Stock Market Price Prediction: A Hybrid LSTM and Sequential
  Self-Attention based Approach
Karan Pardeshi Sukhpal Singh Gill Ahmed M. Abdelmoniem
One of the most enticing research areas is the stock market, and projecting
stock prices may help investors profit by making the best decisions at the
correct time. Deep learning strategies have emerged as a critical technique in
the field of the financial market. The stock market is impacted due to two
aspects, one is the geo-political, social and global events on the bases of
which the price trends could be affected. Meanwhile, the second aspect purely
focuses on historical price trends and seasonality, allowing us to forecast
stock prices. In this paper, our aim is to focus on the second aspect and build
a model that predicts future prices with minimal errors. In order to provide
better prediction results of stock price, we propose a new model named Long
Short-Term Memory (LSTM) with Sequential Self-Attention Mechanism (LSTM-SSAM).
Finally, we conduct extensive experiments on the three stock datasets: SBIN,
HDFCBANK, and BANKBARODA. The experimental results prove the effectiveness and
feasibility of the proposed model compared to existing models. The experimental
findings demonstrate that the root-mean-squared error (RMSE), and R-square (R2)
evaluation indicators are giving the best results.