Abstract
Prediction of future movement of stock prices has been a subject matter
of many research work. On one hand, we have proponents of the Efficient
Market Hypothesis who claim that stock prices cannot be predicted, on
the other hand, there are propositions illustrating that, if
appropriately modeled, stock prices can be predicted with a high level
of accuracy. There is also a gamut of literature on technical analysis
of stock prices where the objective is to identify patterns in stock
price movements and profit from it. In this work, we propose a hybrid
approach for stock price prediction using five deep learning-based
regression models. We select the NIFTY 50 index values of the National
Stock Exchange (NSE) of India, over a period of December 29, 2014 to
July 31, 2020. Based on the NIFTY data during December 29, 2014 to
December 28, 2018, we build two regression models using
convolutional neural networks (CNNs), and three regression models
using long-and-short-term memory (LSTM) networks for predicting
the open values of the NIFTY 50 index records for the period
December 31, 2018 to July 31, 2020. We adopted a multi-step prediction
technique with walk-forward validation. The parameters of the
five deep learning models are optimized using the grid-search technique
so that the validation losses of the models stabilize with an increasing
number of epochs in the model training, and the training and validation
accuracies converge. Extensive results are presented on various metrics
for all the proposed regression models. The results indicate that while
both CNN and LSTM-based regression models are very accurate in
forecasting the NIFTY 50 open values, the CNN model that previous
one week’s data as the input is the fastest in its execution. On the
other hand, the encoder-decoder convolutional LSTM model uses the
previous two weeks’ data as the input is found to be the most accurate
in its forecasting results.