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Guanhao Feng

Personal Details

First Name:Guanhao
Middle Name:
Last Name:Feng
Suffix:
RePEc Short-ID:pfe488
https://sites.google.com/view/gavinfeng/

Affiliation

香港城市大学 (City University of Hong Kong)

http://www.cityu.edu.hk
Hong Kong SAR

Research output

as
Jump to: Working papers Articles

Working papers

  1. 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.
  2. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.
  3. Guanhao Feng & Jingyu He & Nicholas G. Polson, 2018. "Deep Learning for Predicting Asset Returns," Papers 1804.09314, arXiv.org, revised Apr 2018.

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.

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. 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. Paul Schneider & Christian Wagner & Josef Zechner, 2019. "Low Risk Anomalies?," Swiss Finance Institute Research Paper Series 19-50, Swiss Finance Institute.
    2. Pablo Solórzano-Taborga & Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Data Envelopment Analysis and Multifactor Asset Pricing Models," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 8(2), pages 1-18, April.
    3. Andrei, Daniel & Cujean, Julien & Fournier, Mathieu, 2019. "The Low-Minus-High Portfolio and the Factor Zoo," CEPR Discussion Papers 14153, C.E.P.R. Discussion Papers.
    4. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, Osaka University.
    5. José Manuel Cueto & Aurea Grané & Ignacio Cascos, 2020. "Models for Expected Returns with Statistical Factors," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(12), pages 1-17, December.
    6. 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 (Ispra site).
    7. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    8. Abhimanyu Gupta & Myung Hwan Seo, 2019. "Structural stability of infinite-order regression," Papers 1911.08637, arXiv.org.
    9. 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.
    10. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strenght: Theory and Practice," CESifo Working Paper Series 8146, CESifo.
    11. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    12. Cascos Fernández, Ignacio & Grané Chávez, Aurea & Cueto, J.M., 2019. "Models for expected returns with statistical factors," DES - Working Papers. Statistics and Econometrics. WS 28776, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. 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.
    14. 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.
    15. 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).
    16. 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.
    17. 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.
    18. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    19. Manuel Ammann & Mathis Mörke, 2019. "Credit Variance Risk Premiums," Working Papers on Finance 1908, University of St. Gallen, School of Finance.
    20. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    21. 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.
    22. 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).
    23. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    24. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    25. Alla Petukhina & Simon Trimborn & Wolfgang Karl Hardle & Hermann Elendner, 2020. "Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies," Papers 2009.04461, arXiv.org, revised Sep 2020.
    26. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.

  2. 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.

  3. 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. 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.
    2. 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".
    3. Qiong Wu & Zheng Zhang & Andrea Pizzoferrato & Mihai Cucuringu & Zhenming Liu, 2019. "A Deep Learning Framework for Pricing Financial Instruments," Papers 1909.04497, arXiv.org.
    4. 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.
    5. Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised May 2020.
    11. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    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.
    13. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.

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.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (2) 2018-05-14 2019-02-11. Author is listed
  2. NEP-ECM: Econometrics (1) 2019-02-04. Author is listed
  3. NEP-ETS: Econometric Time Series (1) 2019-02-04. Author is listed
  4. NEP-FMK: Financial Markets (1) 2018-05-14. Author is listed
  5. NEP-FOR: Forecasting (1) 2019-02-11. Author is listed
  6. NEP-IFN: International Finance (1) 2018-05-14. Author is listed

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