A Robust Predictive Model for Stock Price Prediction Using Deep Learning
and Natural Language Processing
Abstract
Prediction of future movement of stock prices has been a subject matter
of many research work. There is a gamut of literature of technical
analysis of stock prices where the objective is to identify patterns in
stock price movements and derive profit from it. Improving the
prediction accuracy remains the single most challenge in this area of
research. We propose a hybrid approach for stock price movement
prediction using machine learning, deep learning, and natural language
processing. We select the NIFTY 50 index values of the National Stock
Exchange (NSE) of India, and collect its daily price movement over a
period of three years (2015 – 2017). Based on the data of 2015 – 2017,
we build various predictive models using machine learning, and then use
those models to predict the closing value of NIFTY 50 for the period
January 2018 till June 2019 with a prediction horizon of one week. For
predicting the price movement patterns, we use a number of
classification techniques, while for predicting the actual closing price
of the stock, various regression models have been used. We also build a
Long and Short-Term Memory (LSTM)-based deep learning network for
predicting the closing price of the stocks and compare the prediction
accuracies of the machine learning models with the LSTM model. We
further augment the predictive model by integrating a sentiment analysis
module on Twitter data to correlate the public sentiment of stock prices
with the market sentiment. This has been done using Twitter sentiment
and previous week closing values to predict stock price movement for the
next week. We tested our proposed scheme using a cross validation method
based on Self Organizing Fuzzy Neural Networks (SOFNN) and found
extremely interesting results.