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Option Return Predictability with Machine Learning and Big Data

Citations

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Cited by:

  1. Wang, Jianqiu & Wu, Ke & Yang, Sijie & Zhou, Dexin, 2024. "Asymmetry and the Cross-section of Option Returns," Journal of Financial Markets, Elsevier, vol. 71(C).
  2. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
  3. Kexin Wang & Xiaomeng Zhang & Xinyu Zhang, 2025. "Portfolio Optimization via Transfer Learning," Papers 2511.21221, arXiv.org.
  4. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
  5. Maxim Ulrich & Lukas Zimmer & Constantin Merbecks, 2023. "Implied volatility surfaces: a comprehensive analysis using half a billion option prices," Review of Derivatives Research, Springer, vol. 26(2), pages 135-169, October.
  6. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.
  7. Cakici, Nusret & Zaremba, Adam, 2025. "Accounting vs technical information: what matters more for stock return predictability?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 104(C).
  8. Alessio Brini & David A. Hsieh & Patrick Kuiper & Sean Moushegian & David Ye, 2025. "Empirical Models of the Time Evolution of SPX Option Prices," Papers 2506.17511, arXiv.org.
  9. DeMiguel, Victor & Gil-Bazo, Javier & Nogales, Francisco J. & Santos, André A.P., 2023. "Machine learning and fund characteristics help to select mutual funds with positive alpha," Journal of Financial Economics, Elsevier, vol. 150(3).
  10. Matteo Aquilina & Douglas Kiarelly Godoy de Araujo & Gaston Gelos & Taejin Park & Fernando Perez-Cruz, 2025. "Harnessing artificial intelligence for monitoring financial markets," BIS Working Papers 1291, Bank for International Settlements.
  11. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, 2026. "Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability," Working Papers wp61, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Feb 2026.
  12. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
  13. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  14. Hess, Dieter & Simon, Frederik & Weibels, Sebastian, 2025. "Interpretable machine learning for earnings forecasts: Leveraging high-dimensional financial statement data," CFR Working Papers 25-06, University of Cologne, Centre for Financial Research (CFR).
  15. Fausch, Jürg & Frigg, Moreno & Ruenzi, Stefan & Weigert, Florian, 2026. "Machine learning mutual fund flows," CFR Working Papers 26-03, University of Cologne, Centre for Financial Research (CFR).
  16. Huang, Xinyu & Newton, David P. & Platanakis, Emmanouil & Sutcliffe, Charles, 2025. "Single-stage portfolio optimization with automated machine learning for M6," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1450-1460.
  17. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
  18. Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Forecasting stock return distributions around the globe with quantile neural networks," Papers 2408.07497, arXiv.org, revised Aug 2025.
  19. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
  20. Beckmeyer, Heiner & Wiedemann, Timo, 2025. "All Days Are Not Created Equal: Understanding Momentum by Learning to Weight Past Returns," Journal of Banking & Finance, Elsevier, vol. 181(C).
  21. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2024. "Machine-learning stock market volatility: Predictability, drivers, and economic value," International Review of Financial Analysis, Elsevier, vol. 94(C).
  22. Wen, Conghua & Zhai, Jia & Wang, Yinuo & Cao, Yi, 2024. "Implied volatility is (almost) past-dependent: Linear vs non-linear models," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  23. Murphy, Brid & Feeney, Orla & Rosati, Pierangelo & Lynn, Theo, 2024. "Exploring accounting and AI using topic modelling," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
  24. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2024. "Deep Learning for Options Trading: An End-To-End Approach," Papers 2407.21791, arXiv.org.
  25. Li, Bin & Rossi, Alberto G. & Yan, Xuemin (Sterling) & Zheng, Lingling, 2025. "Machine learning from a “Universe” of signals: The role of feature engineering," Journal of Financial Economics, Elsevier, vol. 172(C).
  26. Hyung Joo Kim & Dong Hwan Oh, 2025. "Local Estimation for Option Pricing: Improving Forecasts with Market State Information," Finance and Economics Discussion Series 2025-076, Board of Governors of the Federal Reserve System (U.S.).
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