0 Part Course  | 
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Artificial Intelligence (AI) in Equity Research

0 Part Course  |  Learn about fundamental principles of AI-driven equity research and portfolio construction

A one-day course presented over two-half days in a virtual class from 9:30am to 1:00pm UK time

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pdf Download:   Course Category Brochure  |   Course Outline

PART ONE

Module 1: Foundations of AI in Equity Research
  • Understanding AI's Role in Equity Research
    • AI vs. traditional equity research methods
    • Key AI concepts: Machine Learning (ML), Natural Language Processing (NLP), Deep Learning
    • How AI is reshaping the role of equity analysts
  • Types of AI Used in Financial Analysis
    • Open-source NLP tools (e.g., spaCy, NLTK) for text analysis
    • Predictive analytics with free ML libraries (e.g., Scikit-learn)
    • Alternative data sources: Free financial data APIs, web scraping
Module 2: AI for Data Gathering & Processing
  • Extracting & Structuring Financial Data
    • Free data sources: SEC filings, financial APIs (Yahoo Finance, Alpha Vantage)
    • AI-powered financial data extraction (Pandas, OpenAI Whisper for earnings calls)
    • Automating real-time market news analysis
  • Sentiment Analysis & NLP for Market Insights
    • AI-driven sentiment scoring using free Python libraries
    • Case study: Using NLP to analyze earnings calls, news sentiment
  • Alternative Data Analysis with AI
    • AI-driven insights from social media, web traffic (Google Trends)
    • Satellite imagery and credit card transaction analysis (public datasets)
    • Open-source AI tools for alternative data (Hugging Face Transformers)
PART TWO

Module 3: AI-Driven Stock Screening & Investment Strategies
  • Machine Learning for Stock Screening
    • Factor investing strategies with AI
    • Open-source ML models for growth vs. value investing
    • Stock price anomaly detection
  • AI in ESG Investing
    • AI-powered ESG scoring using open-source sentiment models
    • Assessing corporate sustainability through AI-driven news analysis
  • Hands-on Activity: Building a simple stock screening model with Python
Module 4: AI for Financial Modelling & Valuation
  • AI-Driven Financial Forecasting
    • Predicting earnings growth using open-source AI
    • AI-enhanced Discounted Cash Flow (DCF) models
    • Time-series forecasting for financial trends
  • AI-Powered Company Valuation
    • Using AI for Monte Carlo simulations (NumPy, TensorFlow)
    • Case Study: Applying AI to predict key financial metrics
  • Hands-on Activity: Creating an AI-assisted DCF model using free AI tools

Module 5: AI for Risk Assessment & Portfolio Optimization

  • AI in Risk Analysis
    • Machine learning for downside risk detection
    • Open-source AI models for credit risk assessment
    • Stress testing portfolios with AI
  • AI-Driven Portfolio Management
    • AI-enhanced Modern Portfolio Theory (MPT)
    • Factor exposure analysis using AI
    • AI-based dynamic rebalancing strategies
  • Hands-on Activity: Building a simple AI-based portfolio optimization model

Module 6: Future Trends, Ethical Considerations & AI Integration

  • Challenges & Limitations of AI in Equity Research
    • Model biases and data quality issues
    • AI regulation in financial markets
    • Transparency in AI-driven investment decisions
  • Ethics & AI Governance in Finance
    • Avoiding bias in AI-driven stock recommendations
    • Ethical considerations in AI model development
  • The Future of AI in Equity Research
    • AI’s impact on passive vs. active investing
    • Quantum computing and AI in finance
    • Next-generation AI models for equity research
  • Final Case Study & Q&A:
    • Developing an AI-driven equity research report using open-source tools
    • Open discussion on AI integration in traditional 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.

  • Master AI-Driven Equity Research – Understand how AI enhances stock analysis, investment decision-making, and financial modelling
  • Leverage Open-Source AI Tools – Use Python-based machine learning and natural language processing (NLP) for market insights and sentiment analysis
  • Extract & Analyse Financial Data – Automate data gathering from earnings reports, news, and alternative data sources using AI
  • Apply AI to Stock Screening & Valuation – Implement AI models to identify undervalued stocks, predict earnings, and enhance valuation techniques
  • Optimize Portfolios with AI – Use AI to assess risk, optimize asset allocation, and develop dynamic investment strategies
  • Navigate Ethical & Regulatory Challenges – Understand AI biases, compliance risks, and responsible AI use in financial markets
  • 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.

£ 895.00
per part

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