The Application of Grey Prediction Models to Depreciation Expense Prediction

• Main Authors: Hsin-Yin Lin, 林欣穎
• Other Authors: Wen-Hui Chen
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/d6u94c

碩士 === 國立臺北科技大學 === 自動化科技研究所 === 103 === Earnings per share (EPS) serves an important indicator for investors to analyze listed companies. Business owners with good reputation and accountability also consider it is important to achieve their annual EPS growth rate. Higher EPS means the company is high profitability per of unit capital, the company has better ability than the other competitors. That means it can use fewer resources to create higher profit. Among the factors affecting a listed company&;#39;s earnings per share, where the cost of the item is depreciation. Depreciation is the cost that cannot be ignored. There are many ways to calculate depreciation. The most principal is according fixed assets application to choose the different depreciation methods. This research will use a listed company for an example. To use 2013 - 2015 first quarter of the company&;#39;s capital expenditure of used data for the analysis sample, and use the calculated used data index value of each fixed assets to the relevance data of transfer property. Use grey prediction theory‘s GM (1.1) model to analyze and forecast the accumulated depreciation of the company that in year at every month, to replace the original calculation of the monthly performed by manual work mode to increase operational efficiency.