Deep Learning for Predicting Asset Returns

Citations

Cited by:

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7. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
8. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
9. Cheng, Tingting & Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2024. "GMM estimation for high-dimensional panel data models," Journal of Econometrics, Elsevier, vol. 244(1).
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10. Lin, Weidong & Taamouti, Abderrahim, 2024. "Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1179-1188.
    • Weidong Lin & Abderrahim Taamouti, 2023. "Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach," Working Papers 202310, University of Liverpool, Department of Economics.
11. Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
12. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
    • Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
13. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
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14. Özgür Ömer Ersin & Melike Bildirici, 2023. "Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19," Mathematics, MDPI, vol. 11(8), pages 1-26, April.
15. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.
16. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
17. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
18. Sak, Halis & Huang, Tao & Chng, Michael T., 2024. "Exploring the factor zoo with a machine-learning portfolio," International Review of Financial Analysis, Elsevier, vol. 96(PA).
19. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
20. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
21. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
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23. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
24. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR), revised 2025.
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26. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
27. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
28. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(1), pages 1-60, February.
29. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
30. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
31. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.