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Empirical Asset Pricing via Machine Learning

Citations

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Cited by:

  1. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
  2. Yonghe Lu & Yanrong Yang & Terry Zhang, 2024. "Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy," Papers 2411.18830, arXiv.org.
  3. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
  4. Cakici, Nusret & Zaremba, Adam, 2024. "What drives stock returns across countries? Insights from machine learning models," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  5. Dong, Mengmeng, 2025. "Economic aggregation of return signals in global markets," Journal of Empirical Finance, Elsevier, vol. 84(C).
  6. Ruoyu Guo & Haochen Qiu & Xuelun Hou, 2025. "A Novel Loss Function for Deep Learning Based Daily Stock Trading System," Papers 2502.17493, arXiv.org, revised Nov 2025.
  7. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness," Papers 2010.08463, arXiv.org, revised Nov 2025.
  8. Baba-Yara, Fahiz & Boons, Martijn & Tamoni, Andrea, 2024. "Persistent and transitory components of firm characteristics: Implications for asset pricing," Journal of Financial Economics, Elsevier, vol. 154(C).
  9. Wu, Ruike & Yang, Yanrong & Shang, Han Lin & Zhu, Huanjun, 2025. "Making distributionally robust portfolios feasible in high dimension," Journal of Econometrics, Elsevier, vol. 252(PA).
  10. Min, Byoung-Kyu & Roh, Tai-Yong, 2025. "Can machine learning uncover abnormal returns in uncharted financial territories?," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
  11. Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024. "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, vol. 94(C).
  12. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  13. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
  14. Zeng, Qing & Lu, Xinjie & Xu, Jin & Lin, Yu, 2024. "Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach," International Review of Financial Analysis, Elsevier, vol. 96(PB).
  15. Amit Goyal & Alessio Saretto, 2022. "Are Equity Option Returns Abnormal? IPCA Says No," Working Papers 2214, Federal Reserve Bank of Dallas.
  16. 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.
  17. Jian Xue & Qian Zhang & Wu Zhu, 2025. "Generative AI for Analysts," Papers 2512.19705, arXiv.org.
  18. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
  19. Muhammad Abro & Hassan Jaleel, 2026. "Regret-Driven Portfolios: LLM-Guided Smart Clustering for Optimal Allocation," Papers 2601.17021, arXiv.org.
  20. 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).
  21. Kuppenheimer, Gregory & Shelly, Stuart & Strauss, Jack, 2023. "Can machine learning identify sector-level financial ratios that predict sector returns?," Finance Research Letters, Elsevier, vol. 57(C).
  22. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Zhao, Junyi, 2025. "Is machine learning a necessity? A regression-based approach for stock return prediction," Journal of Empirical Finance, Elsevier, vol. 81(C).
  23. 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.
  24. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
  25. Zhang, Qiuyue & Que, Jiangjing & Qin, Xiuting, 2023. "Regional financial technology and shadow banking activities of non-financial firms: Evidence from China," Journal of Asian Economics, Elsevier, vol. 86(C).
  26. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
  27. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
  28. 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).
  29. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  30. 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.
  31. Maher Hamid, 2026. "Implementing domain-specific LLMs for strategic investment decisions: a retrospective case study comparing AI and human expertise," Digital Finance, Springer, vol. 8(1), pages 1-134, March.
  32. Wu, Haoran & Gao, Zhiwei & Nie, Boyang & Zhao, Binru, 2025. "Can machines learn Chinese mutual funds?," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
  33. 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).
  34. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
  35. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.
  36. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  37. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
  38. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
  39. Tobias Götze & Marc Gürtler & Eileen Witowski, 0. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 0, pages 1-19.
  40. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
  41. Ioannis Paraskevopoulos & Alvaro Santos, 2025. "The Stochastic Evolution of Financial Asset Prices," Mathematics, MDPI, vol. 13(12), pages 1-24, June.
  42. D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
  43. Bryan Kelly & Semyon Malamud & Lasse Heje Pedersen, 2023. "Principal Portfolios," Journal of Finance, American Finance Association, vol. 78(1), pages 347-387, February.
  44. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
  45. Craig S Wright, 2026. "Utility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions," Papers 2601.07852, arXiv.org.
  46. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
  47. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.
  48. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
  49. Lin William Cong & Ke Tang & Jingyuan Wang & Yang Zhang, 2021. "Deep Sequence Modeling: Development and Applications in Asset Pricing," Papers 2108.08999, arXiv.org.
  50. Hu, Genhua & Ma, Xiaoqing & Zhu, Tingting, 2025. "Forecasting volatility of China’s crude oil futures based on hybrid ML-HAR-RV models," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
  51. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2025. "Predicting IPO first-day returns: Evidence from machine learning analyses," Journal of Banking & Finance, Elsevier, vol. 178(C).
  52. Prabhu Prasad Panda & Maysam Khodayari Gharanchaei & Xilin Chen & Haoshu Lyu, 2024. "Application of Deep Learning for Factor Timing in Asset Management," Papers 2404.18017, arXiv.org.
