Incorporating financial news for forecasting Bitcoin prices based on long short-term memory networks
研究如何用长短期记忆网络从金融新闻中提取信息，结合机器学习模型预测比特币价格，发现加入新闻后预测效果显著优于纯模型，且交易策略回报率高于买入持有。
In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.