Fundamental Analysis and the Prediction of Earnings

  •  Dyna Seng    
  •  Jason R. Hancock    


This paper takes fundamental analysis research beyond the spatial and temporal bounds of previous studies. We
investigate how detailed financial statement data enter the decisions of market makers by examining how current
changes in the fundamental signals chosen can provide information on subsequent earnings changes. Using
global data from 1990 to 2000, we extend the body of research using fundamental signals for prediction of future
earnings changes. Contextual factors that may influence this predictive ability are also investigated. Results
indicate that the fundamental signals are significant predictors of both short- and long-term future earnings
changes. Contextual factors that include prior earnings news, industry membership, macroeconomic conditions
and country of incorporation are all demonstrated to influence this relationship. Research results provide
evidence to support the use of fundamental analysis.

This work is licensed under a Creative Commons Attribution 4.0 License.