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Deep Learning in Asset Pricing

Citations

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Cited by:

  1. Adam Bouland & Wim van Dam & Hamed Joorati & Iordanis Kerenidis & Anupam Prakash, 2020. "Prospects and challenges of quantum finance," Papers 2011.06492, arXiv.org.
  2. Alessi, Lucia & Ossola, Elisa & Panzica, Roberto, 2023. "When do investors go green? Evidence from a time-varying asset-pricing model," International Review of Financial Analysis, Elsevier, vol. 90(C).
  3. Ma, Tian & Wang, Wanwan & Chen, Yu, 2023. "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
  4. 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.
  5. 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).
  6. So-Yoon Cho & Jin-Young Kim & Kayoung Ban & Hyeng Keun Koo & Hyun-Gyoon Kim, 2025. "Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction," Papers 2511.07014, arXiv.org, revised Mar 2026.
  7. Qihui Chen & Nikolai Roussanov & Xiaoliang Wang, 2021. "Semiparametric Conditional Factor Models in Asset Pricing," Papers 2112.07121, arXiv.org, revised Apr 2025.
  8. Zhang, Zhi-Yu & Xie, Chi & Wang, Gang-Jin & Zhu, You & Li, Xiao-Xin, 2025. "From noise to signals: Investor attention as a catalyst for the momentum effect in the Chinese stock market," Global Finance Journal, Elsevier, vol. 67(C).
  9. Hasan Fallahgoul, 2025. "High-Dimensional Learning in Finance," Papers 2506.03780, arXiv.org, revised Jul 2025.
  10. Sungwoo Kang, 2026. "The Limits of Complexity: Why Feature Engineering Beats Deep Learning in Investor Flow Prediction," Papers 2601.07131, arXiv.org.
  11. Jeonggyu Huh & Seungwon Jeong & Hyun-Gyoon Kim & Hyeng Keun Koo & Byung Hwa Lim, 2026. "MarketGANs: Multivariate financial time-series data augmentation using generative adversarial networks," Papers 2601.17773, arXiv.org.
  12. Yanlong Wang & Jian Xu & Shao-Lun Huang & Danny Dongning Sun & Xiao-Ping Zhang, 2025. "Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method," Papers 2503.06929, arXiv.org.
  13. Szymon Lis & Robert 'Slepaczuk & Pawe{l} Sakowski, 2026. "Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks," Papers 2602.18912, arXiv.org.
  14. 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.
  15. Shanyan Lai, 2025. "Asset Pricing in Pre-trained Transformer," Papers 2505.01575, arXiv.org, revised May 2025.
  16. Changeun Kim & Younwoo Jeong & Bong-Gyu Jang, 2025. "Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model," Papers 2512.16251, arXiv.org, revised Apr 2026.
  17. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
  18. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
  19. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  20. Caner, Mehmet & Daniele, Maurizio, 2025. "Deep learning based residuals in non-linear factor models: Precision matrix estimation of returns with low signal-to-noise ratio," Journal of Econometrics, Elsevier, vol. 251(C).
  21. Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025. "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, vol. 98(C).
  22. Witter, Johannes, 2025. "Predicting stock returns with machine learning: Global versus sector models," Junior Management Science (JUMS), Junior Management Science e. V., vol. 10(3), pages 561-581.
  23. Yuxiao Jiao & Guofu Zhou & Wu Zhu & Yingzi Zhu, 2025. "Interpretable Factors of Firm Characteristics," Papers 2508.02253, arXiv.org.
  24. Zefeng Chen & Darcy Pu, 2026. "Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns," Papers 2601.11958, arXiv.org.
  25. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
  26. Shanyan Lai, 2025. "Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks," Papers 2505.01921, arXiv.org, revised May 2025.
  27. Minshuo Chen & Renyuan Xu & Yumin Xu & Ruixun Zhang, 2025. "Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure," Papers 2504.06566, arXiv.org, revised Jan 2026.
  28. Cao, Xinrui & Zeng, Xianpeng, 2025. "Can registration system reform mitigate asset mispricing?," Finance Research Letters, Elsevier, vol. 83(C).
  29. Tian Guo & Emmanuel Hauptmann, 2025. "Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction," Papers 2510.15691, arXiv.org, revised Nov 2025.
  30. Doron Avramov & Xin He, 2026. "Stochastic Discount Factors with Cross-Asset Spillovers," Papers 2602.20856, arXiv.org.
