Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
- Title
- Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
- Creator
- Jain, Yogesh Kumar; Kumar Mannepalli, Praveen; Kumar, Santosh; Malhotra, Amit; Choudhary, Deepak Kumar; Saharan, Mohit
- Description
- The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE.
- Source
- 2025 International Conference on Next Generation of Green Information and Emerging Technologies, GIET 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Machine Learning; price prediction; Stock Market Prediction; Yahoo Finance data
- Coverage
- Jain Y.K., School of Management, IILM University, Greater Noida, India; Kumar Mannepalli P., Chandigarh University, Department of Computer Science and Engineering, Mohali, India; Kumar S., ERA University, Computer Science, Uttar Pradesh, Lucknow, India; Malhotra A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Ghaziabad, India; Choudhary D.K., Muzaffarpur Institute of Technology, Department of Computer Science and Engineering, Bihar, Muzaffarpur, India; Saharan M., Vidya University, Meerut, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 978-166545806-1;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jain, Yogesh Kumar; Kumar Mannepalli, Praveen; Kumar, Santosh; Malhotra, Amit; Choudhary, Deepak Kumar; Saharan, Mohit, “Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25845.