Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment
Approach
- URL: http://arxiv.org/abs/2210.00883v1
- Date: Thu, 22 Sep 2022 14:26:32 GMT
- Title: Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment
Approach
- Authors: Federico D'Amario, Milos Ciganovic
- Abstract summary: 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 $Feathercoin$.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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
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