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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. Zhennan Wu, 2022. "Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1607-1625, April.
  3. 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.
  4. 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).
  5. Li Rong Wang & Hsuan Fu & Xiuyi Fan, 2023. "Stock Price Predictability and the Business Cycle via Machine Learning," Papers 2304.09937, arXiv.org.
  6. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
  7. Daniel Cunha Oliveira & Yutong Lu & Xi Lin & Mihai Cucuringu & Andre Fujita, 2024. "Causality-Inspired Models for Financial Time Series Forecasting," Papers 2408.09960, arXiv.org.
  8. Jia, Yuecheng & Wu, Yangru & Yan, Shu & Liu, Yuzheng, 2023. "A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 74(C).
  9. 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).
  10. Wan, Runzhe & Li, Yingying & Lu, Wenbin & Song, Rui, 2024. "Mining the factor zoo: Estimation of latent factor models with sufficient proxies," Journal of Econometrics, Elsevier, vol. 239(2).
  11. 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).
  12. Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
  13. Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024. "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, vol. 94(C).
  14. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2021. "Fundamental ratios as predictors of ESG scores: a machine learning approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1087-1110, December.
  15. Freire, Gustavo, 2021. "Tail risk and investors’ concerns: Evidence from Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  16. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
  17. Amit Goyal & Alessio Saretto, 2022. "Are Equity Option Returns Abnormal? IPCA Says No," Working Papers 2214, Federal Reserve Bank of Dallas.
  18. 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.
  19. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
  20. 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.
  21. 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).
  22. 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).
  23. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
  24. 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.
  25. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
  26. 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).
  27. 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.
  28. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
  29. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
  30. 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).
  31. Chen, Zhenhua & Liu, Zhenya & Teka, Hanen & Zhang, Yifan, 2022. "Smart money in China's A-share market: Evidence from big data," Research in International Business and Finance, Elsevier, vol. 61(C).
  32. 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).
  33. Jozef Barunik & Martin Hronec & Ondrej Tobek, 2024. "Predicting the distributions of stock returns around the globe in the era of big data and learning," Papers 2408.07497, arXiv.org.
  34. 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.
  35. Immo Stadtmüller & Benjamin R. Auer & Frank Schuhmacher, 2024. "Core-satellite investing with commodity futures momentum," Journal of Asset Management, Palgrave Macmillan, vol. 25(3), pages 261-287, May.
  36. 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).
  37. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
  38. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2020. "Neural Network Models for Empirical Finance," JRFM, MDPI, vol. 13(11), pages 1-22, October.
  39. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
  40. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  41. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
  42. 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.
  43. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
  44. Van Nieuwerburgh, Stijn & Gupta, Arpit, 2019. "Valuing Private Equity Strip by Strip," CEPR Discussion Papers 14241, C.E.P.R. Discussion Papers.
  45. Bairui Du & Delmiro Fernandez-Reyes & Paolo Barucca, 2020. "Image Processing Tools for Financial Time Series Classification," Papers 2008.06042, arXiv.org, revised Aug 2020.
  46. Amini, Shahram & Elmore, Ryan & Öztekin, Özde & Strauss, Jack, 2021. "Can machines learn capital structure dynamics?," Journal of Corporate Finance, Elsevier, vol. 70(C).
  47. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
  48. 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.
  49. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
  50. Areski Cousin & Jérôme Lelong & Tom Picard, 2022. "Rating transitions forecasting: a filtering approach," Working Papers hal-03347521, HAL.
  51. 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.
  52. Bryan Kelly & Semyon Malamud & Lasse Heje Pedersen, 2023. "Principal Portfolios," Journal of Finance, American Finance Association, vol. 78(1), pages 347-387, February.
  53. Ko, Hyungjin & Son, Bumho & Lee, Jaewook, 2024. "A novel integration of the Fama–French and Black–Litterman models to enhance portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
  54. 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).
  55. Xiaohang Ren & Wenting Jiang & Qiang Ji & Pengxiang Zhai, 2024. "Seeing is believing: Forecasting crude oil price trend from the perspective of images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2809-2821, November.
  56. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
  57. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  58. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
  59. Felipe D. Calainho & Alex M. Minne & Marc K. Francke, 2024. "A Machine Learning Approach to Price Indices: Applications in Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 68(4), pages 624-653, May.
  60. 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).
  61. 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.
  62. Lin William Cong & Ke Tang & Jingyuan Wang & Yang Zhang, 2021. "Deep Sequence Modeling: Development and Applications in Asset Pricing," Papers 2108.08999, arXiv.org.
  63. Uta Pigorsch & Sebastian Schafer, 2021. "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers 2112.04755, arXiv.org.
  64. 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.
  65. Zhaoxing Gao & Ruey S. Tsay, 2023. "Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors," Papers 2307.07689, arXiv.org.
  66. 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.
