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StockSense: AI-Powered Prediction with Real-Time News Intelligence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 94 |
| Year of Publication: 2026 |
| Authors: Aashish Bagmar, Mangal Wagh, Jitendra Musale, Eshwari Bhandkar, Simrah Awati, Aarya Jadhav |
10.5120/ijca2026926631
|
Aashish Bagmar, Mangal Wagh, Jitendra Musale, Eshwari Bhandkar, Simrah Awati, Aarya Jadhav . StockSense: AI-Powered Prediction with Real-Time News Intelligence. International Journal of Computer Applications. 187, 94 ( Mar 2026), 42-47. DOI=10.5120/ijca2026926631
Abstract
Stock market forecasting is a difficult task because of the volatility and unpredictable nature of financial data. To address this is-sue, we present an AI-based Stock Prediction Model that predicts future stock prices by analyzing historical data, trading volume, and price movements. Our model captures temporal dependence and finds connections within financial time series using machine learning techniques. Additionally, a sentiment analysis module processes financial news and classifies public sentiment as positive, negative, or neutral, which provides context to the market. Our model is designed as a web application using Python and Flask, making it user-friendly and allowing for analysis and visualization of continuously updated data. Our experimental results show that the model connects past market behavior to future trends, enabling better-informed decisions. In summary, our work emphasizes the value of AI-based predictive analytics in finance for making informed and timely investment choices.
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