Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques
- Title
- Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques
- Creator
- Suresh K.; Sarkar A.; Rai A.; Vasishtha A.; Fernandes T.; Cecil Donald A.
- Description
- This paper presents a comprehensive study on stock price predictions by integrating market factors and sentiment analysis of news headlines. The research is divided into two modules, each employing distinct methodologies to enhance the accuracy of stock price forecasts. In the first module, market factors are investigated using three advanced algorithms: Long Short-Term Memory (LSTM), Gradient Boosting Decision Trees (GBDT), and Facebook Prophet (FBPROPHET). These algorithms are evaluated based on metric scores such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The analysis focuses on predicting high and low values of market prices for the period from January to June 2021. The comparative assessment of these algorithms provides insights into their effectiveness in capturing market trends and making precise predictions. In the second module, the paper explores the impact of news headlines on stock prices by extracting sentiment using three distinct algorithms: lexical-based analysis, Naive Bayes, and FinBERT. The sentiment analysis aims to gauge the market sentiment reflected in news articles and assess its influence on stock price movements. Prediction accuracy is calculated for each algorithm, highlighting their strengths in capturing sentiment patterns. 2024 IEEE.
- Source
- 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, pp. 1837-1841.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- FBPROPHET; Financial markets; FinBERT; GBDT; Lexical- Based; LSTM; MAE; MSE; Naive Bayes; RMSE; Stock price predictions
- Coverage
- Suresh K., Christ University, Department of Computer Science, Bangalore, 560029, India; Sarkar A., Christ University, Department of Computer Science, Bangalore, 560029, India; Rai A., Christ University, Department of Computer Science, Bangalore, 560029, India; Vasishtha A., Christ University, Department of Computer Science, Bangalore, 560029, India; Fernandes T., Christ University, Department of Computer Science, Bangalore, 560029, India; Cecil Donald A., Christ University, Department of Computer Science, Bangalore, 560029, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835037642-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Suresh K.; Sarkar A.; Rai A.; Vasishtha A.; Fernandes T.; Cecil Donald A., “Analyzing Market Factors for Stock Price Prediction using Deep Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19089.