Fundamentals of AI in Equity Research: Building Analyst Workflows for the Future

Part 1: Understanding AI-Augmented Research Workflows

Session 1: The Modern Equity Research Process

• Walk through the end-to-end research workflow — from idea discovery and validation to modelling, valuation, and final report communication.
• Understand how AI integrates into each stage of this process: data gathering, summarisation, analysis, visualisation, and drafting.
• Learn the specific strengths of major AI tools (GPT-5, Claude, Gemini, Perplexity, Copilot) and how they complement traditional analyst work.
• Distinguish clearly between tasks best handled by automation and those that require human insight, judgement, and market context.
• Discuss real-world examples of blended human-AI workflows in professional research teams.

Session 2: Fundamentals of Prompting in Research

• Master the core principles of effective prompting — clarity, context, reasoning, and refinement.
• Apply the “Plan – Prompt – Polish” framework to translate traditional research steps into efficient AI-assisted workflows.
• Learn how to transform a typical analyst assignment (e.g., an earnings call review or thematic summary) into an AI-supported task flow.
• Identify prompt patterns, structures, and modifiers that consistently improve depth, factual precision, and style.
• Discuss common pitfalls of shallow prompting and how to guide AI models toward verifiable, professional-grade output.
• Mini-Case 1: Use AI to extract and synthesise key strategic themes from a company’s annual report, comparing results across multiple models for accuracy and tone.

Part 2: Building the AI-Enhanced Research Note

Session 3: From Idea to Insight – Workflow Design

• Deconstruct complex research tasks into modular, prompt-driven stages that mirror the real analyst workflow.
• Learn to design iterative AI workflows for company analysis, peer benchmarking, and basic valuation.
• Compare “Single-Shot” versus “Multi-Shot” prompting approaches to achieve progressively higher quality and analytical precision.
• Document the evolution of prompts to build traceability and show analytical reasoning.
• Explore how to use AI feedback loops to refine assumptions and strengthen conclusions.
• Mini-Case 2: Iterative prompting exercise – build, test, and refine a concise one-page company note using structured AI feedback.

Session 4: Structuring and Drafting the Report

• Review the standard architecture of an equity research note — Executive Summary, Investment Thesis, Financials, Valuation, and Risks.
• Practise using AI to draft concise, verifiable, and well-reasoned sections that combine data and narrative.
• Integrate qualitative insights (strategy, management tone) with quantitative metrics (margins, growth, multiples) for balanced analysis.
• Employ visual prompting for generating charts, peer comparisons, and valuation commentary that communicate findings effectively.
• Understand how iterative refinement improves flow, tone, and analytical coherence.
• Mini-Case 3: Prompt-chaining exercise – generate and refine valuation commentary, peer tables, and catalyst summaries.

Part 3: Best Practice, Verification, and Future Readiness

Session 5: Risk, Ethics, and Verification

• Recognise and manage common AI pitfalls — hallucinations, bias, and data confidentiality breaches.
• Apply cross-model validation to check accuracy and triangulate findings between GPT-5, Claude, and Gemini.
• Build verification checklists for numerical integrity, citation traceability, and transparent disclosure.
• Discuss compliance, governance, and responsible-use frameworks shaping AI deployment in investment research.

Session 6: Embedding AI in Research Teams

• Learn how to create and maintain reusable prompt templates and workflow libraries for institutional use.
• Explore strategies for scaling AI adoption while preserving analyst judgement, oversight, and accountability.
• Align AI tools with internal data systems, compliance requirements, and quality-control standards.
• Design review frameworks to measure improvements in speed, consistency, and analytical depth.
• Mini-Case 4: Audit and refine a flawed AI-generated report — identify weaknesses, verify data, and rebuild it using structured prompting best practices.

Prerequisites and Tools

Participants should have basic familiarity with Excel, PowerPoint, and financial statement interpretation.
All exercises use open-source or readily accessible tools; paid versions are optional for deeper functionality.

AI Tools

Pro Tier (Preferred for full capability and workflow integration)

• GPT-5 (ChatGPT Pro): Core tool for modelling, valuation, drafting notes, and summarising complex data.
• Gemini 1.5 Pro: Quick fact-finding, macro trend analysis, and Google Sheets integration.
• Perplexity Pro: Real-time search, consensus validation, and alt-data triangulation.
• Notion AI: Structured prompt management, note iteration, and workflow documentation.

Free Tier (Suitable for foundational use)

• ChatGPT Free / Claude Instant / Gemini Basic: Light summarisation, drafting, and brainstorming.
• Claude 4: Deep analysis of filings, transcripts, and risk disclosures (accessible under free usage limits).
• Microsoft Copilot: Excel automation, comp-table generation, and slide creation within Office 365 environments.
• Perplexity Free: Rapid fact-checking and public-data cross-verification.
• Google Sheets or Excel Online: For simple ratio and trend analysis.

