Time series accruals apply in financial distress problem with dimensionality reduction: taking US-listed company for example

• Main Authors: CHENG YU LU, 呂澄宇
• Other Authors: Deron Liang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/862a46

碩士 === 國立中央大學 === 資訊工程學系 === 106 === The financial distressed prediction problem(FDPP) has been discussed for a long time and extensively. The main purpose of this thesis is to focus on US listed companies data to extend FDPP research direction. Most of previous scholars and researcher used financial ratio(FR) to do the prediction. This thesis is hopes to find out new feature besides financial ratio which can improve the performance of FDP result. And we know difference data type will also affect the prediction result. In the past, some scholars had used accruals as feature to do prediction, but its accruals are not comprehensive, or the research question is not focus on FPD but Earning management, and also the data type are year data. Therefore, this thesis focuses on use comprehensive accruals and using time series quarter data to do the research. After all we will dimension reduction to reduce dimensions to improve feature performance and perform feature weight analysis.