Forecasting the Impact Of The News On Cryptocurrency via Machine Learning Algorithms

In this study, it was investigated which of the machine learning algorithms gave the most optimal results in determining the effect of news about cryptocurrencies on the prediction of Bitcoin (BTC) price. In this direction, three different data sets were created, and these data were tested separately in each machine learning algorithm examined. In this way, it is aimed to determine the effect of the data set on the success of machine learning algorithms. By doing web scraping with the script, we developed to determine the news data we used in the study, 684 news made by BBC, CNBC, Fox, Guardian, NY Post, Over News, Sun, USA Today, and VOA news sources were found and by sentiment analysis of them, 564 news related to cryptocurrencies were taken into account in the study. March 2020 and March 2021 were chosen as the dates of the selected news. (The period when the BTC price rose from 9Ks to 30Ks.). The reason for choosing a past time period is to precisely measure the consistency of the predictions made. While creating the data sets used in machine learning algorithms, high, low, volume, open, and close values in the dollar, Ethereum, and gold values were also taken into account, as well as news sources. In this way, it is aimed to make more realistic estimations by taking market data into account. Machine learning methods in the study; Decision tree, deep learning, random forest, support vector machine, gradient boosted trees, and a generalized linear model was used. The generalized linear model (model II), which gives the most successful result with a rate of 98.73%, is explained in detail in the study.