Prediction of Ethereum Short-term Returns Using XGBoost Model

Abstract

Unlike traditional currencies that rely on centralized such as banks or governments, cryptocurrencies have become popular due to its decentralized transactions. Decentralization takes advantage of no requirement for intermediaries, thus reducing transaction fees and processing times. However, investing in cryptocurrencies incurs risks and uncertainties due to price volatility and rapid changes. The fact that prediction of asset prices is complex due to the influence of multiple factors on price movements. This paper studied the technical factor to analyse the short-term returns of Ethereum (ETH) in the periods of 1-10 days. The historical data containing ETH closing price are collected from CoinGecko. The twenty-two indicators are chosen from Momentum, Volatility, and Sentiment factors as candidates to provide valuable insights in market trends. By calculating various indicators based on past closing prices, this study utilizes XGBoost, a powerful boosted decision trees ensemble, to discover patterns in previous trading. The model performance is evaluated using the multi-class AUC-ROC metric, which measures the accuracy of predicting three types of ETH returns: Downtrend, Sideway, and Uptrend. The results show that the models achieve accuracy scores ranging from 0.65 to 0.67. Moreover, the study emphasizes the importance of considering momentum indicators when making investment decisions in Ethereum. Keywords—cryptocurrency investment, technical factor, Ethereum, XGBoost, machine learning