ALBERT 기반 감성분석을 통한 딥러닝 기업부도 예측
Deep Learning Corporate Bankruptcy Prediction through ALBERT-based Sentiment Analysis
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참고문헌
키워드
초록
과거 기업부도 예측에 있어서 재무정보와 주식시장 정보를 기반으로 많은 연구가 활발히 진행되어왔다. 재무정보는 공시된 정보를 활용하므로 기업의 현황을 객관적이고 표준화된 형태의 자료로 활용할 수 있다는 큰 장점이 있지만, 재무정보의 경우 분기 또는 연단위로 작성되고 각 기업의 결산 시점 이후 공시까지 일정 시간이 소요되므로 적시성이 떨어지는 한계점이 있다. 이에 본 연구에서는 각 기업의 최신 정보를 반영할 수 있는 뉴스데이터를 수집하여 정형데이터뿐만 아니라 비정형데이터까지 분석하여 인공지능 기반 기업부도 예측을 실증하였다.
Abstract
Many studies in the past have actively focused on predicting corporate bankruptcy based on financial information and stock market data. Financial information has the great advantage of providing objective and standardized data about the current status of companies. However, financial information is typically reported on a quarterly or yearly basis and takes a certain amount of time to be disclosed after each company's financial settlement. Consequently, this study aims to demonstrate artificial intelligence-based corporate bankruptcy prediction by collecting news data that can reflect the latest information about each company and analyzing not only structured data but also unstructured data.
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