Dr. FENG Guanhao Gavin (馮冠豪博士)

Ph.D. - Business Administration (University of Chicago)

Assistant Professor

Dr. FENG Guanhao Gavin (馮冠豪博士)

Contact Information

Address: 7-276, Lau Ming Wai Academic Building
City University of Hong Kong
Phone: +852 34428346
Fax: +852 34420189
E-mail:
gufeng@cityu.edu.hk
Personal Web:
https://sites.google.com/view/gavinfeng/
SSRN Web:
http://papers.ssrn.com/author=2541802
Public CV: Download

Research Areas

  • Financial time series
  • Empirical asset pricing
  • Machine learning
  • Quantitative finance

Gavin Feng joined the City University of Hong Kong in 2017 as an Assistant Professor. His research interests include financial time series, empirical asset pricing, machine learning, and quantitative finance. One primary goal of his research is to develop statistical and machine learning methods for predicting asset returns in the cross-section and time series.

 

Gavin's research work is frequently invited to present at major academic conferences, including AFA, CICF, SoFie Conference, and Econometric Society Meeting, as well as various university seminars. Gavin also regularly speaks at professional investment research conferences, including CQAsia, Wolfe Research, and Unigestion Factor Investing, as well as many finance institution workshops. His work on taming the factor zoo in asset pricing earned the 2018 AQR Insight Award. His another work on deep learning asset pricing was awarded by the Unigestion Alternative Risk Premia Research Academy.

 

Gavin received a Ph.D. in Business Administration with concentrations on statistics and finance and an M.B.A. from the University of Chicago. He also holds a BS in Honors Economics, a BS in Mathematics, and a Minor in Statistics from Penn State University.

 

Teaching Activities (current academic year)

Academic Year Level Title
2018-2019 Postgraduate Degree Statistical Modelling in Marketing Engineering
    Statistical Modelling in Economics and Finance
  Research Degree Management Science Seminar Series I
    Management Science Seminar Series IV
    Statistical Methods for Business Research