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Abstract:
The SEC requires that all publicly traded companies submit financial reports in a standardized structure using XBRL. This provides a new database to examine the usefulness of accounting information as a basis for a profitable investment strategy. The objective of this study is to replicate previous models in attempting to predict the direction of movement of earnings, by using XBRL data. The study does not attempt to examine the validity of the models, only the ability to use XBRL filings in financial statement analysis. The study analyzes NYSE companies XBRL quarterly data, from 2011 to 2015, using a two-step Logit regression model. The results classified companies as ones that would realize an increase or a decrease in earnings. The final model indicated a significant ability to predict subsequent earnings changes. The predictions appear to be correct on average about 70.7% of the time (higher than those of previous studies). An attempt to create a profitable investment strategy, was successful and provided high abnormal returns. These results suggest that there is merit using XBRL accounting information as a means for forecasting movements in earnings, and creating a profitable investment strategy.
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