Using Textual Information in Annual Reports for Earnings Prediction
Date Issued
2009
Date
2009
Author(s)
Shih, Hui-Yu
Abstract
Earnings prediction has always been a major interest to both the academia and the practitioners. Numerous studies have attempted to develop models for better prediction. The majority of these models incorporate only quantitative accounting data contained in or derived from financial statements. However, since accounting data reflects only past performance, the prediction power of these models is not very satisfactory. In contrast, textual information in annual reports contains lots of future-oriented information. This type of information could be more useful for earnings prediction. This study attempts to investigate whether textual information in annual reports is useful for earnings prediction. We build a prediction model that incorporates the positive/negative sentiment as conveyed by the management through the textual information contained in annual reports. We posit that the model can have more prediction power by including such information. We also posit that prediction error of the accounting-based model will be large if the sentiment is more ambiguous, because the future is more uncertain. By comparing the prediction error of our model against those of random walk model, accounting-based model, and analysts’ forecasts, we find that our model is significantly better than the random walk model and the accounting-based model. Even though our model is inferior to analysts’ forecasts, the difference is not significant. We also find that the accounting-based model has less prediction power when the sentiment is more ambiguous.
Subjects
earnings prediction
fundamental analysis
accounting narratives
content analysis
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