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http://dx.doi.org/10.14372/IEMEK.2022.17.1.51

An Accurate Stock Price Forecasting with Ensemble Learning Based on Sentiment of News  

Kim, Ha-Eun (Daegu University)
Park, Young-Wook (Daegu University)
Yoo, Si-eun (Daegu University)
Jeong, Seong-Woo (Daegu University)
Yoo, Joonhyuk (Daegu University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.1, 2022 , pp. 51-58 More about this Journal
Abstract
Various studies have been conducted from the past to the present because stock price forecasts provide stability in the national economy and huge profits to investors. Recently, there have been many studies that suggest stock price prediction models using various input data such as macroeconomic indicators and emotional analysis. However, since each study was conducted individually, it is difficult to objectively compare each method, and studies on their impact on stock price prediction are still insufficient. In this paper, the effect of input data currently mainly used on the stock price is evaluated through the predicted value of the deep learning model and the error rate of the actual stock price. In addition, unlike most papers in emotional analysis, emotional analysis using the news body was conducted, and a method of supplementing the results of each emotional analysis is proposed through three emotional analysis models. Through experiments predicting Microsoft's revised closing price, the results of emotional analysis were found to be the most important factor in stock price prediction. Especially, when all of input data is used, error rate of ensembled sentiment analysis model is reduced by 58% compared to the baseline.
Keywords
Stock Price Prediction; Sentiment analysis; Macro economic indicator;
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