Paper Title
Nivesh AI: A Multi-Agent, LLM-Augmented Investment Decision Support System for Indian Retail Stock Markets
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Registration ID: IJNRD_324116
Published ID: IJNRD2604677
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Keywords
multi-agent system, stock market analysis, LLM sentiment, ARIMA, ensemble forecasting, Indian equity markets, retail investor, technical analysis, fundamental analysis, Gemini, LangChain
Abstract
Retail participation in Indian equity markets has surged post-pandemic, yet most individual investors lack access to institutional-grade analytical tools. This paper presents Nivesh AI, a full-stack, real-time, multi-agent investment decision support system built specifically for the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). The system orchestrates four specialised agents—a Technical Analysis Agent, a Fundamental Analysis Agent, a Price Prediction Agent, and a Live News Sentiment Agent—coordinated by a Master Orchestrator that fuses their verdicts into a single Nivesh AI Consensus signal. Price forecasting employs an ensemble of Linear Regression (weight 0.55) and ARIMA(5,1,2) (weight 0.45), further adjusted by a real-time sentiment score derived from both rule-based keyword analysis and Google Gemini 2.5 Flash LLM inference on live news headlines. The system attains a 30-day forecasting error as low as 2.84% MAPE on out-of-sample trials across a diverse set of NSE-listed equities. A prediction override tracker records every forecast outcome and marks it correct when directional accuracy is achieved within a 3% error envelope. The platform is deployed as a Streamlit web application backed by MongoDB Atlas and features WhatsApp OTP authentication, a Razorpay wallet, broker deep-links, and an AI Q&A chatbot. Nivesh AI demonstrates that multi-agent orchestration coupled with LLM-powered sentiment can meaningfully outperform single-model baselines for retail-oriented stock decision support.
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How To Cite (APA)
Divyansh Jindal, Om Sharma, Rishabh Sharma, & Naman Sharma (April-2026). Nivesh AI: A Multi-Agent, LLM-Augmented Investment Decision Support System for Indian Retail Stock Markets. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), g643-g647. https://ijnrd.org/papers/IJNRD2604677.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : g643-g647
Other Publication Details
Paper Reg. ID: IJNRD_324116
Published Paper Id: IJNRD2604677
Research Area: Other area not in list
Author Type: Indian Author
Country: Ghaziabad, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604677.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604677
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