Guanhao Gavin Feng

City University of Hong Kong (CityUHK)

Assistant Professor

83 Tat Chee Avenue

Kowloon Tong

Hong Kong

  • City University of Hong Kong (CityUHK)

SCHOLARLY PAPERS

9

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11

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Scholarly Papers (9)

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1.

Taming the Factor Zoo: A Test of New Factors

Fama-Miller Working Paper, Chicago Booth Research Paper No. 17-04
Number of pages: 75 Posted: 20 Mar 2017 Last Revised: 29 Jul 2019
Guanhao Gavin Feng, Stefano Giglio and Dacheng Xiu
City University of Hong Kong (CityUHK), Yale School of Management and University of Chicago - Booth School of Business
Downloads 2,960 (3,888)
Citation 16

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Factors, Stochastic Discount Factor, Post-Selection Inference, Regularized Two-Pass Estimation, Variable Selection, Machine Learning, LASSO, Elastic Net, PCA

2.

Deep Learning in Characteristics-Sorted Factor Models

Number of pages: 41 Posted: 23 Sep 2018 Last Revised: 01 Nov 2019
Guanhao Gavin Feng, Nick Polson and Jianeng Xu
City University of Hong Kong (CityUHK), University of Chicago - Booth School of Business and University of Chicago, Students
Downloads 719 (35,652)
Citation 1

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Alpha, Characteristics-Sorted Factor Models, Cross-Sectional Return, Deep Learning, Firm Characteristics, Machine Learning, Pricing Errors

3.

Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?

Journal of Risk, Forthcoming
Number of pages: 19 Posted: 25 Apr 2016 Last Revised: 25 May 2017
Ben Charoenwong and Guanhao Gavin Feng
National University of Singapore - Department of Finance and City University of Hong Kong (CityUHK)
Downloads 281 (111,624)

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Long-Horizon Volatility Forecast, Temporal Aggregation, Model Selection, Risk Management

4.

Factor Investing: Hierarchical Ensemble Learning

Number of pages: 29 Posted: 06 Feb 2019
Guanhao Gavin Feng and Jingyu He
City University of Hong Kong (CityUHK) and University of Chicago - Booth School of Business - Econometrics and Statistics
Downloads 251 (125,623)

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Hierarchical Model, Firm Characteristics, Market Timing, Portfolio Efficiency, Return Predictability, Risk Anomalies, Seemingly Unrelated Regressions

5.

Sparse Regularization in Marketing and Economics

Number of pages: 29 Posted: 20 Aug 2017 Last Revised: 10 Feb 2018
Guanhao Gavin Feng, Nick Polson, Yuexi Wang and Jianeng Xu
City University of Hong Kong (CityUHK), University of Chicago - Booth School of Business, University of Chicago, Students and University of Chicago, Students
Downloads 85 (303,152)

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Machine learning, Regularization, Proximal Algorithm, Nonconvex Optimization, Marketing Demand Forecasting

6.

Regularizing Bayesian Predictive Regressions

Number of pages: 35 Posted: 17 Aug 2016 Last Revised: 15 Sep 2017
Guanhao Gavin Feng and Nick Polson
City University of Hong Kong (CityUHK) and University of Chicago - Booth School of Business
Downloads 83 (307,657)

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Bayesian predictive regression; prior sensitivity analysis; maximum-a-posteriori; equity-premium predictability; bond risk premia; predictor selection.

7.

Taming the Factor Zoo: A Test of New Factors

Downloads 9 (607,053)
Citation 4

Taming the Factor Zoo: A Test of New Factors

NBER Working Paper No. w25481
Number of pages: 45 Posted: 30 Jan 2019
Guanhao Gavin Feng, Stefano Giglio and Dacheng Xiu
City University of Hong Kong (CityUHK), Yale School of Management and University of Chicago - Booth School of Business
Downloads 9 (632,915)
Citation 3
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Taming the Factor Zoo: A Test of New Factors

CEPR Discussion Paper No. DP14266
Number of pages: 49 Posted: 14 Jan 2020
Guanhao Gavin Feng, Stefano Giglio and Dacheng Xiu
City University of Hong Kong (CityUHK), Yale School of Management and University of Chicago - Booth School of Business
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Elastic Net, Factors, Lasso, Machine Learning, PCA, Post-Selection Inference, Regularized Two-Pass Estimation, Stochastic discount factor, variable selection

8.

Real-Time Macro Information and Bond Return Predictability: Does Deep Learning Help?

Number of pages: 42
Guanhao Gavin Feng, Andras Fulop and Junye Li
City University of Hong Kong (CityUHK), ESSEC Business School and ESSEC Business School
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Deep Learning, Machine Learning, Bond Return Predictability, Real-Time Macro Data, Overlapping and Non-overlapping Returns, Regression Tree

9.

Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?

Journal of Risk, Forthcoming
Posted: 13 May 2017
Ben Charoenwong and Guanhao Gavin Feng
National University of Singapore - Department of Finance and City University of Hong Kong (CityUHK)

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long-horizon volatility, iterated forecasts, temporal aggregation, model selection, risk management, mixed data frequency