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Dissecting Characteristics Nonparametrically

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Cited by:

  1. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
  2. 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.
  3. Andreas Neuhierl & Michael Weber & Michael Weber, 2017. "Monetary Momentum," CESifo Working Paper Series 6648, CESifo.
  4. Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
  5. 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.
  6. 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).
  7. 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).
  8. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
  9. Kent Daniel & David Hirshleifer & Lin Sun, 2020. "Short- and Long-Horizon Behavioral Factors," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1673-1736.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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).
  15. 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.
  16. 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.
  17. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
  18. Weber, Michael, 2018. "Cash flow duration and the term structure of equity returns," Journal of Financial Economics, Elsevier, vol. 128(3), pages 486-503.
  19. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  20. 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.
  21. Chris Florakis & Christodoulos Louca & Roni Michaely & Michael Weber, 2020. "Cybersecurity Risk," Working Papers 2020-178, Becker Friedman Institute for Research In Economics.
  22. 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.
  23. 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.
  24. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "A diagnostic criterion for approximate factor structure," Journal of Econometrics, Elsevier, vol. 212(2), pages 503-521.
  25. 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.
  26. 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.
  27. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
  28. 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.
  29. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
  30. Li, Bo & Liu, Zhenya & Teka, Hanen & Wang, Shixuan, 2023. "The evolvement of momentum effects in China: Evidence from functional data analysis," Research in International Business and Finance, Elsevier, vol. 64(C).
  31. 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.
  32. 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.
  33. 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.
  34. Li, Zhiyong & Wan, Yifan & Wang, Tianyi & Yu, Mei, 2023. "Factor-timing in the Chinese factor zoo: The role of economic policy uncertainty," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
  35. 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).
  36. Stadtmüller, Immo & Auer, Benjamin R. & Schuhmacher, Frank, 2022. "On the benefits of active stock selection strategies for diversified investors," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 342-354.
  37. 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.
  38. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
  39. 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.
  40. Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
  41. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
  42. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
  43. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  44. Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
  45. Alessi, Lucia & Balduzzi, Pierluigi & Savona, Roberto, 2019. "Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data," Working Papers 2019-03, Joint Research Centre, European Commission.
  46. 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.
  47. Langlois, Hugues, 2020. "Measuring skewness premia," Journal of Financial Economics, Elsevier, vol. 135(2), pages 399-424.
  48. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
  49. 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.
  50. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
  51. 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.
  52. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
  53. 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.
  54. 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).
  55. Mohrschladt, Hannes & Nolte, Sven, 2018. "A new risk factor based on equity duration," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 126-135.
  56. 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).
  57. 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.
  58. 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.
  59. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
  60. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
  61. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
  62. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
  63. 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).
  64. 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.
  65. Kelly, Bryan T. & Moskowitz, Tobias J. & Pruitt, Seth, 2021. "Understanding momentum and reversal," Journal of Financial Economics, Elsevier, vol. 140(3), pages 726-743.
  66. 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.
  67. 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.
  68. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
  69. 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.
  70. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
  71. Paul Schneider & Christian Wagner & Josef Zechner, 2020. "Low‐Risk Anomalies?," Journal of Finance, American Finance Association, vol. 75(5), pages 2673-2718, October.
  72. 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.
  73. 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.
  74. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
  75. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
  76. 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.
  77. 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.
  78. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
  79. 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.
  80. Michaely, Roni & Rossi, Stefano & Weber, Michael, 2021. "Signaling safety," Journal of Financial Economics, Elsevier, vol. 139(2), pages 405-427.
  81. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
  82. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
  83. Dreher, Sandra & Eichfelder, Sebastian & Noth, Felix, 2022. "Does IFRS information on tax loss carryforwards and negative performance improve predictions of earnings and cash flows?," arqus Discussion Papers in Quantitative Tax Research 276, arqus - Arbeitskreis Quantitative Steuerlehre.
  84. 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.
  85. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
  86. 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.
  87. Andrew Y. Chen & Jack McCoy, 2022. "Missing Values Handling for Machine Learning Portfolios," Papers 2207.13071, arXiv.org, revised Jan 2024.
  88. Kristoffer Pons Bertelsen, 2022. "The Prior Adaptive Group Lasso and the Factor Zoo," CREATES Research Papers 2022-05, Department of Economics and Business Economics, Aarhus University.
  89. 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.
  90. 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.
  91. Cong, Lin William & George, Nathan Darden & Wang, Guojun, 2023. "RIM-based value premium and factor pricing using value-price divergence," Journal of Banking & Finance, Elsevier, vol. 149(C).
  92. Andrew Y. Chen & Tom Zimmermann, 2022. "Open Source Cross-Sectional Asset Pricing," Critical Finance Review, now publishers, vol. 11(2), pages 207-264, May.
  93. 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.
  94. 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).
  95. Chulwoo Han, 2022. "Bimodal Characteristic Returns and Predictability Enhancement via Machine Learning," Management Science, INFORMS, vol. 68(10), pages 7701-7741, October.
  96. 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.
  97. Vigo Pereira, Caio, 2021. "Portfolio efficiency with high-dimensional data as conditioning information," International Review of Financial Analysis, Elsevier, vol. 77(C).
  98. Bagnara, Matteo & Goodarzi, Milad, 2023. "Clustering-based sector investing," SAFE Working Paper Series 397, Leibniz Institute for Financial Research SAFE.
  99. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
  100. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
  101. 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.
  102. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
  103. 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.
  104. 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.
  105. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
  106. 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).
  107. 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.
  108. 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.
  109. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
  110. 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.
  111. 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.
  112. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J. & Uppal, Raman, 2017. "A Portfolio Perspective on the Multitude of Firm Characteristics," CEPR Discussion Papers 12417, C.E.P.R. Discussion Papers.
  113. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
  114. Andrew Y. Chen & Mihail Velikov, 2020. "Zeroing in on the Expected Returns of Anomalies," Finance and Economics Discussion Series 2020-039, Board of Governors of the Federal Reserve System (U.S.).
  115. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
  116. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
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