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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, 2022. "Growing the Efficient Frontier on Panel Trees," NBER Working Papers 30805, National Bureau of Economic Research, Inc.

    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. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2019. "Taming the Factor Zoo: A Test of New Factors," NBER Working Papers 25481, National Bureau of Economic Research, Inc.

    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. 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.
    9. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
    10. 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).
    11. 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.
    12. 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.
    13. 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.
    14. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    15. 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.
    16. Abhimanyu Gupta & Myung Hwan Seo, 2019. "Robust Inference on Infinite and Growing Dimensional Time Series Regression," Papers 1911.08637, arXiv.org, revised Apr 2023.
    17. 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).
    18. 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.
    19. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    20. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    21. 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).
    22. 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).
    23. 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.
    24. Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    25. 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).
    26. 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.
    27. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    28. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    29. 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.
    30. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    31. 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.
    32. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    33. 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.
    34. Ian Martin & Stefan Nagel, 2019. "Market Efficiency in the Age of Big Data," CESifo Working Paper Series 8015, CESifo.
    35. 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.
    36. 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.
    37. 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).
    38. 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.
    39. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    40. 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.
    41. 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.
    42. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Jan 2024.
    43. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
    44. 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.
    45. 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.
    46. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    47. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    48. 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.
    49. 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.
    50. 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.
    51. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    52. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
    53. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    54. 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.
    55. 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.
    56. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
    57. 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).
    58. 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.
    59. 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.
    60. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    61. 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.
    62. 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.
    63. 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.
    64. 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.
    65. 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.
    66. 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.
    67. 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.
    68. 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.
    69. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    70. 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.
    71. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    72. Frank Kleibergen & Zhaoguo Zhan, 2022. "Misspecification and Weak Identification in Asset Pricing," Papers 2206.13600, arXiv.org.
    73. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    74. Niels Joachim Gormsen & Eben Lazarus, 2023. "Duration‐Driven Returns," Journal of Finance, American Finance Association, vol. 78(3), pages 1393-1447, June.
    75. Christiane Baumeister, 2021. "Measuring Market Expectations," Working Papers 202163, University of Pretoria, Department of Economics.
    76. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2020. "Models for Expected Returns with Statistical Factors," JRFM, MDPI, vol. 13(12), pages 1-17, December.
    77. 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.
    78. 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.
    79. 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).
    80. Lu, Yueliang (Jacques) & Tian, Weidong, 2023. "An on-line machine learning return prediction," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    81. 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.
    82. 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.
    83. 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.
    84. 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.
    85. Kazuki Amagai & Tomoya Suzuki, 2023. "Long-Term Modeling of Financial Machine Learning for Active Portfolio Management," Papers 2301.12346, arXiv.org.
    86. Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).
    87. Lin, Qi, 2021. "The q5 model and its consistency with the intertemporal CAPM," Journal of Banking & Finance, Elsevier, vol. 127(C).
    88. 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).
    89. Luo, Di, 2022. "ESG, liquidity, and stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    90. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    91. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    92. 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.
    93. 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.
    94. 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.
    95. 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.
    96. 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.
    97. 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.
    98. 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.
    99. 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.
    100. 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.
    101. 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.
    102. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    103. 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.
    104. 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.
    105. 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).
    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. M. Hashem Pesaran & Ron P. Smith, 2021. "Factor Strengths, Pricing Errors, and Estimation of Risk Premia," CESifo Working Paper Series 8947, CESifo.
    108. 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.
    109. 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).
    110. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    111. 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.
    112. 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.
    113. Manuel Ammann & Mathis Mörke, 2019. "Credit Variance Risk Premiums," Working Papers on Finance 1908, University of St. Gallen, School of Finance.
    114. 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.
    115. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    116. 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.
    117. 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.
    118. 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).
    119. 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.
    120. Xiao, Yuewen & Zhao, Jing, 2021. "Price dynamics of individual stocks: Jumps and information," Finance Research Letters, Elsevier, vol. 38(C).
    121. 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.
    122. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    123. 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).
    124. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    125. 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).
    126. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    127. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    128. 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).
    129. 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.
    130. 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.
    131. 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.
    132. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.
    133. 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).

  3. 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.

  4. 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. 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.
    3. 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.
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Articles

  1. 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.

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