Long‐and‐Short‐Term Memory (LSTM) NetworksArchitectures and Applications in Stock Price Prediction

Jaydip Sen · Sidra Mehtab

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Stock Market Forecasting Methods Neural Networks and Applications Hydrological Forecasting Using AI

Abstract

Although recurrent neural networks (RNNs) are effective in handling sequential data, they are poor in capturing the long-term dependencies in the data due to a problem known as vanishing and exploding gradients. A variant of RNNs known as Long-and-Short-Term Memory (LSTM) networks effectively gets rid of the problem, and hence these networks are proved to be very efficient and accurate in handling sequential data. This chapter presents the basic design of LSTM networks and highlights their working principles. Six different variants of LSTM models are also presented with a particular focus on stock price forecasting. The models are trained and tested on the historical NIFTY 50 index of the National Stock Exchange (NSE) of India from December 29, 2014 to July 31, 2020. The performances of the models are compared on the basis of their execution speeds and prediction accuracies. It is observed that while the univariate LSTMs with the basic architecture are more accurate than their encoder-decoder counterparts, the opposite is the case for the execution speed.

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References

2011Twitter mood predicts the stock market 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2008Robust performance hypothesis testing with the Sharpe ratio 2017Stock market one-day ahead movement prediction using disparate data sources 2021Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models 2020Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models 2020A time series analysis-based stock price prediction using machine learning and deep learning models 2019Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach

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