Machine Learning Factor Models

Machine Learning Factor Models is a factor model in finance that distinguishes itself by using machine learning algorithms to identify and estimate latent risk factors from large-dimensional datasets, thereby enhancing predictive accuracy and model flexibility compared to traditional statistical or economic theory-driven approaches.

73

Publications

3.1K

Citations

193

Authors

118

Institutions

Publications per year

2017–2026

57

Authors

193

  1. DX
    Dacheng Xiu

    University of Chicago, United States

    5 publications · 706 citations

    5

    h-index

  2. GF
    Guanhao Feng

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 175 citations

    4

    h-index

  3. NG
    Nicholas G. Polson

    University of Chicago, United States

    3 publications · 151 citations

    3

    h-index

  4. JF
    Jianqing Fan

    Princeton University, United States

    3 publications · 142 citations

    3

    h-index

  5. SG
    Stefano Giglio

    Yale University, United States

    3 publications · 222 citations

    3

    h-index

1–5 of 193

Venues

  1. SSRN Electronic Journal

    Repository · 8 publications · 205 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 114 citations

    4

    h-index

  3. International Journal of Forecasting

    3 publications · 163 citations

    3

    h-index

  4. Annals of Operations Research

    2 publications · 76 citations

    2

    h-index

  5. Journal of Econometrics

    2 publications · 505 citations

    2

    h-index

1–5