Net Income Forecast from Analyst Reports by Text Mining

Abstract

In this paper, we propose a methodology of forecasting the change rate of net income which an analyst estimates by applying natural language processing and neural networks in the context of analyst reports. We examine the contents of the reports for useful information on forecasting the direction of revision in analyst estimate earnings. First, our method extracts opinion sentences from the reports while the remaining parts are classified as non-opinion sentences.Second, our method forecasts movements of analyst estimate earnings by inputting the opinion and non-opinion sentences into separate neural networks. In addition to the reports, we input the trend of the net income to the networks. As a result, we found that there were differences between securities firms depending on whether analysts' net income estimates were based on opinions or facts. We also found that the trend of the net income was effective for forecast as well as an analyst report.