论文标题
预测加密货币记录回收：套索和情感方法
Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach
论文作者
论文摘要
加密货币最近已成为一个时尚的话题，这主要是由于它们的破坏性潜力和前所未有的回报报道。此外，学者越来越多地承认许多领域的社交媒体的预测能力，更具体地说是金融市场和经济学。在本文中，我们利用Twitter和Reddit情感的预测能力以及Google趋势索引和音量来预测十种加密货币的日志返回。具体来说，我们考虑$比特币$，$以太坊$，$ tether $，$ binance币$，$ litecoin $，$ enjin coin $，$ hivelen $，$ namecoin $，$ peercoin $和$ feathercoin $。我们使用2018年1月至2022年1月的每日数据评估了拉索 - 瓦尔的性能。在30天的递归预测中，我们可以在50％以上的时间内检索实际系列的正确方向。我们将该结果与主要基准测试进行了比较，并且平均方向准确性（MDA）提高了10％。用作预测因素的情感和注意变量的使用显着提高了MDA的预测准确性，但在根平方误差方面却没有明显提高。我们使用对高维量的双重套索选择进行Granger因果关系测试。结果表明，从社交媒体情绪到加密货币回报中没有“因果关系”
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
