The contextual usefulness of financial statement analysis in predicting earnings growth and measuring market earnings expectations

• Main Author: Lee, Bong-Hack
• Language: ENG
• Published: ScholarWorks@UMass Amherst 1996
• Subjects: Accounting|Finance
• Online Access: https://scholarworks.umass.edu/dissertations/AAI9638988

In the past two decades considerable research in finance and accounting has focused on the forecasting of accounting earnings. Financial practitioners and academics alike have long been interested in predicting corporate earnings. Exploration of the topic has provided a handsome livelihood to professionals in the investment community and has established many a scholarly reputation. This study develops an earnings prediction model that uses non-earnings accounting information in publicly available annual financial statements to predict future earnings changes. Although the relevance of non-earnings financial statement information to future earnings has been established, only Stober (1992) has examined whether non-earnings financial statement information explains additional aspects of the variability of future earnings beyond those predicted by competing sources of earnings expectations, e.g. analysts' earnings forecasts. In this study, we evaluate our earnings forecast, called a financial statement analysis forecast, relative to financial analysts' and share price-based forecasts both in terms of the accuracy of their prediction of earnings growth, and in terms of the contemporaneous association between unexpected earnings, measured using the alternative forecast sources, and security returns during the forecast year. Further this study examines the complementarity of the financial statement analysis forecast to financial analysts' forecasts in various contexts, i.e. prior performance, earnings uncertainty and firm size in predicting earnings and in measuring market earnings expectations. Finally, this study evaluates whether a financial statement analysis forecast is useful in developing a composite model for predicting earnings. The empirical results indicate that financial statement analysis forecasts complement financial analysts' and price-based forecasts in earnings predictions and security valuation. Further analysis on the potential complementarity of financial statement analysis forecasts to financial analysts' forecasts shows that the unique variation in financial statement analysis forecasts is relevant to earnings prediction and market associations in the various contexts mentioned above. Finally, this study provides evidence that indicates current developed earnings forecasts can be utilized in developing an earnings composite model that performs better than any single forecast source.