IDEAS home Printed from https://ideas.repec.org/f/c/pfe488.html
   My authors  Follow this author

Guanhao Feng

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2025. "Growing the Efficient Frontier on Panel Trees," Papers 2501.16730, arXiv.org, revised Feb 2025.

    Cited by:

    1. 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).
    2. Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching," PIER Working Paper Archive 24-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  2. Lin William Cong & Guanhao Feng & Jingyu He & Junye Li, 2023. "Sparse Modeling Under Grouped Heterogeneity with an Application to Asset Pricing," NBER Working Papers 31424, National Bureau of Economic Research, Inc.

    Cited by:

    1. Siyu Bie & Francis X. Diebold & Jingyu He & Junye Li, 2024. "Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching," PIER Working Paper Archive 24-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Xiong, Youlin & Shen, Jun & Yoon, Seong-Min & Dong, Xiyong, 2024. "Macroeconomic determinants of the long-term correlation between stock and exchange rate markets in China: A DCC-MIDAS-X approach considering structural breaks," Finance Research Letters, Elsevier, vol. 61(C).

  3. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.

    Cited by:

    1. Alexander M. Chinco & Andreas Neuhierl & Michael Weber, 2019. "Estimating The Anomaly Base Rate," NBER Working Papers 26493, National Bureau of Economic Research, Inc.
    2. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    3. Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
    4. Paul Schneider & Christian Wagner & Josef Zechner, 2019. "Low Risk Anomalies?," Swiss Finance Institute Research Paper Series 19-50, Swiss Finance Institute.
    5. Wolfgang Breuer & Jannis Bischof & Christian Hofmann & Jochen Hundsdoerfer & Hans-Ulrich Küpper & Marko Sarstedt & Philipp Schreck & Tim Weitzel & Peter Witt, 2023. "Recent developments in Business Economics," Journal of Business Economics, Springer, vol. 93(6), pages 989-1013, August.
    6. 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.
    7. Gospodinov, Nikolay & Robotti, Cesare, 2021. "Common pricing across asset classes: Empirical evidence revisited," Journal of Financial Economics, Elsevier, vol. 140(1), pages 292-324.
    8. Liyun Wu & Muneeb Ahmad & Salman Ali Qureshi & Kashif Raza & Yousaf Ali Khan, 2022. "An analysis of machine learning risk factors and risk parity portfolio optimization," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-19, September.
    9. 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.
    10. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    11. Hasan Fallahgoul, 2025. "High-Dimensional Learning in Finance," Papers 2506.03780, arXiv.org, revised Jun 2025.
    12. 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.
    13. Chiah, Mardy & Long, Huaigang & Zaremba, Adam & Umar, Zaghum, 2023. "Trade competitiveness and the aggregate returns in global stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    14. 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.
    15. Cujean, Julien & Andrei, Daniel & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    16. Yanlong Wang & Jian Xu & Tiantian Gao & Hongkang Zhang & Shao-Lun Huang & Danny Dongning Sun & Xiao-Ping Zhang, 2025. "FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models," Papers 2503.06928, arXiv.org, revised Jun 2025.
    17. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    18. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, The University of Osaka.
    19. Li, Pengshi & Lin, Yan & Yu, Xing & Liu, Guifang, 2025. "Does bid-ask spread explains the smile? On DVF and DML," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    20. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
    21. Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
    22. Bo Yu & Dayong Zhang & Qiang Ji, 2025. "Forecasting portfolio variance: a new decomposition approach," Annals of Operations Research, Springer, vol. 348(1), pages 543-578, May.
    23. Abhimanyu Gupta & Myung Hwan Seo, 2019. "Robust Inference on Infinite and Growing Dimensional Time Series Regression," Papers 1911.08637, arXiv.org, revised Apr 2023.
    24. Yu, Hsin-Yi & Chen, Li-Wen & Chen, Chang-Yi, 2022. "The profitability effect: An evaluation of alternative explanations," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    25. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    26. 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.
    27. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    28. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    29. 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.
    30. Wang, Jinzhe & Zhu, Yifeng, 2024. "A comparison of factor models in China," Journal of Empirical Finance, Elsevier, vol. 79(C).
    31. 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).
    32. 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).
    33. 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).
    34. Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
    35. Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    36. 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).
    37. Ahmed, Shamim & Bu, Ziwen & Symeonidis, Lazaros & Tsvetanov, Daniel, 2023. "Which factor model? A systematic return covariation perspective," Journal of International Money and Finance, Elsevier, vol. 136(C).
