Abhinav Singh,
- Research Scholar, Department of MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
Abstract
The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization tools to guide investment decisions. Incorporating live data streams along with sentimental insights from news outlets and social media significantly boosts its ability to make accurate predictions. This study delves into the design, technological framework, and educational impact of the platform, highlighting its potential to transform traditional investment strategies and empower both retail and institutional investors. By connecting cutting-edge AI technologies with the world of finance, the platform takes a significant step toward making advanced investment tools more accessible to a broader audience.
Keywords: Stock market prediction, AI in finance, machine learning, financial analytics, investment strategies, LSTM networks, sentiment analysis, portfolio optimization, real-time data, predictive modeling
[This article belongs to E-Commerce for Future & Trends ]
Abhinav Singh. Designing an AI-Based Platform for Stock Market Prediction. E-Commerce for Future & Trends. 2025; 12(03):14-19.
Abhinav Singh. Designing an AI-Based Platform for Stock Market Prediction. E-Commerce for Future & Trends. 2025; 12(03):14-19. Available from: https://journals.stmjournals.com/ecft/article=2025/view=232586
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E-Commerce for Future & Trends
| Volume | 12 |
| Issue | 03 |
| Received | 05/03/2025 |
| Accepted | 24/04/2025 |
| Published | 01/11/2025 |
| Publication Time | 241 Days |
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