Research Article
Cryptocurrency Price Tendency Analysis Using Conventional Statistical Model and Machine Learning Approach
@INPROCEEDINGS{10.4108/eai.28-10-2022.2328451, author={Chenyang Liao and Kai Lu and Jiamiao Zhang}, title={Cryptocurrency Price Tendency Analysis Using Conventional Statistical Model and Machine Learning Approach}, proceedings={Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China}, publisher={EAI}, proceedings_a={FFIT}, year={2023}, month={4}, keywords={machine learning; cryptocurrency; prediction model}, doi={10.4108/eai.28-10-2022.2328451} }- Chenyang Liao
Kai Lu
Jiamiao Zhang
Year: 2023
Cryptocurrency Price Tendency Analysis Using Conventional Statistical Model and Machine Learning Approach
FFIT
EAI
DOI: 10.4108/eai.28-10-2022.2328451
Abstract
The market for cryptocurrency has thrived for more than 10 years and has experienced a drastic change. The success of cryptocurrencies was concerned and analyzed worldwide. This research discusses the way to build machine learning and statistical models to predict the future price of the cryptocurrency based on the current price. This article collects and analyzes closed prices of Bitcoins, Ethereum, BNB coin, Avalanche, and Solana from as early as 2019 to the most recent data. Based on prediction models, anticipating the future price through the current market shows a moderate available result with a high related fitness within the regression model. Through market prices, it is possible to predict the future trend of a cryptocurrency. This research further discusses possible features of the cryptocurrency market and looks for the correlation between cryptocurrencies. According to the analysis, models show a strong relationship between Bitcoins and other cryptocurrency types in long-run market performance. Besides, we also argue that it is possible to trace price changes and predict the market based on the present price. These results shed light on guiding further exploration of stimulating market through externality.