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http://dx.doi.org/10.15207/JKCS.2020.11.8.165

A Comparative Analysis of the Prediction Models for the Direction of Stock Price Using the Online Company Reviews  

Lim, Yongtaek (Department of Bigdata Convergence, Korea University)
Lim, Heuiseok (Department of Computer Science and Engineering, Korea University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.8, 2020 , pp. 165-171 More about this Journal
Abstract
Most of the stock price prediction research using text mining uses news and SNS data. However, there is a weakness that it is difficult to get honest and vivid information about companies from them. This paper deals with the problem of the prediction for the direction of stock price by doing text mining the online company reviews of internal staff indicating employee satisfaction. The comparative analysis of the prediction models for the direction of stock price showed the prediction model, which adds internal employee reviews, has better performance than those that did not. This paper presents the convergence study using natural language processing in financial engineering. In the field of stock price prediction, This paper pursued a new methodology that used employee satisfaction. In practice, it is expected to provide useful information in the field of forecasting stock price direction.
Keywords
Stock prediction; Employee satisfaction; Company reviews; Textmining; Machine learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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