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http://dx.doi.org/10.15813/kmr.2021.22.2.002

An Integrated Model for Predicting Changes in Cryptocurrency Return Based on News Sentiment Analysis and Deep Learning  

Kim, Eunmi (Kyung Hee University Smart Tourism Research Center)
Publication Information
Knowledge Management Research / v.22, no.2, 2021 , pp. 19-32 More about this Journal
Abstract
Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.
Keywords
Cryptocurrency; Sentiment Analysis; Deep Learning; LSTM; Integrated Model;
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