  67. Susanna Levantesi & Giulia Zacchia, 2021. "Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy," JRFM, MDPI, vol. 14(3), pages 1-21, March.
  68. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
  69. Blanco, Ivan & De Jesus, Miguel & Remesal, Alvaro, 2023. "Overlapping momentum portfolios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 1-22.
  70. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
  71. , & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," CEPR Discussion Papers 16357, C.E.P.R. Discussion Papers.
  72. 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.
  73. 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.
  74. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
  75. 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.
  76. Guanhao Feng & Jingyu He & Nicholas G. Polson, 2018. "Deep Learning for Predicting Asset Returns," Papers 1804.09314, arXiv.org, revised Apr 2018.
  77. Du, Qingjie & Wang, Yang & Wei, Chishen & Wei, K.C. John, 2023. "Machine learning, anomalies, and the expected market return: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
  78. Michael Pinelis & David Ruppert, 2020. "Machine Learning Portfolio Allocation," Papers 2003.00656, arXiv.org, revised Nov 2021.
  79. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
  80. 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.
  81. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
  82. Mike Kraehenbuehl & Joerg Osterrieder, 2022. "The Efficient Market Hypothesis for Bitcoin in the context of neural networks," Papers 2208.07254, arXiv.org.
  83. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
  84. Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023. "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, vol. 86(PA).
  85. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
  86. 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.
  87. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
  88. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
  89. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
  90. Lei Xu & Qian Liu & Bin Li & Chen Ma, 2022. "Fintech business and firm access to bank loans," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4381-4421, December.
  91. Agbloyor, Elikplimi Komla & Pan, Lei & Dwumfour, Richard Adjei & Gyeke-Dako, Agyapomaa, 2023. "We are back again! What can artificial intelligence and machine learning models tell us about why countries knock at the door of the IMF?," Finance Research Letters, Elsevier, vol. 57(C).
  92. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
  93. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).
  94. 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.
  95. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
  96. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
  97. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
  98. 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.
  99. 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.
  100. 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.
  101. 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.
  102. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).
  103. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
  109. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
  110. Yupeng Cao & Zhi Chen & Qingyun Pei & Fabrizio Dimino & Lorenzo Ausiello & Prashant Kumar & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data," Papers 2404.07452, arXiv.org.
  111. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
  112. G Andrew Karolyi & Stijn Van Nieuwerburgh, 2020. "New Methods for the Cross-Section of Returns," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1879-1890.
  113. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  114. Lavko, Matus & Klein, Tony & Walther, Thomas, 2023. "Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods," QBS Working Paper Series 2023/01, Queen's University Belfast, Queen's Business School.
  115. 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.
  116. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Zhentao Shi, 2023. "Economic Forecasts Using Many Noises," Papers 2312.05593, arXiv.org, revised Dec 2023.
  117. 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.
  118. Chendi Ni & Yuying Li & Peter Forsyth & Ray Carroll, 2020. "Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target," Papers 2006.15384, arXiv.org.
  119. 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.
  120. Nagl, Maximilian, 2024. "Intricacy of cryptocurrency returns," Economics Letters, Elsevier, vol. 239(C).
  121. 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.
  122. Pagano, Marco & Wagner, Christian & Zechner, Josef, 2023. "Disaster resilience and asset prices," Journal of Financial Economics, Elsevier, vol. 150(2).
  123. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  124. 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).
  125. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  126. Daiya Mita & Akihiko Takahashi, 2024. "A New Equity Investment Strategy with Artificial Intelligence, Multi Factors, and Technical Indicators," CARF F-Series CARF-F-588, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  127. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
  128. 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.
  129. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org, revised Aug 2024.
  130. Steven Campbell & Qien Song & Ting-Kam Leonard Wong, 2024. "Macroscopic properties of equity markets: stylized facts and portfolio performance," Papers 2409.10859, arXiv.org, revised Oct 2024.
  131. 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.
  132. 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.
  133. Nawaf Almaskati, 2022. "Machine learning in finance: Major applications, issues, metrics, and future trends," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 1-32, September.
  134. Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16, Federal Reserve Bank of Atlanta.
  135. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
  136. 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).
  137. 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).
  138. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
  139. 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.
  140. Li Liu & Zhiyuan Pan & Yudong Wang, 2022. "Shrinking return forecasts," The Financial Review, Eastern Finance Association, vol. 57(3), pages 641-661, August.
  141. 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.
  142. Lof, Matthijs & Nyberg, Henri, 2024. "Discount rates and cash flows: A local projection approach," Journal of Banking & Finance, Elsevier, vol. 162(C).
  143. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
  144. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
  145. 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.
  146. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
  147. 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).
  148. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
  149. 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).
  150. Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
  151. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
  152. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
  153. 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.
  154. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
  155. Peter Andre & Philipp Schirmer & Johannes Wohlfart, 2024. "Mental Models of the Stock Market," CEBI working paper series 23-07, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
  156. 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).
  157. 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.