  31. 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).
  32. Wang, Jianqiu & Wang, Zhuo & Wu, Ke, 2025. "Forecasting stock market return with anomalies: Evidence from China," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1278-1295.
  33. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
  34. Anastasis Kratsios & Cody Hyndman, 2020. "Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization," Risks, MDPI, vol. 8(2), pages 1-30, April.
  35. Nechvátalová, Lenka, 2025. "Autoencoder asset pricing models and economic restrictions — international evidence," International Review of Financial Analysis, Elsevier, vol. 107(C).
  36. Jian'an Zhang, 2025. "FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management," Papers 2510.02986, arXiv.org.
  37. Elliot L. Epstein & Apaar Sadhwani & Kay Giesecke, 2025. "A Set-Sequence Model for Time Series," Papers 2505.11243, arXiv.org, revised Oct 2025.
  38. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Jan 2026.
  39. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
  40. repec:cam:camjip:2506 is not listed on IDEAS
  41. Chai, Bailin & Jiang, Fuwei & Lin, Yihao & You, Tian, 2025. "Predicting bond risk premiums with machine learning: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
  42. repec:bge:wpaper:1245 is not listed on IDEAS
  43. Bhaskar Goswami & Ajim Uddin, 2026. "Significance of predictors: revisiting stock return predictions using explainable AI," Annals of Operations Research, Springer, vol. 357(1), pages 223-257, February.
  44. Shunyao Wang & Ming Cheng & Christina Dan Wang, 2025. "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks," Papers 2505.06864, arXiv.org.
  45. Mohamed Ben Alaya & Ahmed Kebaier & Djibril Sarr, 2021. "Deep Calibration of Interest Rates Model," Papers 2110.15133, arXiv.org, revised Sep 2024.
  46. Elysee Nsengiyumva & Joseph K. Mung’atu & Charles Ruranga, 2025. "Hybrid GARCH-LSTM Forecasting for Foreign Exchange Risk," FinTech, MDPI, vol. 4(2), pages 1-17, June.
  47. Shanyan Lai, 2025. "Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models," Papers 2508.19006, arXiv.org.
  48. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
  49. Hangyi Zhao, 2026. "Insider Purchase Signals in Microcap Equities: Gradient Boosting Detection of Abnormal Returns," Papers 2602.06198, arXiv.org.
  50. 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.
  51. Federico Vittorio Cortesi & Giuseppe Iannone & Giulia Crippa & Tomaso Poggio & Pierfrancesco Beneventano, 2026. "Same Error, Different Function: The Optimizer as an Implicit Prior in Financial Time Series," Papers 2603.02620, arXiv.org.
  52. Maung, Kenwin & Swanson, Norman R., 2025. "A survey of models and methods used for forecasting when investing in financial markets," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1355-1382.
  53. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
  54. Jiajun Gu & Zichen Yang & Xintong Lin & Sixun Chen & YuTing Lu, 2024. "AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics," Papers 2412.12438, arXiv.org.
  55. Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
  56. 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.
  57. Eghbal Rahimikia & Hao Ni & Weiguan Wang, 2025. "Re(Visiting) Time Series Foundation Models in Finance," Papers 2511.18578, arXiv.org.
  58. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
  59. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, "undated". "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.
  60. Avramov, D. & Ge, S. & Li, S. & Linton, O. B., 2025. "Dual Industry Effects and Cross-Stock Predictability," Cambridge Working Papers in Economics 2512, Faculty of Economics, University of Cambridge.
  61. Yan Liu & Ye Luo & Zigan Wang & Xiaowei Zhang, 2026. "Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning," Papers 2601.00593, arXiv.org.
  62. Kang, Hyoung-Goo & Ryu, Doojin, 2025. "A complementary valuation model and exit multiples," Finance Research Letters, Elsevier, vol. 79(C).
  63. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
  64. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
  65. 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).
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