Abstract
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.
Abstract (translated)
在金融和加密货币领域，准确预测加密货币价格趋势是最具挑战性的。利用人工智能（AI）解决这一挑战是有益的。加密货币市场以大幅增长和波动为特征，吸引了渴望研究和预测加密货币价格运动的投资者和学者。如此预测提供了大量数据，增加了这项任务的复杂性。在我们研究中，我们引入了一种名为硬和软信息融合（HSIF）的新方法，以提高加密货币价格运动预测的准确性。 HSIF方法的一个关键组成部分是硬信息部分，它包括历史价格记录和技术指标。此外，软数据部分从X（前Twitter）中提取，涵盖有关加密货币的新闻标题和推文。要使用这些数据，我们使用基于BERT的双向编码表示的情绪分析方法、金融BERT（FinBERT），这是最佳选择。最后，我们的模型依赖于包括处理过的硬和软数据的信息集。我们使用双向长短时记忆（BiLSTM）模型，因为处理信息在正向和反向方向上可以捕捉到序列信息中的长期依赖关系。 我们的实证研究结果表明，HSIF方法相对于单源数据模型的优越性通过在比特币相关数据上进行测试得到证实。通过在比特币数据集上融合硬信息和软信息，我们的模型在预测价格运动方面的准确率约为96.8%。纳入信息使我们的模型能够抓住社会情绪对价格波动的影响，从而补充基于硬信息的技术分析预测。
URL
https://arxiv.org/abs/2409.18895
