Title
Forecasting Net Income Estimate And Stock Price Using Text Mining From Economic Reports
Abstract
This paper proposes and analyzes a methodology of forecasting movements of the analysts' net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied our method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we performed two additional experiments. First, we employed our proposed method for forecasting the movements of analysts' net income estimates by inputting the opinion and non-opinion sentences into separate neural networks. Besides the reports, we inputted the trend of the net income estimate to the networks. Second, we employed our proposed method for forecasting the movements of stock prices. Consequently, we found differences between security firms, which depend on whether analysts' net income estimates tend to be forecasted by opinions or facts in the context of analyst reports. Furthermore, the trend of the net income estimate was found to be effective for the forecast as well as an analyst report. However, in experiments of forecasting movements of stock prices, the difference between opinion sentences and non-opinion sentences was not effective.
Year
DOI
Venue
2020
10.3390/info11060292
INFORMATION
Keywords
DocType
Volume
text mining, analyst report, forecasting net income estimate, forecasting stock price
Journal
11
Issue
Citations 
PageRank 
6
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Masahiro Suzuki100.34
Hiroki Sakaji23017.97
Kiyoshi Izumi312737.12
Hiroyasu Matsushima464.72
Yasushi Ishikawa500.34