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Autoencoder asset pricing models

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Cited by:

  1. Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury & Ajim Uddin & Syed Moudud‐Ul‐Huq, 2023. "Forecasting nonperforming loans using machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1664-1689, November.
  2. 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).
  3. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
  4. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
  5. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
  6. Adcock, Christopher & Bessler, Wolfgang & Conlon, Thomas, 2022. "Characteristic-sorted portfolios and macroeconomic risks—An orthogonal decomposition," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 24-50.
  7. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
  8. Bruno Spilak & Wolfgang Karl Hardle, 2022. "Risk budget portfolios with convex Non-negative Matrix Factorization," Papers 2204.02757, arXiv.org, revised Jun 2023.
  9. Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
  10. David Alaminos & Ignacio Esteban & M. Belén Salas, 2023. "Neural networks for estimating Macro Asset Pricing model in football clubs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 57-75, April.
  11. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
  12. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  13. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
  14. Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
  15. Castro-Iragorri, C & Ramírez, J, 2021. "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo 19431, Universidad del Rosario.
  16. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
  17. Zikai Wei & Anyi Rao & Bo Dai & Dahua Lin, 2023. "HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE," Papers 2306.02848, arXiv.org.
  18. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
  19. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
  20. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
  21. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "On statistical arbitrage under a conditional factor model of equity returns," Papers 2309.02205, arXiv.org.
  22. Ni Zhan & Yijia Sun & Aman Jakhar & He Liu, 2021. "Graphical Models for Financial Time Series and Portfolio Selection," Papers 2101.09214, arXiv.org.
  23. Yilun Wang & Shengjie Guo, 2024. "RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction," Papers 2403.02500, arXiv.org.
  24. 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.
  25. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
  26. Daniel Poh & Stephen Roberts & Stefan Zohren, 2022. "Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity," Papers 2208.09968, arXiv.org, revised Feb 2023.
  27. Peter B. Lerner, 2022. "Fourier Integral Operator Model of Market Liquidity: The Chinese Experience 2009–2010," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
  28. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
  29. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  30. Li Rong Wang & Hsuan Fu & Xiuyi Fan, 2023. "Stock Price Predictability and the Business Cycle via Machine Learning," Papers 2304.09937, arXiv.org.
  31. Qihui Chen, 2022. "A Unified Framework for Estimation of High-dimensional Conditional Factor Models," Papers 2209.00391, arXiv.org.
  32. Jiti Gao & Bin Peng & Yanrong Yang, 2023. "A Localized Neural Network with Dependent Data: Estimation and Inference," Papers 2306.05593, arXiv.org.
  33. Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023. "Deep Neural Network Estimation in Panel Data Models," Papers 2305.19921, arXiv.org.
  34. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Higher-order Expansions and Inference for Panel Data Models," Papers 2205.00577, arXiv.org, revised Jun 2023.
  35. 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.
  36. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
  37. Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
  38. Doron Avramov & Si Cheng & Lior Metzker & Stefan Voigt, 2023. "Integrating Factor Models," Journal of Finance, American Finance Association, vol. 78(3), pages 1593-1646, June.
  39. 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.
  40. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
  41. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
  42. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
  43. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
  44. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
  45. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org.
  46. Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
  47. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
  48. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
  49. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
  50. Ioana Boier, 2022. "Multiresolution Signal Processing of Financial Market Objects," Papers 2210.15934, arXiv.org, revised Nov 2022.
  51. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
  52. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
  53. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  54. Kazuki Amagai & Tomoya Suzuki, 2023. "Long-Term Modeling of Financial Machine Learning for Active Portfolio Management," Papers 2301.12346, arXiv.org.
  55. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
  56. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
  57. Hector O. Zapata & Supratik Mukhopadhyay, 2022. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing," JRFM, MDPI, vol. 15(11), pages 1-17, November.
  58. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  59. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
  60. Nozomu Kobayashi & Yoshiyuki Suimon & Koichi Miyamoto & Kosuke Mitarai, 2023. "The cross-sectional stock return predictions via quantum neural network and tensor network," Papers 2304.12501, arXiv.org, revised Feb 2024.
  61. 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.
  62. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
  63. 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.
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