Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach

TL;DR

This paper explores the use of LASSO-VAR models combined with social media sentiment, Google Trends, and volume data to forecast cryptocurrency log-returns, achieving over 50% accuracy in predicting return directions.

Contribution

It introduces a novel forecasting approach integrating social media sentiment and attention variables with LASSO-VAR for cryptocurrencies, demonstrating improved directional accuracy.

Findings

LASSO-VAR achieves over 50% correct direction prediction.

Sentiment and attention variables improve forecast accuracy in terms of MDA.

No causality found from social media sentiment to cryptocurrency returns.

Abstract

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider $Bitcoin$, $Ethereum$, $Tether$, $Binance Coin$, $Litecoin$, $Enjin Coin$, $Horizen$, $Namecoin$, $Peercoin$, and $Feathercoin$. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no "causality" from Social Media sentiment to cryptocurrencies returns