  53. Cui, Mengqi & Li, Daye, 2024. "A four-factor model based on factor momentum," Pacific-Basin Finance Journal, Elsevier, vol. 87(C).
  54. 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).
  55. , & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," CEPR Discussion Papers 16357, C.E.P.R. Discussion Papers.
  56. Yao Wang & Jingmei Zhao & Qing Li & Xiangyu Wei, 2024. "Considering momentum spillover effects via graph neural network in option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 1069-1094, June.
  57. Avramov, Doron & Li, Minwen & Wang, Hao, 2021. "Predicting corporate policies using downside risk: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 1-26.
  58. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
  59. Guanhao Feng & Jingyu He & Nicholas G. Polson, 2018. "Deep Learning for Predicting Asset Returns," Papers 1804.09314, arXiv.org, revised Apr 2018.
  60. Xiang Ao & Jingxuan Zhang & Xinyu Zhao, 2026. "Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks," Papers 2604.09650, arXiv.org.
  61. Michael Pinelis & David Ruppert, 2020. "Machine Learning Portfolio Allocation," Papers 2003.00656, arXiv.org, revised Nov 2021.
  62. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
  63. Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 657-684, April.
  64. Mike Kraehenbuehl & Joerg Osterrieder, 2022. "The Efficient Market Hypothesis for Bitcoin in the context of neural networks," Papers 2208.07254, arXiv.org.
  65. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
  66. Hasan Fallahgoul, 2025. "High-Dimensional Learning in Finance," Papers 2506.03780, arXiv.org, revised Jul 2025.
  67. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
  68. 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).
  69. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
  70. 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.
  71. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
  72. Mike Lindow & David DeFranza & Arul Mishra & Himanshu Mishra, 2021. "Scared into Action: How Partisanship and Fear are Associated with Reactions to Public Health Directives," Papers 2101.05365, arXiv.org.
  73. 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.
  74. Xiaohong Chen & Yuan Liao & Weichen Wang, 2022. "Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves," Papers 2301.00092, arXiv.org, revised Jan 2023.
  75. 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.
  76. Jinghai He & Cheng Hua & Chunyang Zhou & Zeyu Zheng, 2025. "Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information," Papers 2501.17992, arXiv.org.
  77. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2023. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 126151, London School of Economics and Political Science, LSE Library.
  78. Song, Yingying & Chen, Xinxin, 2025. "Which opinion is more trustworthy: An analysts’ earnings forecast quality assessment framework based on machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 75(PB).
  79. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
  80. Yuan, Ying & Qu, Yong & Wang, Tianyang, 2025. "Predicting risk premiums: A constraint-based model," Journal of Empirical Finance, Elsevier, vol. 83(C).
  81. Yujie Ding & Shuai Jia & Tianyi Ma & Bingcheng Mao & Xiuze Zhou & Liuliu Li & Dongming Han, 2023. "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction," Papers 2310.05627, arXiv.org.
  82. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
  83. Wei, Mingye & Zhang, Min & Wei, Lu & Chen, Meiqi, 2025. "IPOhelper: Mining features in registration statements for listing prediction of technological innovation companies," Emerging Markets Review, Elsevier, vol. 68(C).
  84. Stavros Degiannakis & Panagiotis Delis & George Filis & George Giannopoulos, 2025. "Trading VIX on Volatility Forecasts: Another Volatility Puzzle?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1602-1618, July.
  85. Pedro Isaac Chavez-Lopez & Tae-Hwy Lee, 2025. "Quantile-Covariance Three-Pass Regression Filter," Working Papers 202501, University of California at Riverside, Department of Economics.
  86. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  87. Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
  88. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Benign Overfitting in Economic Forecasting via Noise Regularization," Papers 2312.05593, arXiv.org, revised Apr 2026.
  89. 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.
  90. Rabeh Khalfaoui & Sami Ben Jabeur & Shawkat Hammoudeh & Wissal Ben Arfi, 2025. "The role of political risk, uncertainty, and crude oil in predicting stock markets: evidence from the UAE economy," Annals of Operations Research, Springer, vol. 345(2), pages 1105-1135, February.
  91. Chendi Ni & Yuying Li & Peter Forsyth & Ray Carroll, 2020. "Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target," Papers 2006.15384, arXiv.org.
  92. Bolin Mao & Chenhui Chu & Yuta Nakashima & Hajime Nagahara, 2022. "Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models," KIER Working Papers 1082, Kyoto University, Institute of Economic Research.
  93. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.
  94. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
  95. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  96. 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).
  97. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  98. 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.
  99. Chariton Chalvatzis & Dimitrios Hristu-Varsakelis, 2019. "High-performance stock index trading: making effective use of a deep LSTM neural network," Papers 1902.03125, arXiv.org, revised May 2019.
