Stock Price Prediction using Bidirectional LSTM with Attention
- 24 May 2022
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2022 1st International Conference on AI in Cybersecurity (ICAIC)
Abstract
The stock market is one of the most important topics of today’s economy due to its fluctuating nature and far-reaching impact. Despite the difficulty of mathematical modeling of time-series of stock prices, deep learning and black-box modeling have been reported to perform well in literature. In this work, we have experimented with different recurrent neural network architectures to predict stock prices for elite Nasdaq companies. We have improved the baseline models by reducing the number of parameters and using bidirectional processing with attention. We show that using a Gaussian noise vector can regularize the model to improve the robustness without using any dropout. Our proposed model achieves the lowest mean square error for AMZN (0.12), AAPL (0.15), NFLX (0.03), GOOG (0.08), GOOGL (0.06), CSCO (0.01), COST (0.06), FB (0.03), 8 out of 10 companies. Our work aims to show that with better architecture design, RNN variants can outperform baseline models despite having a fraction of the parameters.Keywords
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