  Prediction of stock prices has been an important area of research for a long
time. While supporters of the efficient market hypothesis believe that it is
impossible to predict stock prices accurately, there are formal propositions
demonstrating that accurate modeling and designing of appropriate variables may
lead to models using which stock prices and stock price movement patterns can
be very accurately predicted. In this work, we propose an approach of hybrid
modeling for stock price prediction building different machine learning and
deep learning-based models. For the purpose of our study, we have used NIFTY 50
index values of the National Stock Exchange (NSE) of India, during the period
December 29, 2014 till July 31, 2020. We have built eight regression models
using the training data that consisted of NIFTY 50 index records during
December 29, 2014 till December 28, 2018. Using these regression models, we
predicted the open values of NIFTY 50 for the period December 31, 2018 till
July 31, 2020. We, then, augment the predictive power of our forecasting
framework by building four deep learning-based regression models using long-and
short-term memory (LSTM) networks with a novel approach of walk-forward
validation. We exploit the power of LSTM regression models in forecasting the
future NIFTY 50 open values using four different models that differ in their
architecture and in the structure of their input data. Extensive results are
presented on various metrics for the all the regression models. The results
clearly indicate that the LSTM-based univariate model that uses one-week prior
data as input for predicting the next week open value of the NIFTY 50 time
series is the most accurate model.
