Exploring Cryptocurrency Influence factors Using Feature Selection Algorithm

Abstract

Cryptocurrency prices have changed very dynamically in the market. Buyers and sellers can trade cryptocurrency on the market without time limits compared to traditional trading markets such as exchange currency markets and stock markets. Also, since it is a cryptocurrency created by an anonymous inventor, cryptocurrency price predictions are not determined by the company’s financial performance. Rather, the cryptocurrency price is related to how many investors participate in the market. In this sense, the prediction of cryptocurrency prices is very difficult and related to market participants. In this study, to better understand cryptocurrency pricing factors, we explore cryptocurrency price forecasts and use deep learning to improve forecasts. Specifically, we collected variables related to the investor’s decision. Use linear regression to select features to find important variables in cryptocurrency price prediction. In regression analysis, three models were created to identify the model that represents the best performance of cryptocurrency price prediction. Model 1 uses all variables without function selection. Model 2 uses only variables that are important in feature selection. Model 3 uses only variables that are not important for feature selection. Our test results show that Model 2 outperforms other models. We conclude that using the appropriate variables can improve cryptocurrency price predictions.