One of the most important challenges in the financial and cryptocurrency field is accurately predicting cryptocurrency price trends. Leveraging artificial intelligence (AI) is beneficial in addressing this challenge. Cryptocurrency markets, marked by substantial growth and volatility, attract investors and scholars keen on deciphering and forecasting cryptocurrency price movements. The vast and diverse array of data available for such predictions increases the complexity of the task. In our study, we introduce a novel approach termed hard and soft information fusion (HSIF) to enhance the accuracy of cryptocurrency price movement forecasts. The hard information component of our approach encompasses historical price records alongside technical indicators. Complementing this, the soft data component extracts from X (formerly Twitter), encompassing news headlines and tweets about the cryptocurrency. To use this data, we use the Bidirectional Encoder Representations from Transformers (BERT)-based sentiment analysis method, financial BERT (FinBERT), which performs best. Finally, our model feeds on the information set including processed hard and soft data. We employ the bidirectional long short-term memory (BiLSTM) model because processing information in both forward and backward directions can capture long-term dependencies in sequential information. Our empirical findings emphasize the superiority of the HSIF approach over models dependent on single-source data by testing on Bitcoin-related data. By fusing hard and soft information on Bitcoin dataset, our model has about 96.8\% accuracy in predicting price movement. Incorporating information enables our model to grasp the influence of social sentiment on price fluctuations, thereby supplementing the technical analysis-based predictions derived from hard information.

翻译：金融与加密货币领域最重要的挑战之一在于精准预测加密货币价格趋势。利用人工智能技术有助于应对这一挑战。加密货币市场以显著的增长与波动性为特征，吸引了众多致力于解读和预测加密货币价格走势的投资者与学者。可用于此类预测的数据规模庞大且类型多样，增加了任务的复杂性。本研究提出一种称为硬软信息融合的新方法，以提升加密货币价格走势预测的准确性。该方法中的硬信息部分包含历史价格记录与技术指标。作为补充，软数据部分从X平台提取，涵盖关于加密货币的新闻标题与推文。为利用此类数据，我们采用基于Transformer双向编码器表征的BERT情感分析方法——金融BERT，该方法表现最优。最终，我们的模型以包含处理后的硬软数据的信息集作为输入。我们采用双向长短期记忆模型，因其通过前向与后向处理信息的能力可捕捉序列信息中的长期依赖关系。基于比特币相关数据的测试表明，我们的实证结果凸显了HSIF方法相对于依赖单源数据模型的优越性。通过在比特币数据集上融合硬软信息，我们的模型在价格走势预测中达到约96.8%的准确率。信息融合使模型能够把握社会情绪对价格波动的影响，从而对基于硬信息的技术分析预测形成补充。