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클라우드 네이티브 방식의 분산 엣지 클라우드를 위한 연합학습
구자빈(Jabin Koo),김종원(JongWon Kim) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
본 논문은 클라우드 네이티브 방식의 분산 엣지 클라우드에서의 전통적인 중앙화된 기계학습 방법의 현실적인 한계들을 제시하고 해당 한계들을 극복할 수 있는 방법인 연합학습의 모의구현을 위해 클라우드 네이티브 방식의 분산 엣지 클라우드를 가정한 연합학습 클러스터 환경을 구축하고 해당 클러스터 환경 위에서 연합학습의 실행을 보인다.
Bankruptcy Risk and Income Smoothing Tendency of NBFIs in Bangladesh
Shahima JABIN,Shohana Islam SUMONA 한국유통과학회 2021 Asian Journal of Business Environment (AJBE) Vol.11 No.2
Purpose: The study mainly investigates bankruptcy risk and income smoothing tendency of Non-Banking Financial Institutions (NBFIs) in Bangladesh. External parties of NBFIs take investment decisions based on financial reports. Stable and predictable income is one of their preference. On the other hand, poor income is one of the signs of NBFIs having bankruptcy risk. Hence the study tries to find whether the NBFIs having bankruptcy are involved in income smoothing or not. Research design, data and methodology: Data were collected from the annual report of twenty-two listed NBFIs in Bangladesh. Data from 2013 to 2017 were used. Altman’s Z score and Eckel’s model are used to detecting bankruptcy risk and income smoothing respectively. Results: Result implies that most of the NBFIs which have bankruptcy risk are not involved in income smoothing. Therefore, NBFIs which has bankruptcy risk are involved less with income smoothing. Conclusions: The present study revealed that most of the listed NBFIs in Bangladesh are facing bankruptcy risk. They didn’t use any fraudulent technique to show smooth income. The findings will help the investor to take an investment decision on NBFIs in Bangladesh. It will convey signals to the stock market in Bangladesh