  158. Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022. "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers 2204.12657, arXiv.org, revised Oct 2022.
  159. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org.
  160. 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).
  161. 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).
  162. 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.
  163. 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).
  164. 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.
  165. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
  166. 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).
  167. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
  168. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
  169. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
  170. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
  171. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
  172. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
  173. Edeling, Alexander & Srinivasan, Shuba & Hanssens, Dominique M., 2021. "The marketing–finance interface: A new integrative review of metrics, methods, and findings and an agenda for future research," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 857-876.
  174. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
  175. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2024. "Forecasting crude oil market volatility: A comprehensive look at uncertainty variables," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1022-1041.
  176. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
  177. Xue Gong & Weiguo Zhang & Weijun Xu & Zhe Li, 2022. "Uncertainty index and stock volatility prediction: evidence from international markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-44, December.
  178. 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.
  179. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
  180. Chen Zhang, 2022. "Asset Pricing and Deep Learning," Papers 2209.12014, arXiv.org.
  181. Fang, Ming & Taylor, Stephen, 2021. "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, vol. 204(C).
  182. Sun, Chuanping, 2024. "Factor correlation and the cross section of asset returns: A correlation-robust machine learning approach," Journal of Empirical Finance, Elsevier, vol. 77(C).
  183. Dominik Wolff & Ulrich Neugebauer, 2019. "Tree-based machine learning approaches for equity market predictions," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 273-288, July.
  184. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
  185. Agar Brugiavini & Petru Crudu, 2023. "The Role of Disability Insurance on the Labour Market Trajectories of Europeans," Working Papers 2023:20, Department of Economics, University of Venice "Ca' Foscari".
  186. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
  187. Wang, Nianling & Zhang, Mingzhi & Zhang, Yuan, 2024. "Return prediction: A tree-based conditional sort approach with firm characteristics," Finance Research Letters, Elsevier, vol. 60(C).
  188. Cakici, Nusret & Zaremba, Adam, 2021. "Liquidity and the cross-section of international stock returns," Journal of Banking & Finance, Elsevier, vol. 127(C).
  189. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2023. "Climate risks and realized volatility of major commodity currency exchange rates," Journal of Financial Markets, Elsevier, vol. 62(C).
  190. Siddhartha Chib & Simon C. Smith, 2024. "Factor Selection and Structural Breaks," Finance and Economics Discussion Series 2024-037, Board of Governors of the Federal Reserve System (U.S.).
  191. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2020. "Building Cross-Sectional Systematic Strategies By Learning to Rank," Papers 2012.07149, arXiv.org.
  192. 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).
  193. Evaggelia Siopi & Thomas Poufinas & James Ming Chen & Charalampos Agiropoulos, 2023. "Can Regulation Affect the Solvency of Insurers? New Evidence from European Insurers," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 29(1), pages 15-30, May.
  194. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
  195. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
  196. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
  197. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  198. Areski Cousin & J'er^ome Lelong & Tom Picard, 2021. "Rating transitions forecasting: a filtering approach," Papers 2109.10567, arXiv.org, revised Jun 2023.
  199. Gomez-Gonzalez, Jose E. & Uribe, Jorge M. & Valencia, Oscar, 2024. "Asymmetric Sovereign Risk: Implications for Climate Change Preparation," IDB Publications (Working Papers) 13447, Inter-American Development Bank.
  200. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
  201. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
  202. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
  203. José Parra-Moyano & Daniel Partida & Moritz Gessl & Somnath Mazumdar, 2024. "Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models," Digital Finance, Springer, vol. 6(3), pages 427-439, September.
  204. Arpit Gupta & Stijn Van Nieuwerburgh, 2021. "Valuing Private Equity Investments Strip by Strip," Journal of Finance, American Finance Association, vol. 76(6), pages 3255-3307, December.
  205. Jian Chen & Jiaquan Yao & Qunzi Zhang & Xiaoneng Zhu, 2023. "Global Disaster Risk Matters," Management Science, INFORMS, vol. 69(1), pages 576-597, January.
  206. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2020. "A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance," Working Paper series 20-27, Rimini Centre for Economic Analysis.
  207. Wenbo Wu & Jiaqi Chen & Zhibin (Ben) Yang & Michael L. Tindall, 2021. "A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection," Management Science, INFORMS, vol. 67(7), pages 4577-4601, July.
  208. 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.
  209. Yuxin Liu & Jimin Lin & Achintya Gopal, 2024. "NeuralBeta: Estimating Beta Using Deep Learning," Papers 2408.01387, arXiv.org, revised Oct 2024.
  210. Kevin J. Lansing & Michael Tubbs, 2018. "Using Sentiment and Momentum to Predict Stock Returns," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
  211. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
  212. Daiya Mita & Akihiko Takahashi, 2024. "A New Equity Investment Strategy with Artificial Intelligence, Multi Factors, and Technical Indicators," CIRJE F-Series CIRJE-F-1230, CIRJE, Faculty of Economics, University of Tokyo.
