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Download fileStock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
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. Researchers have also worked on technical analysis
of stocks with a goal of identifying patterns in the stock price movements
using advanced data mining techniques. 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 from 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. Using the grid-searching
technique, the hyperparameters of the LSTM models are optimized so that it is
ensured that validation losses stabilize with the increasing number of epochs,
and the convergence of the validation accuracy is achieved. 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 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's open value of the NIFTY 50 time series is the
most accurate model.
History
Email Address of Submitting Author
jaydip.sen@acm.orgORCID of Submitting Author
https://orcid.org/0000-0002-4120-8700.Submitting Author's Institution
Praxis Business SchoolSubmitting Author's Country
- India