论文标题
除了交易数据：公众意识和兴趣对加密货币波动的隐藏影响
Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility
论文作者
论文摘要
自从比特币于2009年首次出现在现场以来，加密货币已成为全球现象，作为重要的分散金融资产。然而，它们的分散性质导致对传统法定货币的显着波动，使得准确预测加密货币汇率复杂的任务。这项研究检查了影响比特币汇率波动率的各种独立因素。为此，我们提出了一个多模式的Adaboost-LSTM合奏模型，该模型不仅利用了历史交易数据，而且还结合了相关推文中的公众情感，搜索量和区块链哈希结果数据所表现出的公众兴趣。我们开发的模型通过预测整体加密货币价值分布的波动，从而提高了其投资决策价值。我们已经通过全面的实验对这种方法进行了广泛的测试，从而验证了多模式组合对独家对交易数据的依赖的重要性。进一步的实验表明，我们的方法显着超过了现有的预测工具和方法，证明了19.29％的改善。该结果强调了外部独立因素对加密货币波动的影响。
Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