  213. Yanci Zhang & Mengjia Xia & Mingyang Li & Haitao Mao & Yutong Lu & Yupeng Lan & Jinlin Ye & Rui Dai, 2023. "Form 10-K Itemization," Papers 2303.04688, arXiv.org.
  214. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
  215. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
  216. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi & Richard Y. D. Xu, 2022. "Time‐varying neural network for stock return prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 3-18, January.
  217. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
  218. Yuegang Song & Ruibing Wu, 2022. "The Impact of Financial Enterprises’ Excessive Financialization Risk Assessment for Risk Control based on Data Mining and Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1245-1267, December.
  219. Umar, Zaghum & Zaremba, Adam & Umutlu, Mehmet & Mercik, Aleksander, 2024. "Interaction effects in the cross-section of country and industry returns," Journal of Banking & Finance, Elsevier, vol. 165(C).
  220. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
  221. I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.
  222. Steven Y. K. Wong & Jennifer S. K. Chan & Lamiae Azizi, 2024. "Quantifying neural network uncertainty under volatility clustering," Papers 2402.14476, arXiv.org, revised Sep 2024.
  223. Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).
  224. Luiz Renato Lima & Lucas Lúcio Godeiro, 2023. "Equity‐premium prediction: Attention is all you need," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 105-122, January.
  225. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
  226. Chaeshick Chung & Sukjin Park, 2021. "Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks," Working Papers 2108, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
  227. Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
  228. Back, Kerry & Crotty, Kevin & Kazempour, Seyed Mohammad, 2022. "Validity, tightness, and forecasting power of risk premium bounds," Journal of Financial Economics, Elsevier, vol. 144(3), pages 732-760.
  229. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
  230. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
  231. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
  232. Rama Cont & Mihai Cucuringu & Chao Zhang, 2021. "Cross-Impact of Order Flow Imbalance in Equity Markets," Papers 2112.13213, arXiv.org, revised Jun 2023.
  233. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
  234. E. Lorenzo & G. Piscopo & M. Sibillo, 2024. "Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages," Computational Management Science, Springer, vol. 21(1), pages 1-22, June.
  235. Christian Fieberg & Lars Hornuf & Gerrit Liedtke & Thorsten Poddig, 2020. "Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis," CESifo Working Paper Series 8377, CESifo.
  236. van Binsbergen, Jules H. & Boons, Martijn & Opp, Christian C. & Tamoni, Andrea, 2023. "Dynamic asset (mis)pricing: Build-up versus resolution anomalies," Journal of Financial Economics, Elsevier, vol. 147(2), pages 406-431.
  237. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2024. "Panel data nowcasting: The case of price–earnings ratios," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 292-307, March.
  238. Liao, Cunfei & Ma, Tian, 2024. "From fundamental signals to stock volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
  239. 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.
  240. Xue Gong & Weiguo Zhang & Yuan Zhao & Xin Ye, 2023. "Forecasting stock volatility with a large set of predictors: A new forecast combination method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1622-1647, November.
  241. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
  242. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
  243. Malakhov, Alexey & Riley, Timothy B. & Yan, Qing, 2024. "Do hedge funds bet against beta?," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1507-1525.
  244. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
  245. David A. Mascio & Marat Molyboga & Frank J. Fabozzi, 2023. "The battle of the factors: Macroeconomic variables or investor sentiment?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2280-2291, December.
  246. Breitung, Christian, 2023. "Automated stock picking using random forests," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 532-556.
  247. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  248. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2019. "Machine Learning for Forecasting Excess Stock Returns The Five-Year-View," Graz Economics Papers 2019-06, University of Graz, Department of Economics.
  249. De Nard, Gianluca & Zhao, Zhao, 2022. "A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 654-676.
  250. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
  251. Andrea Rigamonti, 2024. "Can machine learning make technical analysis work?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 38(3), pages 399-412, September.
  252. Xianzheng Zhou & Hui Zhou & Huaigang Long, 2023. "Forecasting the equity premium: Do deep neural network models work?," Modern Finance, Modern Finance Institute, vol. 1(1), pages 1-11.
  253. 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.
  254. Zhenning Hong & Ruyan Tian & Qing Yang & Weiliang Yao & Tingting Ye & Liangliang Zhang, 2021. "Asset Allocation via Machine Learning," Accounting and Finance Research, Sciedu Press, vol. 10(4), pages 1-34, November.
  255. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
  256. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
  257. Guettler, Andre & Naeem, Mahvish & Norden, Lars & Van Doornik, Bernardus, 2024. "Pre-publication revisions of bank financial statements: A novel way to monitor banks?," Journal of Financial Intermediation, Elsevier, vol. 58(C).
  258. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
  259. Rossi, Alberto G. & Utkus, Stephen, 2024. "The diversification and welfare effects of robo-advising," Journal of Financial Economics, Elsevier, vol. 157(C).
