Sector categorization using gradient boosted trees trained on fundamental firm data

Ming Fang, Lilian Kuo, Frank Shih, Stephen Taylor

Research output: Contribution to journalArticlepeer-review

Abstract

We examine to what extent the GICS sector categorization of equity securities may be systematically reconstructed from historical quarterly firm fundamental data using gradient boosted tree classification. Model complexity and performance tradeoffs are examined and relative feature importance is described. Potential extensions are outlined including ideas to improve feature engineering, validating internal consistency and integrating additional data sources to further improve classification accuracy.

Original languageEnglish (US)
Pages (from-to)91-99
Number of pages9
JournalAlgorithmic Finance
Volume8
Issue number3-4
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Finance
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computational Mathematics

Keywords

  • financial ratios
  • fundamental data
  • GICS sector
  • gradient boosted trees

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