    38. Cueto, José Manuel & Grané Chávez, Aurea & Cascos Fernández, Ignacio, 2019. "Models for expected returns with statistical factors," DES - Working Papers. Statistics and Econometrics. WS 28776, Universidad Carlos III de Madrid. Departamento de Estadística.
    39. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    40. 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.
    41. Vafai, Nima & Rakowski, David, 2024. "The sources of portfolio volatility and mutual fund performance," International Review of Financial Analysis, Elsevier, vol. 91(C).
    42. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    43. 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).
    44. 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.
    45. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    46. Stanislav Anatolyev & Anna Mikusheva, 2018. "Factor models with many assets: strong factors, weak factors, and the two-pass procedure," Papers 1807.04094, arXiv.org, revised Apr 2019.
    47. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    48. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 119289, London School of Economics and Political Science, LSE Library.
    49. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia & Brzeszczyński, Janusz, 2024. "Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    50. 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.
    51. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Jia, Zhimin, 2023. "Complete subset averaging methods in corporate bond return prediction," Finance Research Letters, Elsevier, vol. 54(C).
    52. Alexander M. Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2020. "Necessary Evidence For A Risk Factor’s Relevance," NBER Working Papers 27227, National Bureau of Economic Research, Inc.
    53. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    54. Chi-Ming Ho, 2023. "Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    55. Xin Zhang & Lan Wu & Zhixue Chen, 2021. "Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm," Papers 2104.12484, arXiv.org.
    56. Han, SeungOh, 2024. "Hedging strategies for U.S. factor and sector exchange-traded funds during geopolitical events," Finance Research Letters, Elsevier, vol. 65(C).
    57. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
    58. Bang, Jeongseok & Ryu, Doojin & Webb, Robert I., 2023. "ESG controversy as a potential asset-pricing factor," Finance Research Letters, Elsevier, vol. 58(PA).
    59. Liu, Chen & Wang, Chao & Tran, Minh-Ngoc & Kohn, Robert, 2025. "A long short-term memory enhanced realized conditional heteroskedasticity model," Economic Modelling, Elsevier, vol. 142(C).
    60. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
    61. Bang, Jeongseok & Ryu, Doojin, 2024. "ESG factors and the cross-section of expected stock returns: A LASSO-based approach," Finance Research Letters, Elsevier, vol. 65(C).
    62. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    63. Cueto, José Manuel & Grané Chávez, Aurea & Cascos Fernández, Ignacio, 2021. "How to explain the cross-section of equity returns through Common Principal Components," DES - Working Papers. Statistics and Econometrics. WS 32258, Universidad Carlos III de Madrid. Departamento de Estadística.
    64. Shafiullah, Muhammad & Senthilkumar, Arunachalam & Lucey, Brian M. & Naeem, Muhammad Abubakr, 2024. "Deciphering asymmetric spillovers in US industries: Insights from higher-order moments," Research in International Business and Finance, Elsevier, vol. 70(PA).
    65. Avis Devine & Andrew Sanderford & Chongyu Wang, 2024. "Sustainability and Private Equity Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 161-187, February.
    66. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    67. Chi, Yeguang & El-Jahel, Lina & Vu, Thanh, 2025. "Media emotion intensity and commodity futures pricing," Journal of Commodity Markets, Elsevier, vol. 37(C).
    68. Solène Collot & Tobias Hemauer, 2021. "A literature review of new methods in empirical asset pricing: omitted-variable and errors-in-variable bias," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(1), pages 77-100, March.
    69. Andrew Detzel & Robert Novy‐Marx & Mihail Velikov, 2023. "Model Comparison with Transaction Costs," Journal of Finance, American Finance Association, vol. 78(3), pages 1743-1775, June.
    70. 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.
    71. Yoontae Hwang & Yaxuan Kong & Stefan Zohren & Yongjae Lee, 2025. "Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization," Papers 2502.00828, arXiv.org.
    72. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    73. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    74. 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).
    75. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    76. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    77. Jorge M. Uribe & Montserrat Guillen, 2020. "Generalized Market Uncertainty Measurement in European Stock Markets in Real Time," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    78. Beaulieu, Marie-Claude & Dufour, Jean-Marie & Khalaf, Lynda, 2025. "Identification-robust and simultaneous inference in multifactor asset pricing models," Journal of Econometrics, Elsevier, vol. 248(C).
