The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Verification on stock return predictability of text in analyst reports

애널리스트 보고서 텍스트의 주가예측력에 대한 검증

  • Young-Sun Lee (Department of Statistics, Sookmyung Women's Univesity) ;
  • Akihiko Yamada (Bigdata Convergence and Open Sharing System, Seoul National Univesity) ;
  • Cheol-Won Yang (School of Business Administration, Dankook Univerisity) ;
  • Hohsuk Noh (Department of Statistics, Sookmyung Women's Univesity)
  • Received : 2023.04.07
  • Accepted : 2023.05.13
  • Published : 2023.10.31
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Abstract

As sharing of analyst reports became widely available, reports generated by analysts have become a useful tool to reduce difference in financial information between market participants. The quantitative information of analyst reports has been used in many ways to predict stock returns. However, there are relatively few domestic studies on the prediction power of text information in analyst reports to predict stock returns. We test stock return predictability of text in analyst reports by creating variables representing the TONE from the text. To overcome the limitation of the linear-model-assumption-based approach, we use the random-forest-based F-test.

온라인 플랫폼을 통한 애널리스트 보고서의 공유가 가능해짐에 따라 애널리스트들이 생성한 보고서는 시장 참여자들 간 금융 정보 격차를 줄일 수 있는 유용한 도구가 되었으며, 애널리스트 보고서의 정량적 정보가 주식수익률 예측에 다수 활용되었다. 하지만 상대적으로 애널리스트 보고서 내 텍스트 정보의 주식수익률 예측 정보력에 대한 국내 자료 기반 연구는 상대적으로 많이 부족하다. 본 연구는 애널리스트 보고서에서 추출 가능한 텍스트로부터 어조 변수를 생성하여 주식수익률 예측에 정보력이 있는지를 검증하되, 기존 연구들의 선형모형 가정 기반 검정의 한계를 해결하고자 랜덤 포레스트 기반의 F-test를 사용하여 기업수익률 예측력을 검증하였다.

Keywords

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