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Dissecting Characteristics Nonparametrically

Citations

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Cited by:

  1. 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.
  2. 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).
  3. Dong, Mengmeng, 2025. "Economic aggregation of return signals in global markets," Journal of Empirical Finance, Elsevier, vol. 84(C).
  4. 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).
  5. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
  6. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
  7. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
  8. 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).
  9. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
  10. 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.
  11. Wu, Haoran & Gao, Zhiwei & Nie, Boyang & Zhao, Binru, 2025. "Can machines learn Chinese mutual funds?," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
  12. 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).
  13. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  14. Bandi, Federico M. & Chaudhuri, Shomesh E. & Lo, Andrew W. & Tamoni, Andrea, 2021. "Spectral factor models," Journal of Financial Economics, Elsevier, vol. 142(1), pages 214-238.
  15. Chris Florackis & Christodoulos Louca & Roni Michaely & Michael Weber, 2023. "Cybersecurity Risk," The Review of Financial Studies, Society for Financial Studies, vol. 36(1), pages 351-407.
  16. Pan, Zhiyuan & Zhong, Hao & Wang, Yudong & Huang, Juan, 2024. "Forecasting oil futures returns with news," Energy Economics, Elsevier, vol. 134(C).
  17. Deshui Yu & Yayi Yan, 2023. "Joint dynamics of stock returns and cash flows: A time‐varying present‐value framework," Financial Management, Financial Management Association International, vol. 52(3), pages 513-541, September.
  18. 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).
  19. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
  20. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
  21. Alex Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2022. "A New Test of Risk Factor Relevance," Journal of Finance, American Finance Association, vol. 77(4), pages 2183-2238, August.
  22. Sun, Chuanping, 2025. "A correlation-robust shrinkage estimator: Oracle inequality and an application on out-of-sample factor selection," Economics Letters, Elsevier, vol. 255(C).
  23. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
  24. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
  25. Jinghai He & Cheng Hua & Chunyang Zhou & Zeyu Zheng, 2025. "Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information," Papers 2501.17992, arXiv.org.
  26. 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.
  27. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  28. Cheng, Mingmian & Liao, Yuan & Yang, Xiye, 2023. "Uniform predictive inference for factor models with instrumental and idiosyncratic betas," Journal of Econometrics, Elsevier, vol. 237(2).
  29. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  30. Michaely, Roni & Rossi, Stefano & Weber, Michael, 2021. "Signaling safety," Journal of Financial Economics, Elsevier, vol. 139(2), pages 405-427.
  31. 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).
  32. 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.
  33. 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.
  34. Molero González, Laura & Cerqueti, Roy & Mattera, Raffaele & Sánchez Granero, Miguel Ángel & Trinidad Segovia, Juan Evangelista, 2025. "Analyzing clustered factors in the cryptocurrency market with Random Matrix Theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 665(C).
  35. Molero-González, L. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A. & García-Medina, A., 2023. "Market Beta is not dead: An approach from Random Matrix Theory," Finance Research Letters, Elsevier, vol. 55(PA).
  36. 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).
  37. Antonio Garcia-Amate & Laura Molero-González & Miguel Angel Sánchez-Granero & Juan Evangelista Trinidad-Segovia & Andres García-Medina, 2024. "Testing the significance of pricing factors of oil and gas companies," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-18, December.
  38. Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024. "Forecasting realized volatility: Does anything beat linear models?," Journal of Empirical Finance, Elsevier, vol. 78(C).
  39. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
  40. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
  41. Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
  42. 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.).
  43. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
  44. Yoshimasa Uematsu & Shinya Tanaka, 2026. "Post-Screening Portfolio Selection," Papers 2604.17593, arXiv.org.
  45. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
  46. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
  47. Alessi, Lucia & Balduzzi, Pierluigi & Savona, Roberto, 2019. "Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data," JRC Working Papers in Economics and Finance 2019-03, Joint Research Centre, European Commission.
  48. 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.
  49. James, Robert & Leung, Henry & Leung, Jessica Wai Yin & Prokhorov, Artem, 2023. "Forecasting tail risk measures for financial time series: An extreme value approach with covariates," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 29-50.
  50. Babiak, Mykola & Baruník, Jozef, 2026. "Deep learning, predictability, and optimal portfolio returns," Journal of Empirical Finance, Elsevier, vol. 87(C).
  51. Wolfgang Breuer & Andreas Knetsch, 2023. "Recent trends in the digitalization of finance and accounting," Journal of Business Economics, Springer, vol. 93(9), pages 1451-1461, November.
