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

Keywords

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

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