  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.
