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Detecting Equity Style Information Within Institutional Media

In: Essays on Financial Analytics

Author

Listed:
  • Cédric Gillain

    (HEC Liège, Management School of the University of Liège)

  • Ashwin Ittoo

    (HEC Liège, Management School of the University of Liège)

  • Marie Lambert

    (HEC Liège, Management School of the University of Liège)

Abstract

This study examines the detection of information related to small and large equity styles. Using a novel database of magazines targeting institutional investors, the institutional media, we compare the performance of dictionary-based and supervised machine learning algorithms (Naïve Bayes and support vector machine). Our three main findings are (1) restricted word lists are the most efficient approach, (2) bigram term frequency matrices are the best weighting scheme for algorithms, and (3) Naïve Bayes exhibits overfitting while support vector machine delivers encouraging results. Overall, our results provide material to construct small-cap and large-cap coverage indexes from specialized financial media.

Suggested Citation

  • Cédric Gillain & Ashwin Ittoo & Marie Lambert, 2023. "Detecting Equity Style Information Within Institutional Media," Lecture Notes in Operations Research, in: Pascal Alphonse & Karima Bouaiss & Pascal Grandin & Constantin Zopounidis (ed.), Essays on Financial Analytics, pages 131-157, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-29050-3_8
    DOI: 10.1007/978-3-031-29050-3_8
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