Data for: 3514038

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.