AI-Driven Sentiment Synthesis: Leveraging Advanced RAG Frameworks for Alpha Generation in Equity Trading

Author(s)	Mr. Animesh Sarkar
Country	India
Abstract	In high-frequency and fundamental equity markets, the ability to decode "soft" management signals during corporate conference calls is a critical source of alpha. Traditional sentiment analysis often misses the linguistic nuance between cautious optimism and structural concern. This paper proposes a state-of-the-art Retrieval-Augmented Generation (RAG) framework that integrates Hybrid Retrieval, Semantic Chunking, and a Human-in-the-Loop (HITL) verification layer. By utilizing GenAI APIs constrained by zero-hallucination guardrails, the system converts unstructured transcript data into actionable trading signals. The research demonstrates how this architecture evolves investment banking from manual data parsing to a paradigm of "Augmented Intelligence," providing a measurable competitive edge in buy-side and sell-side positioning.
Keywords	Generative AI, Retrieval-Augmented Generation (RAG), Equity Trading, Sentiment Momentum Index, Human-in-the-Loop, Alpha Generation.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-11

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