  260. Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
  261. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2024. "Predicting Bond Return Predictability," Management Science, INFORMS, vol. 70(2), pages 931-951, February.
  262. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
  263. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
  264. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  265. Ali Mehrabian & Ehsan Hoseinzade & Mahdi Mazloum & Xiaohong Chen, 2024. "Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction," Papers 2410.03707, arXiv.org.
  266. Kim, Jang Ho, 2022. "Analyzing diversification benefits of cryptocurrencies through backfill simulation," Finance Research Letters, Elsevier, vol. 50(C).
  267. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
  268. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
  269. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023. "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, vol. 237(2).
  270. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
  271. El Amine Cherrat & Snehal Raj & Iordanis Kerenidis & Abhishek Shekhar & Ben Wood & Jon Dee & Shouvanik Chakrabarti & Richard Chen & Dylan Herman & Shaohan Hu & Pierre Minssen & Ruslan Shaydulin & Yue , 2023. "Quantum Deep Hedging," Papers 2303.16585, arXiv.org, revised Nov 2023.
  272. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
  273. Peter Carr & Liuren Wu & Zhibai Zhang, 2019. "Using Machine Learning to Predict Realized Variance," Papers 1909.10035, arXiv.org.
  274. Luo, Qin & Bu, Jinfeng & Xu, Weiju & Huang, Dengshi, 2023. "Stock market volatility prediction: Evidence from a new bagging model," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 445-456.
  275. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2023. "Sovereign Risk and Economic Complexity: Machine Learning Insights on Causality and Prediction," IREA Working Papers 202315, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.
  276. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Nov 2024.
  277. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
  278. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
  279. Guo, Yangli & Ma, Feng & Li, Haibo & Lai, Xiaodong, 2022. "Oil price volatility predictability based on global economic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
  280. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
  281. Chad Brown, 2024. "Statistical Properties of Deep Neural Networks with Dependent Data," Papers 2410.11113, arXiv.org, revised Nov 2024.
  282. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, Wiley Blackwell, vol. 60(2), pages 467-515, May.
  283. Zhao, Chencheng & Yuan, Xianghui & Long, Jun & Jin, Liwei & Guan, Bowen, 2023. "Financial indicators analysis using machine learning: Evidence from Chinese stock market," Finance Research Letters, Elsevier, vol. 58(PD).
  284. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
  285. Li, Zhao-Chen & Xie, Chi & Wang, Gang-Jin & Zhu, You & Zeng, Zhi-Jian & Gong, Jue, 2024. "Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 673-711.
  286. Lirong Gan & Wei-han Liu, 2024. "Option Pricing Based on the Residual Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1327-1347, April.
  287. Damir Filipovic & Paul Schneider, 2024. "Fundamental properties of linear factor models," Papers 2409.02521, arXiv.org, revised Oct 2024.
  288. 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.
  289. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
  290. Zaremba, Adam & Kizys, Renatas & Tzouvanas, Panagiotis & Aharon, David Y. & Demir, Ender, 2021. "The quest for multidimensional financial immunity to the COVID-19 pandemic: Evidence from international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
  291. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.
  292. Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
  293. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.
  294. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
  295. Catherine D'Hondt & Rudy De Winne & Eric Ghysels & Steve Raymond, 2019. "Artificial Intelligence Alter Egos: Who benefits from Robo-investing?," Papers 1907.03370, arXiv.org.
  296. Kovvuri, Veera Raghava Reddy & Fu, Hsuan & Fan, Xiuyi & Seisenberger, Monika, 2023. "Fund performance evaluation with explainable artificial intelligence," Finance Research Letters, Elsevier, vol. 58(PB).
  297. Jiang, Fuwei & Ma, Tian & Zhu, Feifei, 2024. "Fundamental characteristics, machine learning, and stock price crash risk," Journal of Financial Markets, Elsevier, vol. 69(C).
  298. Jiang, Hao & Li, Sophia Zhengzi & Wang, Hao, 2021. "Pervasive underreaction: Evidence from high-frequency data," Journal of Financial Economics, Elsevier, vol. 141(2), pages 573-599.
  299. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
  300. Runshan Fu & Yan Huang & Param Vir Singh, 2020. "Crowd, Lending, Machine, and Bias," Papers 2008.04068, arXiv.org.
  301. Nakagawa, Kei & Sakemoto, Ryuta, 2022. "Cryptocurrency network factors and gold," Finance Research Letters, Elsevier, vol. 46(PB).
  302. Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
  303. Campanella, Francesco & Serino, Luana & Battisti, Enrico & Giakoumelou, Anastasia & Karasamani, Isabella, 2023. "FinTech in the financial system: Towards a capital-intensive and high competence human capital reality?," Journal of Business Research, Elsevier, vol. 155(PA).
