Cryptocurrency Return Forecasting with LASSO-VAR and Sentiment Data

Summary

The paper evaluates whether social media sentiment and attention measures can help forecast daily log returns for ten cryptocurrencies. Its predictors include Twitter and Reddit sentiment, Google Trends indexes, and trading volume. The forecasting method is a LASSO-regularized vector autoregression, evaluated with a recursive 30-day forecast over daily observations from January 2018 through January 2022. The study also compares forecasts with benchmarks and applies a post-double LASSO procedure for Granger-causality analysis in high-dimensional VARs.

The reported directional accuracy exceeds 50%, and mean directional accuracy improves by 10% against the main benchmarks. Adding sentiment and attention improves directional accuracy, but not root mean squared error. The causality analysis finds no evidence that social media sentiment Granger-causes cryptocurrency returns. These results suggest that sentiment and attention may aid directional forecasts without improving magnitude errors or establishing a causal relationship. The excerpt does not name the benchmarks or provide further details on robustness, transaction costs, or out-of-sample implementation.

Key ideas

• The study uses LASSO-VAR models to forecast returns for ten cryptocurrencies from daily data.
• Twitter and Reddit sentiment, Google Trends, and volume are included as predictors.
• Sentiment and attention improve mean directional accuracy relative to the stated benchmarks, but not root mean squared error.
• The reported Granger-causality analysis finds no causal predictive link from social media sentiment to cryptocurrency returns.
• The excerpt does not describe trading-cost adjustments or implementation results.

Tags

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


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









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

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