Sector Categorization Using Gradient Boosted Trees Trained on Fundamental Firm Data

6 Pages Posted: 20 Jun 2019

See all articles by Lilian Kuo

Lilian Kuo

New Jersey Institute of Technology

Frank Shi

New Jersey Institute of Technology

Stephen Michael Taylor

New Jersey Institute of Technology

Date Written: June 13, 2019

Abstract

We demonstrate that the GICS sector and industry group categorizations can be systematically reconstructed from quarterly firm fundamental data using gradient boosted tree classification with high accuracy. Model complexity and performance tradeoffs are examined and relative feature importance is described. Potential extensions are outlined including validating internal consistency of existing classification methods and reducing model complexity.

Keywords: GICS Sector, Gradient Boosted Trees, Fundamental Data, Financial Ratios

JEL Classification: D40, C80

Suggested Citation

Kuo, Lilian and Shi, Frank and Taylor, Stephen Michael, Sector Categorization Using Gradient Boosted Trees Trained on Fundamental Firm Data (June 13, 2019). Available at SSRN: https://ssrn.com/abstract=3403818 or http://dx.doi.org/10.2139/ssrn.3403818

Lilian Kuo

New Jersey Institute of Technology ( email )

University Heights
Newark, NJ 07102
United States

Frank Shi

New Jersey Institute of Technology ( email )

University Heights
Newark, NJ 07102
United States

Stephen Michael Taylor (Contact Author)

New Jersey Institute of Technology ( email )

University Heights
Newark, NJ 07102
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
41
Abstract Views
220
PlumX Metrics