  52. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
  53. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
  54. Meng, Qingbin & Qi, Ji & Wang, Solomon & Zhao, Xuankai, 2026. "Disciplining the factor zoo: Identifying pricing factors in the Chinese stock market," Economic Modelling, Elsevier, vol. 155(C).
  55. Qian, Yihe & Zhang, Yang, 2025. "Long-term forecasting in asset pricing: Machine learning models’ sensitivity to macroeconomic shifts and firm-specific factors," The North American Journal of Economics and Finance, Elsevier, vol. 78(C).
  56. Maysam Khodayari Gharanchaei & Prabhu Prasad Panda & Xilin Chen, 2024. "Quantitative Investment Diversification Strategies via Various Risk Models," Papers 2407.01550, arXiv.org.
  57. 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.
  58. Anja Vinzelberg & Benjamin R. Auer, 2021. "Do crude oil futures still fuel portfolio performance?," Review of Financial Economics, John Wiley & Sons, vol. 39(4), pages 402-423, October.
  59. Ruofan Xu & Qingliang Fan, 2025. "Single-Index Quantile Factor Model with Observed Characteristics," Papers 2506.19586, arXiv.org.
  60. Auer, Benjamin R. & Schuhmacher, Frank & Niemann, Sebastian, 2023. "Cloning mutual fund returns," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 31-37.
  61. Thomas Gehrig & Leopold Sögner & Arne Westerkamp, 2025. "Extending the demand system approach to asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 39(1), pages 133-166, March.
  62. 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.
  63. Chen, Shan & Li, Tao, 2025. "A unified duration-based explanation of the value, profitability, and investment anomalies," Journal of Empirical Finance, Elsevier, vol. 84(C).
  64. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
  65. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2020. "Estimation of large dimensional conditional factor models in finance," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 219-282, Elsevier.
  66. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
  67. Gagan Deep & Akash Deep & William Lamptey, 2025. "Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals," Papers 2512.12924, arXiv.org.
  68. repec:cam:camjip:2506 is not listed on IDEAS
  69. Liu, Yanchu & Zhou, Heyang & Yang, Haisheng, 2025. "Latent factor models for the Chinese commodity futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
  70. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
  71. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
  72. 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.
  73. O’Sullivan, Conall & Papavassiliou, Vassilios G. & Wafula, Ronald Wekesa & Boubaker, Sabri, 2024. "New insights into liquidity resiliency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
  74. repec:bge:wpaper:1245 is not listed on IDEAS
  75. Shunyao Wang & Ming Cheng & Christina Dan Wang, 2025. "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks," Papers 2505.06864, arXiv.org.
  76. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
  77. Kwon, Tae Yeon, 2025. "Feature importance in linear models with ensemble machine learning: A study of the Fama and French five-factor model," Finance Research Letters, Elsevier, vol. 71(C).
  78. Yuhan Cheng & Heyang Zhou & Yanchu Liu, 2025. "Large Language Models and Futures Price Factors in China," Papers 2509.23609, arXiv.org.
  79. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
  80. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
  81. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
  82. Tian, Guangning & Peng, Yuchao & Du, Huancheng & Meng, Yuhao, 2024. "Forecasting crude oil returns in different degrees of ambiguity: Why machine learn better?," Energy Economics, Elsevier, vol. 139(C).
  83. Sandra Dreher & Sebastian Eichfelder & Felix Noth, 2024. "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," Journal of Business Economics, Springer, vol. 94(1), pages 1-39, January.
  84. 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.
  85. Bo Li & Sabri Boubaker & Zhenya Liu & Waël Louhichi & Yao Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 527-559, August.
  86. Dohyun Chun & Jongho Kang & Jihun Kim, 2024. "Forecasting returns with machine learning and optimizing global portfolios: evidence from the Korean and U.S. stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
  87. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  88. Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
  89. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
  90. Chen, Minghui & Hanauer, Matthias X. & Kalsbach, Tobias, 2025. "Model complexity and the performance of global versus regional models," Economics Letters, Elsevier, vol. 257(C).
  91. Madhura Dasgupta & Samarth Gupta, 2024. "What Determines Enterprise Borrowing from Self Help Groups? An Interpretable Supervised Machine Learning Approach," Journal of Financial Services Research, Springer;Western Finance Association, vol. 66(1), pages 77-99, August.
  92. 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.
  93. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
  94. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
  95. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  96. Sak, Halis & Huang, Tao & Chng, Michael T., 2024. "Exploring the factor zoo with a machine-learning portfolio," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  97. Coqueret, Guillaume & Pérignon, Christophe, 2025. "Persistent Anomalies and Nonstandard Errors," HEC Research Papers Series 1578, HEC Paris.
  98. 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.