    79. Thuy Duong Dang & Fabian Hollstein & Marcel Prokopczuk & Zhiguo He, 2023. "Which Factors for Corporate Bond Returns?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(4), pages 615-652.
    80. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org, revised Sep 2024.
    81. 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.
    82. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    83. Fathi, Masoumeh & Grobys, Klaus & Äijö, Janne, 2025. "A common component of Fama and French factor variances," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    84. 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.
    85. Andre Guettler & Mahvish Naeem & Lars Norden & Bernardus Van Doornik, 2024. "Pre-Publication Revisions of Bank Financial Statements: a novel way to monitor banks?," Working Papers Series 590, Central Bank of Brazil, Research Department.
    86. Fulvio Ortu & Pietro Reggiani & Federico Severino, 2024. "Persistence-based capital allocation along the FOMC cycle," CIRANO Working Papers 2024s-02, CIRANO.
    87. Wang, Wenhao & Zhang, Qingyi & An, Pengda & Cai, Feifei, 2024. "Momentum and reversal strategies with low uncertainty," Finance Research Letters, Elsevier, vol. 68(C).
    88. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    89. 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.
    90. Maryam Movahedifar & Hossein Hassani & Masoud Yarmohammadi & Mahdi Kalantari & Rangan Gupta, 2021. "A robust approach for outlier imputation: Singular Spectrum Decomposition," Working Papers 202164, University of Pretoria, Department of Economics.
    91. Nygaard, Knut & Sørensen, Lars Qvigstad, 2024. "Betting on war? Oil prices, stock returns, and extreme geopolitical events," Energy Economics, Elsevier, vol. 136(C).
    92. 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.
    93. Marcos López de Prado & Joseph Simonian & Francesco A. Fabozzi & Frank J. Fabozzi, 2025. "Enhancing Markowitz's portfolio selection paradigm with machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 319-340, March.
    94. Cong Wang, 2024. "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers 2408.09271, arXiv.org, revised Sep 2024.
    95. Hengxu Lin & Dong Zhou & Weiqing Liu & Jiang Bian, 2021. "Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation," Papers 2107.05201, arXiv.org, revised Oct 2021.
    96. Rafael Branco & Alexandre Rubesam & Mauricio Zevallos, 2024. "Forecasting realized volatility: Does anything beat linear models?," Post-Print hal-04835657, HAL.
    97. Ledoit, Olivier & Wolf, Michael, 2025. "Markowitz portfolios under transaction costs," The Quarterly Review of Economics and Finance, Elsevier, vol. 100(C).
    98. Xialu Liu & John Guerard & Rong Chen & Ruey Tsay, 2024. "Improving Estimation of Portfolio Risk Using New Statistical Factors," Papers 2409.17182, arXiv.org.
    99. Fabrizio Ghezzi & Anindo Sarkar & Thomas Quistgaard Pedersen & Allan Timmermann, 2025. "Optimal asset allocation and nonlinear return predictability from the dividend-price ratio," Annals of Operations Research, Springer, vol. 346(1), pages 415-445, March.
    100. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).
    101. Covachev, Svetoslav & Martel, Jocelyn & Brito-Ramos, Sofia, 2025. "Are ESG factors truly unique?," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    102. 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.
    103. Jiang, Hao & Li, Sophia Zhengzi & Yuan, Peixuan, 2025. "Granular information and sectoral movements," Journal of Economic Dynamics and Control, Elsevier, vol. 171(C).
    104. Alexandre Belloni & Mingli Chen & Oscar Hernan Madrid Padilla & Zixuan & Wang, 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," Papers 1912.02151, arXiv.org, revised Aug 2022.
    105. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    106. Donggyu Kim, 2024. "High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202416, University of California at Riverside, Department of Economics.
    107. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    108. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    109. Cheng, Tingting & Jiang, Shan & Zhao, Albert Bo & Zhao, Junyi, 2025. "Is machine learning a necessity? A regression-based approach for stock return prediction," Journal of Empirical Finance, Elsevier, vol. 81(C).
    110. Pablo Solórzano-Taborga & Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Data Envelopment Analysis and Multifactor Asset Pricing Models," IJFS, MDPI, vol. 8(2), pages 1-18, April.
    111. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    112. Frank Kleibergen & Zhaoguo Zhan, 2022. "Misspecification and Weak Identification in Asset Pricing," Papers 2206.13600, arXiv.org.
    113. Fan, Qingliang & Wu, Ruike & Yang, Yanrong & Zhong, Wei, 2024. "Time-varying minimum variance portfolio," Journal of Econometrics, Elsevier, vol. 239(2).
    114. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    115. Mekelburg, Erik & Strauss, Jack, 2024. "Pooling and winsorizing machine learning forecasts to predict stock returns with high-dimensional data," Journal of Empirical Finance, Elsevier, vol. 79(C).
    116. Niels Joachim Gormsen & Eben Lazarus, 2023. "Duration‐Driven Returns," Journal of Finance, American Finance Association, vol. 78(3), pages 1393-1447, June.
