Grey group model forecasting of quarterly accounting earnings

  • Authors:

  • Zheng-Lin Chen;Chan-Chien Chiu;Chia-Jui Lai

  • Affiliations:

  • Department of Accounting, Zhongnan University of Economics and Law, Wuhan City, Hubei, China;Department of Business Administration, Dahan Institute of Technology, Hualien, Taiwan;Department of Finance and Banking, Dahan Institute of Technology, Hualien, Taiwan

  • Venue:
  • WSEAS Transactions on Information Science and Applications
  • Year:
  • 2009

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Abstract

Forecasting quarterly accounting earnings is an important task in prospective analysis and seasonality is an important phenomenon in those data's behavior. In this paper, we investigate the efficiency of applying grey group model to forecast the earning per share. Unlike traditional statistical model such as Foster Model, grey group model inheres not only easy to calculate but also few observations needed than statistics methods when model building. Furthermore, the model liberates the users from the sample data assumptions from statistics analysis such as identically independent distribution. A data sample with fifty firms trading on Taiwan Stock Exchange is employed here and the forecasting performances are compared with those obtained by Foster Model. The results demonstrate that the grey group model is a competitive and competent one in prospective analysis.