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Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models - 2020

Stock Price Prediction Using Cnn And Lstm-Based Deep Learning Models

Research Area:  Machine Learning

Abstract:

Designing robust and accurate predictive models for stock price prediction has been an active area of research over a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. There exist propositions in the literature that have demonstrated that if properly designed and optimized, predictive models can very accurately and reliably predict future values of stock prices. This paper presents a suite of deep learning-based models for stock price prediction. We use the historical records of the NIFTY 50 index listed in the National Stock Exchange (NSE) of India, during the period from December 29, 2008 to July 31, 2020 for training and testing the models. Our proposition includes two regression models built on convolutional neural networks (CNNs), and three long-and-short-term memory (LSTM) network-based predictive models. For the purpose of forecasting the open values of the NIFTY 50 index records, we adopted a multi-step prediction technique with walk-forward validation. In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made. We present detailed results on the forecasting accuracies for all our proposed models. The results show that while all the models are very accurate in forecasting the NIFTY 50 open values, the univariate encoder-decoder convolutional LSTM with previous two weeks data as the input is the most accurate model. On the other hand, a univariate CNN model with previous one week-s data as the input is found to be the fastest model in terms of its execution speed.

Keywords:  

Author(s) Name:  Sidra Mehtab; Jaydip Sen

Journal name:  

Conferrence name:  International Conference on Decision Aid Sciences and Application (DASA)

Publisher name:  IEEE

DOI:  10.1109/DASA51403.2020.9317207

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9317207

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