AI Stock Insight: An Intelligent Platform for Real-Time Stock and IPO Prediction Using Machine Learning and Sentiment Analysis

Abstract

The rapid growth of Artificial Intelligence (AI) and data analytics has brought a major transformation to the financial world, helping investors and traders make better, data-driven decisions. AI Stock Insight is an intelligent, GUI-based platform that predicts daily stock prices and upcoming IPO trends using a combination of machine learning algorithms, technical indicators, and real-time news sentiment analysis. The system gathers live financial data through APIs such as Yahoo Finance and Twelve Data, which provide up-to-date stock and market information. For price forecasting, Linear Regression and Random Forest Regression models are used, offering reliable short-term predictions. At the same time, TextBlob-based sentiment analysis reads live financial news and headlines to understand the public’s mood and investor confidence, helping to predict whether a company or IPO might perform positively or negatively in the market. The system also analyzes technical indicators such as RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Bollinger Bands to study stock momentum and market stability. For IPO forecasting, the system evaluates companies like OYO Rooms, Mobikwik, Pharmeasy, and Ola Electric etc.. identifying listing price trends and potential investor demand based on both technical and sentiment-based data. All analysis and predictions are displayed in an interactive GUI dashboard that shows clear graphs, comparison charts, and trend summaries. Users can explore multiple stocks, view 5-day forecasts, check IPO trends, and download visual reports in just a few clicks.By combining AI models, live financial data, and sentiment analytics, AI Stock Insight delivers a smart and user-friendly solution for real-world financial forecasting.

Introduction

The stock market is highly unpredictable, influenced by factors such as company performance, news, economic conditions, and investor sentiment. To simplify market analysis for everyday users, the project AI Stock Insight uses machine learning and artificial intelligence to predict stock prices and IPO performance. The system analyzes historical stock data, technical indicators (like RSI, MACD, and Bollinger Bands), and real-time news sentiment to forecast trends. Users can view predicted prices, market trends, and interactive graphs through a simple GUI, with options to download reports.

The platform also provides IPO trend analysis, estimating performance based on early trading data, public interest, and news sentiment. By combining price forecasting, sentiment analysis, and live visualization, AI Stock Insight helps non-experts make informed investment decisions while learning how AI applies to finance. The system uses models like Random Forest Regression, integrates APIs for live data, and presents results through intuitive dashboards, making complex financial insights accessible and actionable.

Conclusion

In this study, we use a mix of old stock data, technical indicators and news sentiment to predict stock prices and first-day IPO price. By combining numbers (OHLCV) with news mood, the model try to capture not only price trends but also market feeling, which is really importent in real world. The Random Forest Regressor (RFR) works good here cause it can handle complicated relations between features. Using Monte Carlo style simulation also help to make prediction more real, so they dont look too smooth or fake. Plus adding sentiment-adjusted price help the model consider news impact which maybe not directly in numbers. The IPO prediction model is simple, but it uses sentiment and trading volume to guess first-day price. This can help investors to understand how IPO might do on first day. Some things we find: 1) Using sentiment improve accuracy when combined with technical indicators. 2) Simulating future features let model predict short term prices even if we dont have real future data. 3) Hybrid approach is flexible and can use for other markets or financial stuff too. In short, framework is practical, understandable, and gives good predictions for both stock prices and IPOs.

References

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Copyright

Copyright © 2025 Nishant Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.