Cryptocurrency News Website with Prediction

Abstract

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain- based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods. Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is.