The Influencing factors and Prediction Methods of Bitcoin’s Price

Konferenz: CAIBDA 2022 - 2nd International Conference on Artificial Intelligence, Big Data and Algorithms
17.06.2022 - 19.06.2022 in Nanjing, China

Tagungsband: CAIBDA 2022

Seiten: 5Sprache: EnglischTyp: PDF

Autoren:
Wu, Sibo (Shandong University Weihai, Shandong, China)

Inhalt:
Bitcoin has become increasingly valuable in society, attracting investors with its fluctuating value, and many countries are now developing their own virtual currencies. In order for investors to invest with confidence and for countries to have more control over their virtual currencies, it is desirable to study Bitcoin in depth. To research Bitcoin deeply, this paper is devote to analyze what are the factors affecting the price of Bitcoin and research the approach to predicting Bitcoin’s price. First, the influce factors of Bitcoin are studied. It is found that the current factors affecting Bitcoin mainly include macro factors and Bitcoin itself. This study of factors may provide valuable information for investors to analyze the invest. Then, the classical Bitcoin prediction methods based on machine learning are reasearched. It mainly contains two kinds of methods, imprecise prediction and accurate prediction. These machine learning methods usually construct a neural network model based on the given dataset, and then use the neural network to predict the detailed price of Bitcoin. Although big data and machine learning can be used to predict the price trend of Bitcoin, it is not legal tender, and it is not guaranteed by the government. Bitcoin's large price fluctuations make it a risky investment. Therefore, it is suggested for investors to consider whether to invest in Bitcoin. Besides, there is a certain error or sudden world-class major events affect the prediction, so the prediction models can not provide certain information. For the government, if it wants to ensure the value of its virtual currency, it should control the circulation of currency, neither too much nor too little.