journal of Finance, American Finance Association, vol. 79(1), pages 459-503, February.
  14. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
  15. Yabu, Takuya, 2023. "On Discrete Probability Distributions to Grasp the Number of Samples in a Population," OSF Preprints yv24f, Center for Open Science.
  16. Jérôme Dugast & Thierry Foucault, 2024. "Equilibrium Data Mining and Data Abundance," Post-Print hal-04941346, HAL.
  17. Yabu, Takuya, 2023. "On Discrete Probability Distributions to Grasp the Number of Samples in a Population," OSF Preprints yv24f_v1, Center for Open Science.
  18. Kaplanski, Guy, 2023. "The race to exploit anomalies and the cost of slow trading," Journal of Financial Markets, Elsevier, vol. 62(C).
  19. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org, revised Sep 2024.
  20. Goodarzi, Milad & Meinerding, Christoph, 2023. "Asset allocation with recursive parameter updating and macroeconomic regime identifiers," Discussion Papers 06/2023, Deutsche Bundesbank.
  21. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
  22. Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020. "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics 130, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
  23. Garg, Karan, 2021. "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers 11, Warwick Monash Economics Student Papers.
  24. Wu, Fei & Hu, Yan & Shen, Me, 2024. "The color of FinTech: FinTech and corporate green transformation in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
  25. Matteo Bagnara, 2024. "Asset Pricing and Machine Learning: A critical review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 27-56, February.
  26. 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.
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