  100. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
  101. Lisa R. Goldberg & Saad Mouti, 2019. "Sustainable Investing and the Cross-Section of Returns and Maximum Drawdown," Papers 1905.05237, arXiv.org, revised Dec 2023.
  102. Jing Hao & Feng He & Feng Ma & Shibo Zhang & Xiaotao Zhang, 2025. "Machine learning vs deep learning in stock market investment: an international evidence," Annals of Operations Research, Springer, vol. 348(1), pages 93-115, May.
  103. Huo, Da & Shi, Yongdong & Wang, Chao & Wang, Lihan & Xing, Weize & Yang, Mo & Zhao, Jingjing, 2025. "Measuring systemic risk in China: A new hybrid approach incorporating ensemble learning and risk spillover networks," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
  104. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
  105. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
  106. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 61(1), pages 329-376, March.
  107. Yilie Huang & Yanwei Jia & Xun Yu Zhou, 2024. "Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study," Papers 2412.16175, arXiv.org, revised Mar 2026.
  108. Josef Novotny & Petr Hajek, 2026. "A hybrid adaptive trading strategy integrating investor sentiment for precious metal ETFs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-33, December.
  109. Jing Wu & Zhaocheng Zhang & Sean X. Zhou, 2022. "Credit Rating Prediction Through Supply Chains: A Machine Learning Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1613-1629, April.
  110. Lof, Matthijs & Nyberg, Henri, 2024. "Discount rates and cash flows: A local projection approach," Journal of Banking & Finance, Elsevier, vol. 162(C).
  111. Veena Jain & Rishi Rajan Sahay & Nupur, 2024. "Multi-verse metaheuristic and deep learning approach for portfolio selection with higher moments," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(5), pages 1956-1970, May.
  112. 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.
  113. Angelidis, Timotheos & Sakkas, Athanasios & Tessaromatis, Nikolaos, 2025. "Predicting commodity returns: Time series vs. cross sectional prediction models," Journal of Commodity Markets, Elsevier, vol. 38(C).
  114. Yanlong Wang & Jian Xu & Tiantian Gao & Hongkang Zhang & Shao-Lun Huang & Danny Dongning Sun & Xiao-Ping Zhang, 2025. "FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models," Papers 2503.06928, arXiv.org, revised Jun 2025.
  115. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
  116. Goulet Coulombe Philippe, 2025. "To Bag is to Prune," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(6), pages 669-697.
  117. Byun, Suk-Joon & Cho, Sangheum & Kim, Da-Hea, 2024. "Can a machine learn from behavioral biases? Evidence from stock return predictability of deep learning models," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
  118. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
  119. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
  120. 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.
  121. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
  122. Akash Deep & Abootaleb Shirvani & Chris Monico & Svetlozar Rachev & Frank J. Fabozzi, 2024. "Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading," Papers 2412.15448, arXiv.org, revised Feb 2025.
  123. Peter Andre & Philipp Schirmer & Johannes Wohlfart, 2023. "Mental Models of the Stock Market," CESifo Working Paper Series 10691, CESifo.
  124. BBVA Research & Alvaro Ortiz & Tomasa Rodrigo, 2025. "Global | Geopolítica, geoeconomía y riesgo: un enfoque basado en aprendizaje automático [Global | Geopolitics, geoeconomics and risk: a machine learning approach]," Working Papers 25/14, BBVA Bank, Economic Research Department.
  125. Ylinen, Mika & Ranta, Mikko, 2025. "Predicting corporate innovation using machine learning and social media data," Technovation, Elsevier, vol. 148(C).
  126. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
  127. Peng, Yaohao & Nagata, Mateus Hiro, 2020. "An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
  128. Vitor Azevedo & Georg Sebastian Kaiser & Sebastian Mueller, 2023. "Stock market anomalies and machine learning across the globe," Journal of Asset Management, Palgrave Macmillan, vol. 24(5), pages 419-441, September.
  129. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
  130. Sergio Consoli & Luca Tiozzo Pezzoli & Elisa Tosetti, 2022. "Neural forecasting of the Italian sovereign bond market with economic news," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 197-224, December.
  131. Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024. "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, vol. 78(C).
  132. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
  133. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
  134. Mekelburg, Erik & Strauss, Jack, 2024. "Pooling and winsorizing machine learning forecasts to predict stock returns with high-dimensional data," Journal of Empirical Finance, Elsevier, vol. 79(C).
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  737. Oleg Roshka, 2026. "Global Persistence, Local Residual Structure: Forecasting Heterogeneous Investment Panels," Papers 2604.09821, arXiv.org, revised May 2026.
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  744. 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.
  745. Tatsuru Kikuchi, 2026. "The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector," Papers 2602.02607, arXiv.org.
  746. Yun-Shi Dai & Peng-Fei Dai & St'ephane Goutte & Duc Khuong Nguyen & Wei-Xing Zhou, 2025. "Multiscale risk spillovers and external driving factors: Evidence from the global futures and spot markets of staple foods," Papers 2501.15173, arXiv.org.
  747. David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.
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