  304. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
  305. Ma, Tian & Liao, Cunfei & Jiang, Fuwei, 2024. "Factor momentum in the Chinese stock market," Journal of Empirical Finance, Elsevier, vol. 75(C).
  306. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
  307. Yaojie Zhang & Mengxi He & Zhikai Zhang, 2024. "Forecasting stock returns with industry volatility concentration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2705-2730, November.
  308. Liang Zeng & Lei Wang & Hui Niu & Ruchen Zhang & Ling Wang & Jian Li, 2021. "Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling," Papers 2107.11972, arXiv.org, revised Jul 2024.
  309. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
  310. Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
  311. Weijia Peng & Chun Yao, 2023. "Sector-level equity returns predictability with machine learning and market contagion measure," Empirical Economics, Springer, vol. 65(4), pages 1761-1798, October.
  312. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
  313. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
  314. Billio, Monica & Lo, Andrew W. & Pelizzon, Loriana & Getmansky, Mila & Zareei, Abalfazl, 2021. "Global realignment in financial market dynamics: Evidence from ETF networks," SAFE Working Paper Series 304, Leibniz Institute for Financial Research SAFE.
  315. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
  316. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
  317. Tse, Tiffany Tsz Kwan & Hanaki, Nobuyuki & Mao, Bolin, 2024. "Beware the performance of an algorithm before relying on it: Evidence from a stock price forecasting experiment," Journal of Economic Psychology, Elsevier, vol. 102(C).
  318. Karim, Sitara & Shafiullah, Muhammad & Naeem, Muhammad Abubakr, 2024. "When one domino falls, others follow: A machine learning analysis of extreme risk spillovers in developed stock markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
  319. Bollerslev, Tim & Medeiros, Marcelo C. & Patton, Andrew J. & Quaedvlieg, Rogier, 2022. "From zero to hero: Realized partial (co)variances," Journal of Econometrics, Elsevier, vol. 231(2), pages 348-360.
  320. Simon Hediger & Jeffrey Näf & Marc S. Paolella & Paweł Polak, 2023. "Heterogeneous tail generalized common factor modeling," Digital Finance, Springer, vol. 5(2), pages 389-420, June.
  321. Ngo, Vu Minh & Nguyen, Huan Huu & Van Nguyen, Phuc, 2023. "Does reinforcement learning outperform deep learning and traditional portfolio optimization models in frontier and developed financial markets?," Research in International Business and Finance, Elsevier, vol. 65(C).
  322. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
  323. Wang, Jianqiu & Wu, Ke & Tong, Guoshi & Chen, Dongxu, 2023. "Nonlinearity in the cross-section of stock returns: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 174-205.
  324. Tu, Xueyong & Li, Bin, 2024. "Robust portfolio selection with smart return prediction," Economic Modelling, Elsevier, vol. 135(C).
  325. 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.
  326. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
  327. Zongwu Cai & Pixiong Chen, 2022. "New Online Investor Sentiment and Asset Returns," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202216, University of Kansas, Department of Economics, revised Nov 2022.
  328. Chen, Ding & Guo, Biao & Zhou, Guofu, 2023. "Firm fundamentals and the cross-section of implied volatility shapes," Journal of Financial Markets, Elsevier, vol. 63(C).
  329. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
  330. Aubry, Mathieu & Kräussl, Roman & Manso, Gustavo & Spaenjers, Christophe, 2019. "Machine learning, human experts, and the valuation of real assets," CFS Working Paper Series 635, Center for Financial Studies (CFS).
  331. 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.
  332. Hüseyin İlker Erçen & Hüseyin Özdeşer & Turgut Türsoy, 2022. "The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach," Sustainability, MDPI, vol. 14(9), pages 1-19, April.
  333. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
  334. 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.
  335. Li, Weiping & Mei, Feng, 2020. "Asset returns in deep learning methods: An empirical analysis on SSE 50 and CSI 300," Research in International Business and Finance, Elsevier, vol. 54(C).
  336. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
  337. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
  338. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam, 2024. "Cryptocurrency anomalies and economic constraints," International Review of Financial Analysis, Elsevier, vol. 94(C).
  339. Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
  340. Maria Cristina Arcuri & Gino Gandolfi & Manou Monteux & Giovanni Verga, 2021. "What Factors Influence European Corporate Bond Spread?," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(4), pages 1-87, July.
  341. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
  342. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
  343. Theo Berger & Jana Koubová, 2024. "Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2904-2916, November.
  344. Jing, Zhongbo & Li, Qin & Zhao, Hongyi & Zhao, Yang, 2024. "Predicting stock price crash risk in China: A modified graph WaveNet model," Finance Research Letters, Elsevier, vol. 64(C).
  345. Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022. "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, vol. 60(C).
  346. Azevedo, Vitor, 2023. "Analysts’ underreaction and momentum strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
  347. Mustafa, Andy Ali & Lin, Ching-Yang & Kakinaka, Makoto, 2022. "Detecting market pattern changes: A machine learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
  348. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
  349. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
  350. 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.
