论文标题
Finbert-LSTM：使用新闻情绪分析的基于深度学习的股票价格预测
FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis
论文作者
论文摘要
经济严重取决于股票市场。上升趋势通常与繁荣相对应，而下降趋势与衰退相关。因此，很长一段时间以来，预测股票市场一直是研究和实验的中心。能够预测市场中的短期变动使投资者能够从投资中获得更大的回报。股票价格极为波动，对金融市场敏感。在本文中，我们使用深度学习网络来预测股票价格，吸收有关市场信息的财务，商业和技术新闻文章。首先，我们创建一个简单的多层感知器（MLP）网络，然后扩展到更复杂的复发性神经网络（RNN），例如长期短期内存（LSTM），最后提出了Finbert-LSTM模型，该模型通过分析短期市场信息将股票的观点集成到更准确的股票价格，以预测股票价格。然后，我们使用平均绝对误差（MAE），平均绝对百分比误差（MAPE）和准确度量来评估MLP，LSTM，Finbert-LSTM模型的性能，以评估MLP，LSTM，Finbert-LSTM模型的性能，以评估MLP，LSTM，Finbert-LSTM模型的性能。
Economy is severely dependent on the stock market. An uptrend usually corresponds to prosperity while a downtrend correlates to recession. Predicting the stock market has thus been a centre of research and experiment for a long time. Being able to predict short term movements in the market enables investors to reap greater returns on their investments. Stock prices are extremely volatile and sensitive to financial market. In this paper we use Deep Learning networks to predict stock prices, assimilating financial, business and technology news articles which present information about the market. First, we create a simple Multilayer Perceptron (MLP) network and then expand into more complex Recurrent Neural Network (RNN) like Long Short Term Memory (LSTM), and finally propose FinBERT-LSTM model, which integrates news article sentiments to predict stock price with greater accuracy by analysing short-term market information. We then train the model on NASDAQ-100 index stock data and New York Times news articles to evaluate the performance of MLP, LSTM, FinBERT-LSTM models using mean absolute error (MAE), mean absolute percentage error (MAPE) and accuracy metrics.
