Guanhao 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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37

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8

Scholarly Papers (9)

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1.

Taming the Factor Zoo: A Test of New Factors

Fama-Miller Working Paper
Number of pages: 75 Posted: 20 Mar 2017 Last Revised: 29 Jul 2019
Guanhao 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 3,193 (3,682)
Citation 19

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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: 44 Posted: 23 Sep 2018 Last Revised: 25 Mar 2020
Guanhao 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 902 (27,635)
Citation 3

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

3.

Factor Investing: Hierarchical Ensemble Learning

Number of pages: 29 Posted: 06 Feb 2019
Guanhao Feng and Jingyu He
City University of Hong Kong (CityUHK) and University of Chicago - Booth School of Business - Econometrics and Statistics
Downloads 298 (111,132)

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

4.

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 Feng
National University of Singapore - Department of Finance and City University of Hong Kong (CityUHK)
Downloads 291 (113,548)

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

5.

Sparse Regularization in Marketing and Economics

Number of pages: 29 Posted: 20 Aug 2017 Last Revised: 10 Feb 2018
Guanhao 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 92 (304,662)

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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 Feng and Nick Polson
City University of Hong Kong (CityUHK) and University of Chicago - Booth School of Business
Downloads 89 (308,927)

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

7.

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

Number of pages: 43 Posted: 03 Feb 2020 Last Revised: 18 May 2020
Guanhao Feng, Andras Fulop and Junye Li
City University of Hong Kong (CityUHK), ESSEC Business School and ESSEC Business School
Downloads 70 (361,308)

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Deep Learning, Machine Learning, Bond Return Predictability, Real-Time Macro Data, Overlapping and Non-overlapping Returns

8.

Taming the Factor Zoo: A Test of New Factors

Downloads 13 (608,359)
Citation 14

Taming the Factor Zoo: A Test of New Factors

NBER Working Paper No. w25481
Number of pages: 45 Posted: 30 Jan 2019
Guanhao 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 12 (640,041)
Citation 4
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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 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 1 (736,017)
Citation 10
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Elastic Net, Factors, Lasso, Machine Learning, PCA, Post-Selection Inference, Regularized Two-Pass Estimation, Stochastic discount factor, variable selection

9.

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

Journal of Risk, Forthcoming
Posted: 13 May 2017
Ben Charoenwong and Guanhao 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