  351. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
  352. Nusret Cakici & Christian Fieberg & Daniel Metko & Adam Zaremba, 2024. "Do Anomalies Really Predict Market Returns? New Data and New Evidence," Review of Finance, European Finance Association, vol. 28(1), pages 1-44.
  353. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
  354. Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).
  355. Campisi, Giovanni & Muzzioli, Silvia & De Baets, Bernard, 2024. "A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices," International Journal of Forecasting, Elsevier, vol. 40(3), pages 869-880.
  356. Ye, Jing & Xue, Minggao, 2021. "Influences of sentiment from news articles on EU carbon prices," Energy Economics, Elsevier, vol. 101(C).
  357. Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023. "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, vol. 126(C).
  358. Zhang, Yaojie & Wang, Yudong, 2023. "Forecasting crude oil futures market returns: A principal component analysis combination approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 659-673.
  359. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers 2306.10224, arXiv.org, revised Feb 2024.
  360. Schnaubelt, Matthias & Seifert, Oleg, 2020. "Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements?," FAU Discussion Papers in Economics 04/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  361. 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).
  362. Kim, Jang Ho & Han, Jiwoon & Kang, Taehyeon & Fabozzi, Frank J., 2023. "A machine learning approach for comparing the largest firm effect," Emerging Markets Review, Elsevier, vol. 54(C).
  363. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
  364. Yu, Fanchao, 2023. "Macroeconomic information, global economic policy uncertainty and gold futures return predictability," Finance Research Letters, Elsevier, vol. 55(PA).
  365. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  366. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
  367. 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.
  368. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
  369. Gomez-Gonzalez, Jose E. & Uribe, Jorge M. & Valencia, Oscar, 2024. "Sovereign Risk and Economic Complexity," IDB Publications (Working Papers) 13393, Inter-American Development Bank.
  370. Huang, Dashan & Li, Jiangyuan & Wang, Liyao & Zhou, Guofu, 2020. "Time series momentum: Is it there?," Journal of Financial Economics, Elsevier, vol. 135(3), pages 774-794.
  371. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
  372. Serge Nyawa & Dieudonné Tchuente & Samuel Fosso-Wamba, 2024. "COVID-19 vaccine hesitancy: a social media analysis using deep learning," Annals of Operations Research, Springer, vol. 339(1), pages 477-515, August.
  373. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
  374. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
  375. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
  376. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework," Papers 2010.14694, arXiv.org, revised Jul 2021.
  377. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
  378. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
  379. Fang, Yi & Chen, Yuzhi & Ren, Hang, 2023. "A factor pricing model based on machine learning algorithm," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 280-297.
  380. Li, Ang & Liu, Mark & Sheather, Simon, 2023. "Predicting stock splits using ensemble machine learning and SMOTE oversampling," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
  381. 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.
  382. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
  383. Tan, Xilong & Tao, Yubo, 2023. "Trend-based forecast of cryptocurrency returns," Economic Modelling, Elsevier, vol. 124(C).
  384. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
  385. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
  386. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
  387. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
  388. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
  389. Thibault Jaisson, 2021. "Deep differentiable reinforcement learning and optimal trading," Papers 2112.02944, arXiv.org, revised Apr 2022.
  390. Haixiang Yao & Shenghao Xia & Hao Liu, 2024. "Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1770-1794, September.
  391. Andrew J. Patton & Yasin Simsek, 2023. "Generalized Autoregressive Score Trees and Forests," Papers 2305.18991, arXiv.org.
  392. 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.
  393. Djoumbissie David Romain, 2020. "Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input," Papers 2011.13113, arXiv.org, revised Apr 2022.
  394. Hediger, Simon & Michel, Loris & Näf, Jeffrey, 2022. "On the use of random forest for two-sample testing," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
  395. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.
  396. Georg Keilbar & Weining Wang, 2022. "Modelling systemic risk using neural network quantile regression," Empirical Economics, Springer, vol. 62(1), pages 93-118, January.
  397. Park, Yang-Ho, 2022. "Spread position as a leading economic indicator," Journal of Financial Markets, Elsevier, vol. 59(PA).
  398. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation of Semiparametric Multi-Index Models Using Deep Neural Networks," Monash Econometrics and Business Statistics Working Papers 21/23, Monash University, Department of Econometrics and Business Statistics.
  399. Chang Liu & Sandra Paterlini, 2023. "Stock Price Prediction Using Temporal Graph Model with Value Chain Data," Papers 2303.09406, arXiv.org.
  400. Hai Lin & Pengfei Liu & Cheng Zhang, 2023. "The trend premium around the world: Evidence from the stock market," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 317-358, June.
  401. Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022. "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, vol. 48(C).
