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

arXiv: https://arxiv.org/abs/1912.07700 · HTML: https://mlsanity.com/paper/1912.07700/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 23 of 23)

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- [Design and Analysis of Robust Deep Learning Models for Stock Price Prediction](https://mlsanity.com/paper/2106.09664/) (2021)
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- [Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models](https://mlsanity.com/paper/2111.01137/) (2021)
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- [Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks](https://mlsanity.com/paper/2202.02728/) (2022)
- [Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model](https://mlsanity.com/paper/2203.01326/) (2022)
- [Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market](https://mlsanity.com/paper/2211.07080/) (2022)
- [Analysis of Sectoral Profitability of the Indian Stock Market Using an LSTM Regression Model](https://mlsanity.com/paper/2111.04976/) (2021)
- [A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks](https://mlsanity.com/paper/2210.00984/) (2022)
- [Optimum Risk Portfolio and Eigen Portfolio: A Comparative Analysis Using Selected Stocks from the Indian Stock Market](https://mlsanity.com/paper/2107.11371/) (2021)
- [Machine Learning in Finance-Emerging Trends and Challenges](https://mlsanity.com/paper/2110.11999/) (2021)
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- [SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest](https://mlsanity.com/paper/2410.07143/) (2024)
- [ESAN: Efficient Sentiment Analysis Network of A-Shares Research Reports for Stock Price Prediction](https://mlsanity.com/paper/2112.11444/) (2021)
- [Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition](https://mlsanity.com/paper/2204.01827/) (2022)
- [A Comparative Analysis of Portfolio Optimization Using Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning Approaches on the Indian Stock Market](https://mlsanity.com/paper/2305.17523/) (2023)
- [Portfolio Optimization: A Comparative Study](https://mlsanity.com/paper/2307.05048/) (2023)
- [Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks](https://mlsanity.com/paper/2309.13696/) (2023)
- [Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning](https://mlsanity.com/paper/2407.01572/) (2024)

## Abstract (arXiv)

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 of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 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 - 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 and found extremely interesting results.