Authors : Kalyani Sanap; Neha Rathod; Sakshi Alone; Achal Battulwar; Vrukshada Ukinkar; Dr. Manisha More

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/msknfuak

DOI : https://doi.org/10.38124/ijisrt/26mar500

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Financial markets' volatility, driven by economic, political, and psychological factors, complicates stock price forecasting and investment decisions. This paper proposes a real-time stock trend analysis and investment support system using a hybrid deep learning ensemble of GRU, LSTM, and Transformer models with attention mechanisms for accurate price predictions from live API data (e.g., Yahoo Finance). It integrates K-means clustering for risk-based asset categorization (stocks, mutual funds, gold, bonds), decision trees for personalized buy/hold/sell recommendations, and portfolio optimization via MPT or reinforcement learning. An interactive dashboard visualizes trends, predictions, and insights, empowering users with data-driven, diversified strategies to enhance returns and manage risks.

Keywords : Deep Learning, Stock Forecasting, GRU-LSTM-Transformer, Real-Time Analysis, Investment Recommendations, KMeans Clustering, Portfolio Optimization, Financial Dashboard.

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