    117. Zhu, Lin & Jiang, Fuwei & Tang, Guohao & Jin, Fujing, 2024. "From macro to micro: Sparse macroeconomic risks and the cross-section of stock returns," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    118. Christiane Baumeister, 2021. "Measuring Market Expectations," Working Papers 202163, University of Pretoria, Department of Economics.
    119. 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.).
    120. 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).
    121. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2020. "Models for Expected Returns with Statistical Factors," JRFM, MDPI, vol. 13(12), pages 1-17, December.
    122. 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.
    123. 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.
    124. Dong, Dayong & Wu, Keke & Fang, Jianchun & Gozgor, Giray & Yan, Cheng, 2022. "Investor attention factors and stock returns: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    125. Lu, Yueliang (Jacques) & Tian, Weidong, 2023. "An on-line machine learning return prediction," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    126. Yuan Liao & Viktor Todorov, 2024. "Changes in the span of systematic risk exposures," Quantitative Economics, Econometric Society, vol. 15(3), pages 817-847, July.
    127. Sang Il Lee & Seong Joon Yoo, 2019. "Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets," Papers 1903.06478, arXiv.org, revised Sep 2019.
    128. Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
    129. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    130. Gabriele D'Acunto & Paolo Bajardi & Francesco Bonchi & Gianmarco De Francisci Morales, 2021. "The Evolving Causal Structure of Equity Risk Factors," Papers 2111.05072, arXiv.org.
    131. Kazuki Amagai & Tomoya Suzuki, 2023. "Long-Term Modeling of Financial Machine Learning for Active Portfolio Management," Papers 2301.12346, arXiv.org.
    132. Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).
    133. Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
    134. Sun, Yang & Zhang, Xuan & Zhang, Zhekai, 2022. "The reduced-rank beta in linear stochastic discount factor models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    135. Luo, Di, 2022. "ESG, liquidity, and stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    136. Felix Divo & Eric Endress & Kevin Endler & Kristian Kersting & Devendra Singh Dhami, 2024. "Forecasting Company Fundamentals," Papers 2411.05791, arXiv.org, revised Jun 2025.
    137. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    138. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    139. 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.
    140. 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.
    141. Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020. "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics 130, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    142. Mao, Jie & Shao, Jingjing & Wang, Weiguan, 2025. "Risk premium principal components for the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 89(C).
    143. 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.
    144. Konan Chan & Mei‐Xuan Li & Chu‐Bin Lin & Yanzhi Wang, 2022. "Organization capital effect in stock returns—The role of R&D," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(7-8), pages 1237-1263, July.
    145. Venky Nagar & Jordan Schoenfeld, 2024. "Measuring weather exposure with annual reports," Review of Accounting Studies, Springer, vol. 29(1), pages 1-32, March.
    146. Lashkaripour, Mohammadhossein, 2024. "Some stylized facts about bitcoin halving," Finance Research Letters, Elsevier, vol. 69(PB).
    147. Alla Petukhina & Simon Trimborn & Wolfgang Karl Härdle & Hermann Elendner, 2021. "Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies," Quantitative Finance, Taylor & Francis Journals, vol. 21(11), pages 1825-1853, November.
    148. Xialu Liu & John Guerard & Rong Chen & Ruey Tsay, 2025. "Improving estimation of portfolio risk using new statistical factors," Annals of Operations Research, Springer, vol. 346(1), pages 245-261, March.
    149. Pascal Böni & Heinz Zimmermann, 2021. "Are stock prices driven by expected growth rather than discount rates? Evidence based on the COVID-19 crisis," Risk Management, Palgrave Macmillan, vol. 23(1), pages 1-29, June.
    150. 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.
    151. Mörstedt, Torsten & Lutz, Bernhard & Neumann, Dirk, 2024. "Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(2), pages 670-685.
    152. Dichev, Ilia & Huang, Xinyi & Lee, Donald K.K & Zhao, Jianxin, 2023. "You have a point - but a point is not enough: The case for distributional forecasts of earnings," SocArXiv 4b2y8, Center for Open Science.
    153. Mark H.A. Davis & Sébastien Lleo, 2021. "Risk‐sensitive benchmarked asset management with expert forecasts," Mathematical Finance, Wiley Blackwell, vol. 31(4), pages 1162-1189, October.
    154. Wang, Xiaowei & Wang, Rui & Zhang, Yichun, 2024. "Is there more to asset price linkages in China than meets the eye: Cross-asset momentum and the role of hybrid funds," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    155. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    156. R. Douglas Martin & Daniel Z. Xia, 2022. "Efficient bias robust regression for time series factor models," Journal of Asset Management, Palgrave Macmillan, vol. 23(3), pages 215-234, May.
