FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis
This work addresses the problem of short-term stock market prediction for investors, but it is incremental as it combines existing methods like FinBERT and LSTM.
The paper tackled stock price prediction by integrating news sentiment analysis with deep learning models, resulting in the FinBERT-LSTM model achieving greater accuracy, with specific metrics like mean absolute error (MAE) and mean absolute percentage error (MAPE) evaluated on NASDAQ-100 data.
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.