A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

Proceedings of the 2019 International Conference on Business Analytics and Intelligence (ICBAI 2019), December 2019, Bangalore, INDIA.

6 Pages Posted: 15 Sep 2020

See all articles by Sidra Mehtab

Sidra Mehtab

NSHM Knowledge Campus - School of Computing and Analytics

Jaydip Sen

Praxis Business School

Multiple version iconThere are 2 versions of this paper

Date Written: December 1, 2019

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.

Keywords: Stock Price Prediction, Classification, Regression, LSTM, Sentiment Analysis, Granger Causality, Cross-validation, Self-Organizing Fuzzy Neural Networks

JEL Classification: C22; C32; C38; C45; C51; C52; C53; C58

Suggested Citation

Mehtab, Sidra and Sen, Jaydip, A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing (December 1, 2019). Proceedings of the 2019 International Conference on Business Analytics and Intelligence (ICBAI 2019), December 2019, Bangalore, INDIA. , Available at SSRN: https://ssrn.com/abstract=3665352

Sidra Mehtab (Contact Author)

NSHM Knowledge Campus - School of Computing and Analytics ( email )

India

Jaydip Sen

Praxis Business School ( email )

Bakrahat Road, P.O Rasapunja,
Off Thakurpukur Raod
Kolkata, West Bengal 700104
India
+919836189765 (Phone)

HOME PAGE: http://praxis.academia.edu/JaydipSen

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