  99. 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.
  100. Wang, Chuyu & Zhang, Guanglong, 2025. "In the shadows of opacity: Firm information quality and latent factor model performance," International Review of Financial Analysis, Elsevier, vol. 100(C).
  101. 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).
  102. Chu Zhang, 2024. "Testing Pricing Errors of Models with Latent Factors and Firm Characteristics as Covariances," Management Science, INFORMS, vol. 70(3), pages 1706-1728, March.
  103. 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.
  104. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
  105. Clint Howard, 2024. "Choices Matter When Training Machine Learning Models for Return Prediction," Financial Analysts Journal, Taylor & Francis Journals, vol. 80(4), pages 81-107, October.
  106. Li, Bin & Rossi, Alberto G. & Yan, Xuemin (Sterling) & Zheng, Lingling, 2025. "Machine learning from a “Universe” of signals: The role of feature engineering," Journal of Financial Economics, Elsevier, vol. 172(C).
  107. Weber, Michael, 2018. "Cash flow duration and the term structure of equity returns," Journal of Financial Economics, Elsevier, vol. 128(3), pages 486-503.
  108. Joseph E. Engelberg & Richard B. Evans & Greg Leonard & Adam V. Reed & Matthew C. Ringgenberg, 2025. "The Loan Fee Anomaly: A Short Seller’s Best Ideas," Management Science, INFORMS, vol. 71(7), pages 5529-5551, July.
  109. 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).
  110. 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).
  111. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam & Cakici, Nusret, 2025. "A factor model for the cross-section of country equity risk premia," Journal of Banking & Finance, Elsevier, vol. 171(C).
  112. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
  113. Liu, Tingting & Lu, Zhongjin (Gene) & Shu, Tao & Wei, Fengrong, 2022. "Unique bidder-target relatedness and synergies creation in mergers and acquisitions," Journal of Corporate Finance, Elsevier, vol. 73(C).
  114. Yuan Liao & Xinjie Ma & Andreas Neuhierl & Linda Schilling, 2025. "The Uncertainty of Machine Learning Predictions in Asset Pricing," Papers 2503.00549, arXiv.org.
  115. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
  116. Mohrschladt, Hannes & Nolte, Sven, 2018. "A new risk factor based on equity duration," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 126-135.
  117. Eunchong Kim & Taehee Cho & Bonha Koo & Hyoung-Goo Kang, 2023. "Conditional autoencoder asset pricing models for the Korean stock market," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-30, July.
  118. Dong, C. & Li, S., 2021. "Specification Lasso and an Application in Financial Markets," Cambridge Working Papers in Economics 2139, Faculty of Economics, University of Cambridge.
  119. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
  120. 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).
  121. Hongyi Liu, 2025. "Deep Learning for Conditional Asset Pricing Models," Papers 2509.04812, arXiv.org.
  122. Antonio Marsi, 2023. "Predicting European stock returns using machine learning," SN Business & Economics, Springer, vol. 3(7), pages 1-25, July.
  123. 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.
  124. 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.
  125. Changeun Kim & Younwoo Jeong & Bong-Gyu Jang, 2025. "Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model," Papers 2512.16251, arXiv.org, revised Apr 2026.
  126. 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.
  127. 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.
  128. Hendrik Jenett & Cathrine Nagl & Maximilian Nagl & S. McKay Price & Wolfgang Schaefers, 2026. "Dynamics of REIT Returns and Volatility: Analyzing Time-Varying Drivers Through an Explainable Machine Learning Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 72(1), pages 1-40, January.
  129. Li, Jinchuan & Zhu, Yifeng, 2026. "Taming crypto anomalies: A Lasso-type factor model," Research in International Business and Finance, Elsevier, vol. 83(C).
  130. 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.
  131. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
  132. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
  133. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
  134. Yuxiao Jiao & Guofu Zhou & Wu Zhu & Yingzi Zhu, 2025. "Interpretable Factors of Firm Characteristics," Papers 2508.02253, arXiv.org.
  135. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  136. 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.
  137. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
  138. Arasteh, Abdollah, 2025. "A data-driven prediction method for multi-period portfolio optimization using the real options approach," Finance Research Letters, Elsevier, vol. 80(C).
  139. McLean, R. David & Pontiff, Jeffrey & Reilly, Christopher, 2025. "Taking sides on return predictability," Journal of Financial Economics, Elsevier, vol. 173(C).
  140. Hao Jiang & Naveen Khanna & Qian Yang & Jiayu Zhou, 2024. "The Cyber Risk Premium," Management Science, INFORMS, vol. 70(12), pages 8791-8817, December.
  141. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
  142. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
  143. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
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