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

arXiv: https://arxiv.org/abs/2210.00883 · HTML: https://mlsanity.com/paper/2210.00883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy](https://mlsanity.com/paper/1408.1494/) (2014)
- [Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure](https://mlsanity.com/paper/1902.10991/) (2019)
- [Improved Forecasting of Cryptocurrency Price using Social Signals](https://mlsanity.com/paper/1907.00558/) (2019)
- [A bibliometric analysis of Bitcoin scientific production](https://mlsanity.com/paper/1906.08933/) (2019)

## Abstract (arXiv)

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