    157. Zacharias Sautner & Laurence Van Lent & Grigory Vilkov & Ruishen Zhang, 2023. "Firm‐Level Climate Change Exposure," Journal of Finance, American Finance Association, vol. 78(3), pages 1449-1498, June.
    158. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "Reprint of: The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 244(2).
    159. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factor Centrality and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_17, University of São Paulo (FEA-USP).
    160. 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).
    161. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.
    162. Qingliang Fan & Ruike Wu & Yanrong Yang, 2024. "Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets," Papers 2410.01826, arXiv.org.
    163. Carsten Lausberg & Felix Brandt, 2024. "Forecasting risk and return of listed real estate: [Die Prognose von Risiko und Rendite von Immobilienaktien:]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 10(1), pages 1-38, December.
    164. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2021. "How to Explain the Cross-Section of Equity Returns through Common Principal Components," Mathematics, MDPI, vol. 9(9), pages 1-22, April.
    165. 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).
    166. 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.
    167. Jie Fang & Jianwu Lin & Shutao Xia & Yong Jiang & Zhikang Xia & Xiang Liu, 2020. "Neural Network-based Automatic Factor Construction," Papers 2008.06225, arXiv.org, revised Oct 2020.
    168. 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.
    169. Manuel Ammann & Mathis Mörke, 2019. "Credit Variance Risk Premiums," Working Papers on Finance 1908, University of St. Gallen, School of Finance.
    170. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-Learning the Skill of Mutual Fund Managers," CEPR Discussion Papers 18129, C.E.P.R. Discussion Papers.
    171. Shanyan Lai, 2025. "Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks," Papers 2505.01921, arXiv.org, revised May 2025.
    172. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    173. Lin William Cong & Guanhao Feng & Jingyu He & Xin He, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.
    174. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    175. Bang, Jeongseok & Kang, Yeonchan & Ryu, Doojin, 2024. "Potential pricing factors in the Korean market," Finance Research Letters, Elsevier, vol. 67(PB).
    176. Rama K. Malladi, 2024. "Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1021-1045, March.
    177. Ma, Xiuli & Zhang, Xindong & Liu, Weimin, 2021. "Further tests of asset pricing models: Liquidity risk matters," Economic Modelling, Elsevier, vol. 95(C), pages 255-273.
    178. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factors’ CPDAG Roots and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_18, University of São Paulo (FEA-USP).
    179. Szczygielski, Jan Jakub & Charteris, Ailie & Obojska, Lidia & Brzeszczyński, Janusz, 2024. "Recession fears and stock markets: An application of directional wavelet coherence and a machine learning-based economic agent-determined Google fear index," Research in International Business and Finance, Elsevier, vol. 72(PA).
    180. Prodosh Eugene Simlai, 2025. "Investor sophistication, investor sentiment, and cash-based operating profitability," Review of Quantitative Finance and Accounting, Springer, vol. 64(3), pages 1079-1103, April.
    181. Assoe, Kodjovi & Attig, Najah & Sy, Oumar, 2024. "The battle of factors," Global Finance Journal, Elsevier, vol. 62(C).
    182. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.
    183. Xiao, Yuewen & Zhao, Jing, 2021. "Price dynamics of individual stocks: Jumps and information," Finance Research Letters, Elsevier, vol. 38(C).
    184. 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.
    185. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    186. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    187. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    188. 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).
    189. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    190. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    191. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    192. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    193. Matteo Bagnara, 2024. "Asset Pricing and Machine Learning: A critical review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(1), pages 27-56, February.
    194. Martha López & Eduardo Sarmiento G., 2023. "Excess Asset Returns Predictability in an Emerging Economy: The Case of Colombia," Borradores de Economia 1243, Banco de la Republica de Colombia.
    195. Jiajun Gu & Zichen Yang & Xintong Lin & Sixun Chen & YuTing Lu, 2024. "AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics," Papers 2412.12438, arXiv.org.
    196. 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.
    197. 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.
    198. 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.
    199. Cakici, Nusret & Zaremba, Adam & Bianchi, Robert J. & Pham, Nga, 2021. "False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).

