인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
Stock investors seek to minimize risk and continually evaluate whether their target companies are suitable. To achieve this, it is crucial to understand the trends in corporate valuation. This study aims to gain valuable insights into the evaluation of portfolio companies through a factor analysis of stock price fluctuations. In this study, text data were used to analyze the factors behind stock price fluctuations. To model the data, it was necessary to quantify the text using methods such as bag-of-words. This made the specific information vague. Therefore, it was impossible to examine the specific factors that caused stock price fluctuations at any given time. For example, financial statements (e.g., investor relations materials) were provided as textual information to disclose information about how companies perform. As these contexts contain various content, each had a different influence on stock price fluctuations. However, if the data were used in model learning, differences in content might not be represented and might be considered to have the same effect as financial data. We focus on this issue of past studies and propose a new method to analyze the detailed information of the stock price for each time.. We first constructed a model using information other than textual data. Second, we focused on the model residuals to clarify the textual information to be focused on. The proposed model was based on a time-varying coefficient model that used numerical data such as economic trend indicators. In addition, we validated the proposed model by analyzing real-world data.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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