Stock Market Prediction

Abstract

Stock market investments are inherently risky and volatile, making accurate prediction of stock prices a valuable tool for investors. This research proposes an AI-based stock market prediction web application that forecasts future stock prices and provides actionable insights such as Buy/Sell/Hold signals, trend analysis, risk assessment, and portfolio simulation. The system integrates deep learning models like LSTM, GRU, or Transformer networks for predictive analytics and includes interactive dashboards with charts and real-time notifications. Additionally, the platform offers multi-stock comparison, sentiment analysis, and downloadable reports for decision support. The proposed system aims to assist both novice and experienced investors in making informed decisions and understanding market trends effectively.

Introduction

The text discusses the development of an AI-based stock market prediction web application designed to help investors make better financial decisions. Stock prices are highly dynamic and influenced by many factors such as company performance, economic conditions, investor sentiment, and global events. Traditional statistical models often fail to capture the non-linear and temporal patterns present in financial time series. Therefore, artificial intelligence and deep learning techniques are used to improve prediction accuracy.

The proposed system integrates deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformers to analyze historical stock data and forecast future prices. The web application provides features including historical and predicted price charts, Buy/Sell/Hold signals, risk and trend analysis, portfolio simulation, alerts for significant market changes, and optional news sentiment analysis. These tools help investors visualize market trends and plan investment strategies.

The system architecture consists of a frontend interface built using web technologies (HTML, CSS, JavaScript, React/Streamlit/Dash) and a Python-based backend using frameworks such as Flask, FastAPI, or Django. The backend processes data collected from financial APIs, performs data preprocessing and technical indicator generation, and applies AI models for prediction. Key modules include data collection, preprocessing, prediction, visualization, evaluation, and advanced analysis features such as multi-stock comparison and risk assessment.

The workflow begins when a user selects a stock and timeframe. The system retrieves historical market data, processes it, generates technical indicators, and uses AI models to forecast prices and generate trading signals. Results are displayed through interactive dashboards with charts, alerts, and portfolio simulation tools.

Although the system improves prediction and decision-making, it has some limitations, such as dependency on data quality, potential inaccuracies during unexpected market events, and high computational requirements for real-time multi-stock predictions.

Overall, the application demonstrates that combining AI-based predictive analytics, visualization, and portfolio simulation can provide a powerful, user-friendly platform that supports investors, researchers, and students in understanding stock market trends and making informed investment decisions.

Conclusion

The Stock Market Prediction Web Application integrates machine learning with an interactive, user-friendly web interface to forecast stock prices and generate actionable insights. By combining LSTM/GRU/Transformer models, portfolio simulation, trend and risk analysis, and visualization modules, the system assists investors in making informed decisions and understanding market behavior. The platform demonstrates the potential of AI to enhance investment planning and academic research in financial markets.

References

[1] A. Sendi, F. Hoque, and M. T. Hoque, “Decoding Stock Trends: A Comparative Study of GRU, LSTM, and Transformer Models in Tech Sector Prediction,” Trans. Eng. Comput. Sci., vol. 13, no. 03, pp. 46–60, May 2025. [2] “Deep Learning Approaches for Stock Price Prediction: A Comparative Study of LSTM, RNN, and GRU Models,” IEEE Conf. Publ., 2025. [3] “RNN, GRU, and LSTM Model Analysis for AI?Powered Indian Stock Market Index Forecasting,” IEEE Conf. Publ., 2025. [4] “Stock Prediction Under GRU, LSTM and GNN,” IEEE Conf. Publ., 2025. [5] “Prediction of Stock Prices Using a Combination of LSTM and GRU Algorithms on LQ45 Indexed Stocks,” IEEE Conf. Publ., 2025. [6] V. N. Patel and S. Tandel, “A Comparative Analysis of LSTM and GRU Models Enhanced with Technical Indicators for Stock Forecasting,” ResearchGate, 2025. [7] G. Rizky Naufal and A. Wibowo, “Time Series Forecasting Based on Deep Learning CNN?LSTM?GRU Model on Stock Prices,” Int. J. Eng. Trends Technol., vol. 71, no. 6, pp. 126?133, 2023. [8] Y. Touzani and K. Douzi, “An LSTM and GRU Based Trading Strategy Adapted to the Moroccan Market,” J. Big Data, vol. 8, art. 126, Sept. 2021. [9] T. B. Shahi, A. Shrestha, A. Neupane, and W. Guo, “Stock Price Forecasting with Deep Learning: A Comparative Study,” Mathematics, vol. 8, no. 9, art. 1441, Aug. 2020..

Copyright

Copyright © 2026 Kartik Sunil Kute, Aryan Vilas Khankar, Pranay Dnyandev Davkhar, Siddarth Balasaheb Kunjir, Prof. Nawale S. K.. 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.