  4. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.

    Cited by:

    1. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    2. Cássio Roberto de Andrade Alves & Márcio Laurini, 2023. "Estimating the Capital Asset Pricing Model with Many Instruments: A Bayesian Shrinkage Approach," Mathematics, MDPI, vol. 11(17), pages 1-20, September.

  5. Guanhao Feng & Jingyu He & Nicholas G. Polson, 2018. "Deep Learning for Predicting Asset Returns," Papers 1804.09314, arXiv.org, revised Apr 2018.

    Cited by:

    1. 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).
    2. Tingting Cheng & Chaohua Dong & Jiti Gao & Oliver Linton, 2022. "GMM Estimation for High-Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers 11/22, Monash University, Department of Econometrics and Business Statistics.
    3. Weidong Lin & Abderrahim Taamouti, 2023. "Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach," Working Papers 202310, University of Liverpool, Department of Economics.
    4. Özgür Ömer Ersin & Melike Bildirici, 2023. "Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19," Mathematics, MDPI, vol. 11(8), pages 1-26, April.
    5. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    6. 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).
    7. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    8. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    9. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    10. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    11. Luyang Chen & Markus Pelger & Jason Zhu, 2024. "Deep Learning in Asset Pricing," Management Science, INFORMS, vol. 70(2), pages 714-750, February.
    12. 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.
    13. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(1), pages 1-60, February.
    14. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    15. 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.
    16. 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.
    17. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    18. H. Rad & R. Low & J. Miffre & R. Faff, 2023. "The commodity risk premium and neural networks," Post-Print hal-04322519, HAL.
    19. Lin William Cong & Ke Tang & Jingyuan Wang & Yang Zhang, 2021. "Deep Sequence Modeling: Development and Applications in Asset Pricing," Papers 2108.08999, arXiv.org.
    20. Alexis Bogroff & Dominique Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
    21. 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.
    22. 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).
    23. 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.
    24. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
    25. 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.
    26. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
    27. 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).
    28. 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.
    29. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR), revised 2025.
    30. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    31. 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.

Articles

  1. Feng, Guanhao & He, Jingyu & Polson, Nicholas G. & Xu, Jianeng, 2024. "Deep Learning in Characteristics-Sorted Factor Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 59(7), pages 3001-3036, November.

    Cited by:

    1. Cong, Lin William & Feng, Guanhao & He, Jingyu & He, Xin, 2025. "Growing the efficient frontier on panel trees," Journal of Financial Economics, Elsevier, vol. 167(C).
    2. Wu, Hongxu & Wang, Qiao & Li, Jianping & Deng, Zhibin, 2025. "Enhancing stock return prediction in the Chinese market: A GAN-based approach," Research in International Business and Finance, Elsevier, vol. 75(C).

  2. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    See citations under working paper version above.
  3. 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.
    See citations under working paper version above.
  4. Feng Guanhao & Polson Nicholas & Xu Jianeng, 2016. "The market for English Premier League (EPL) odds," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(4), pages 167-178, December.

    Cited by:

    1. Chu Dani & Wu Yifan & Swartz Tim B., 2018. "Modified Kelly criteria," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(1), pages 1-11, March.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.