Financial / Data Platforms

(Used for input workbooks and sourced financial information to be worked on via case studies and demonstrations; participants will have some access via materials provided during the session.)

• TIKR Pro: Company fundamentals, estimates, and peer benchmarking.
• Koyfin Pro: Charting, screening, macro dashboards, and time-series analysis.
• Seeking Alpha Pro: Earnings transcripts, management commentary, sentiment and crowd analysis.

As this is a Mastering the Foundations course, we will use the above tools to iterate, prompt, and build the key workstreams that support idea generation, ongoing idea monitoring, and core equity research processes. However, since we are not using an integrated or out-of-the-box AI software suite, the course will not cover fully automated financial model builds. The focus is on mastering the workflows, prompting techniques, and analytical reasoning that underpin high-quality professional research using AI assistance.

This AI training course is delivered by a highly accomplished professional with a track record of exceptional performance in various sell-side and buy-side roles. He began his career at Citigroup's Industrials team in London, where he gained extensive experience in M&A and capital markets activities. Throughout his time there, he contributed to numerous pitches and transactions, specialising in diversified industrial sectors such as automotive, aerospace & defence, and metals & mining.

Driven by his passion for US biotech investments, he joined Rothschild & Co. in a senior position, where he provides strategic financial advice to clients in the healthcare industry. His deep understanding of the sector enabled him to navigate complex challenges and identify lucrative opportunities.

To broaden his investing experience, hejoined Redline Capital Partners: focusing on active portfolio management and investment analysis with an emphasis on long/short public equities.

In 2019, he established Third Wave Capital, a proprietary trading firm focused on US healthcare and technology investments. He has also been actively engaged in Indian public equities and commodities for more than a decade, working closely with his family’s investment initiatives.

He holds a Bachelor of Science (Hons) degree in Accounting & Finance from the University of Warwick, strengthening his financial knowledge and capabilities. With his wealth of experience and deep industry passion, he is highly regarded for his analytical skills, strategic thinking, and commitment to delivering exceptional results.

In addition to conducting Redcliffe’s AI training courses, he offers training and consulting services to academic institutions and financial organisations, receiving acclaim for his ability to provide valuable insights to delegates with diverse levels of experience.

• Produce an AI-augmented equity research note, combining both qualitative and quantitative analysis of a chosen company while following professional research standards.
• Use AI tools to accelerate data gathering, summarisation, and drafting while maintaining data integrity, transparency, and analytical rigour.
• Deliver well-structured sections covering company overview, industry context, strategy, financials, valuation, risks, ESG considerations, catalysts, and conclusion.
• Design efficient prompting workflows that reflect how analysts work in practice, from sourcing and analysis through drafting and review.
• Develop hands-on skill in multi-step prompting and report iteration, learning how to improve quality through context, structure, and feedback loops.
• Understand the ethical and regulatory aspects of using AI in research, including disclosure, bias control, and data privacy.
• Build and present a final AI-powered research report applying all techniques learned to analyse a listed company and generate clear investment insights.

• Accessible and Practitioner-Focused – Designed for professionals new to AI in research, emphasising core concepts, prompting fundamentals, and practical workflows rather than coding or data-science theory.
• Flexible Delivery Format – Delivered live online over two half-days, combining guided instruction with interactive exercises and case-based discussion.
• Built Entirely on Free & Open-Source Tools – All exercises use readily available AI platforms and data sources, ensuring participants can continue practising independently without costly licences.
• Structured Yet Practical Learning – Introduces AI techniques such as summarisation, natural-language analysis, and assisted valuation in a clear, step-by-step way that mirrors a real analyst’s workflow.
• AI for Better Decision-Making – Demonstrates how AI can enhance screening, peer comparison, and insight generation while preserving the analyst’s judgment and accountability.
• Responsible and Compliant Use of AI – Highlights key ethical, bias, and regulatory considerations to ensure outputs remain transparent, verifiable, and aligned with professional standards.
• Hands-On Learning Throughout – Participants complete short, guided exercises that apply AI tools to genuine research tasks.

• Financial Professionals: Investment bankers, analysts, and portfolio managers aiming to deepen their expertise in asset management.
• Asset Management Executives: Senior leaders in asset management, hedge funds, and private equity seeking strategic and market insights.
• Investment Advisors: Consultants advising on investment strategies and portfolio management.
• Corporate Finance Executives: CFOs and finance directors interested in advanced investment strategies and market effects.
• Graduate Students and Academics: Those in finance or economics fields looking to enhance their practical and theoretical knowledge in asset management.

This AI in Equity Research course teaches finance professionals how to use AI and machine learning in equity research and portfolio management. It covers data extraction, sentiment analysis, stock screening, risk assessment, and portfolio optimization using AI. The course includes hands-on exercises with open-source AI tools and discusses ethical considerations and future trends in AI-driven finance.