Cryptocurrency Price Prediction using Bi-GRU Model with English and Malay News Sentiment Features

Abstract

The effect of sentiment from news sources on cryptocurrency prices has garnered significant interest from the financial community including researchers. However, current findings reporting the usefulness of sentiment features in cryptocurrency price prediction is still mixed. This paper aims to explore various feature combinations encompassing cryptocurrency historical prices and sentiment scores of the current day aggregated from English and Malay news sources to predict the closing price of the next day. Each news headline related to Bitcoin and Ethereum within the duration of one year is first manually annotated and then aggregated on a daily basis to generate the sentiment features. A Bi-GRU deep learning model is implemented to predict the cryptocurrency closing price of the next day given a combination of historical price and sentiment features of the current day. Our findings evidently show sentiment features when combined with the closing price of the current day contribute significantly to improve the model's performance. Our study is also the first attempt to examine the effect of Malay news sentiment on cryptocurrency prices and we have demonstrated that cryptocurrency price prediction models leveraging Malay news sentiment features for Bitcoin and Ethereum are able to yield performance that is at par with models using English news sentiment features.