Artificial Intelligence (AI) in Equity Research

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 equity research note of roughly ten pages covering qualitative and quantitative analysis of a chosen company.
• Use AI tools to speed up data gathering, summarisation and drafting while maintaining data integrity and rigorous due diligence.
• Deliver clear sections on company overview, industry context, strategy, financials, valuation, risks, ESG considerations, catalysts and conclusion.
• Build an AI-Powered Equity Research Report – Apply everything learned in a final case study, using AI to analyse stocks and generate investment insights

• Flexible in terms of delivery
• Based on Free & Open-Source AI Tools – No reliance on proprietary or paid software; all implementations use publicly available AI libraries and data sources
• Comprehensive Yet Accessible – Covers machine learning, natural language processing (NLP), financial forecasting, and AI-driven stock selection in a structured and digestible manner
• AI-Enhanced Investment Decision Making – Learn how AI models improve factor investing, portfolio optimization, and risk assessment for modern investors
• Ethical & Regulatory Awareness – Explore the risks, biases, and governance frameworks essential for responsible AI deployment in finance
• Practical examples utilising AI

• 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.