  402. Liyuan Cui & Guanhao Feng & Yongmiao Hong, 2024. "Regularized Gmm For Time‐Varying Models With Applications To Asset Pricing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(2), pages 851-883, May.
  403. Zhao, Jinsong & Li, Xinghao & Yu, Chin-Hsien & Chen, Shi & Lee, Chi-Chuan, 2022. "Riding the FinTech innovation wave: FinTech, patents and bank performance," Journal of International Money and Finance, Elsevier, vol. 122(C).
  404. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
  405. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).
  406. Jules H van Binsbergen & Xiao Han & Alejandro Lopez-Lira, 2023. "Man versus Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases," The Review of Financial Studies, Society for Financial Studies, vol. 36(6), pages 2361-2396.
  407. Sun, Chuanwang & Xu, Mengjie & Wang, Bo, 2024. "Deep learning: Spatiotemporal impact of digital economy on energy productivity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
  408. Jackwerth, Jens Carsten & Menner, Marco, 2020. "Does the Ross recovery theorem work empirically?," Journal of Financial Economics, Elsevier, vol. 137(3), pages 723-739.
  409. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
  410. Fengmin Xu & Jieao Ma, 2023. "Intelligent option portfolio model with perspective of shadow price and risk-free profit," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  411. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
  412. Wolfgang Bessler & Dominik Wolff, 2024. "Portfolio Optimization with Sector Return Prediction Models," JRFM, MDPI, vol. 17(6), pages 1-34, June.
  413. Li, Yuying & Forsyth, Peter A., 2019. "A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 189-204.
  414. 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.
  415. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  416. Jing-Zhi Huang & Zhan Shi, 2023. "Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance," Management Science, INFORMS, vol. 69(3), pages 1780-1804, March.
  417. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
  418. Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021. "Option return predictability with machine learning and big data," CFR Working Papers 21-08, University of Cologne, Centre for Financial Research (CFR).
  419. Dat Mai, 2024. "StockGPT: A GenAI Model for Stock Prediction and Trading," Papers 2404.05101, arXiv.org, revised Oct 2024.
  420. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
  421. Xiao, Xiang & Hua, Xia & Qin, Kexin, 2024. "A self-attention based cross-sectional return forecasting model with evidence from the Chinese market," Finance Research Letters, Elsevier, vol. 62(PA).
  422. 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.
  423. Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022. "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers 2022-09, Department of Economics and Business Economics, Aarhus University.
  424. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
  425. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.
  426. Adil Haniev, 2024. "Intangible Assets and US Stock Returns: An analysis using the Index Method, Panel Regression, and Machine Learning," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(3), pages 833-854.
  427. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  428. Bagnara, Matteo, 2024. "The economic value of cross-predictability: A performance-based measure," SAFE Working Paper Series 424, Leibniz Institute for Financial Research SAFE.
  429. Lu, Zhongjin & Malliaris, Steven & Qin, Zhongling, 2023. "Heterogeneous liquidity providers and night-minus-day return predictability," Journal of Financial Economics, Elsevier, vol. 148(3), pages 175-200.
  430. Anubha Goel & Puneet Pasricha & Juho Kanniainen, 2024. "Time-Series Foundation Model for Value-at-Risk," Papers 2410.11773, arXiv.org, revised Oct 2024.
  431. Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
  432. Nicolas Suhadolnik & Jo Ueyama & Sergio Da Silva, 2023. "Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach," JRFM, MDPI, vol. 16(12), pages 1-21, November.
  433. Yan Hu & Jian Ni, 2024. "A deep learning‐based financial hedging approach for the effective management of commodity risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 879-900, June.
  434. Han, Chulwoo & He, Zhaodong & Toh, Alenson Jun Wei, 2023. "Pairs trading via unsupervised learning," European Journal of Operational Research, Elsevier, vol. 307(2), pages 929-947.
  435. 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).
  436. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
  437. Son, Bumho & Lee, Jaewook, 2022. "Graph-based multi-factor asset pricing model," Finance Research Letters, Elsevier, vol. 44(C).
  438. Zhaobo Zhu & Licheng Sun, 2024. "When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis," Post-Print hal-04703041, HAL.
  439. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
  440. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
  441. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
  442. Hao Qian & Hongting Zhou & Qian Zhao & Hao Chen & Hongxiang Yao & Jingwei Wang & Ziqi Liu & Fei Yu & Zhiqiang Zhang & Jun Zhou, 2024. "MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction," Papers 2402.06633, arXiv.org.
  443. Casas Villalba, Maria Isabel, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de Estadística.
  444. Bo Yu & Bruce Mizrach & Norman R. Swanson, 2020. "New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section," Econometrics, MDPI, vol. 8(2), pages 1-52, May.
  445. Li, Xiaodan & Gong, Xue & Ge, Futing & Huang, Jingjing, 2024. "Forecasting stock volatility using pseudo-out-of-sample information," International Review of Economics & Finance, Elsevier, vol. 90(C), pages 123-135.
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