| Intermediate | CPD: 14 hours | 4 half-days | Live |
Description
Transform the way you analyse markets with the power of AI. This intensive, hands-on course shows finance professionals how AI is reshaping trading, research, and portfolio management – and how to apply these capabilities immediately.
Using real market data, practical case studies, and ready-to-use AI tools, participants leave with skills they can deploy the very next day. Gain a competitive edge and turn AI from a buzzword into a bottom-line advantage.
Learning Outcomes
By attending this two-day course, you will:
- Understand how AI is reshaping financial markets to deliver real commercial value.
- Identify meaningful opportunities to use AI in trading, investment research, portfolio management, and risk oversight.
- Interpret AI-generated insights to support faster, more informed financial decisions.
- Evaluate AI-driven strategies using clear performance and risk metrics.
- Apply practical AI tools to analyse markets, evaluate ideas, and enhance investment processes.
Who Should Attend
Anyone who wants to understand and apply AI in financial markets in a practical, results-driven way. This includes investment analysts, asset managers, traders, and those working in execution and risk management.
Prerequisites
You should have a basic understanding of financial markets, asset classes, market terminology, and common investment strategies. The ability to run pre-written Python code in a Jupyter notebook is useful but not essential.
Book Now!Seminar Content
Introduction and Foundations
- What is AI?
- Machine learning, deep learning, generative AI, and LLMs
- AI agents and automated financial workflows
- Why AI matters in financial markets today
- Opportunities and limitations of AI in financial decision-making
- Regulatory and compliance context
- search Case Study: AI applications in investment management
Data in Financial Markets
- Market data: prices, volumes, order books, fundamentals, and news
- Alternative data: consumer spending, sentiment, macroeconomic indicators, geolocation, and other real-world data
- Using generative AI to explore and analyse large financial datasets
- Preparing and combining datasets for AI applications
- Data quality and common modelling pitfalls: survivorship bias, look-ahead bias, leakage, and overfitting
- computer Workshop: Importing and exploring market and alternative data
AI Techniques Used in Market Applications
- Predictive modelling: forecasting returns, volatility, and market trends
- Classification models for trading signals and investment decisions
- AI-assisted portfolio construction and optimisation
- Unsupervised learning: identifying market regimes and anomalies
- Natural language processing (NLP) and sentiment analysis of financial news
- Generative AI and LLMs for financial analysis
- AI agents for market monitoring and investment research
- computer Workshop: Using AI to turn financial news into trading signals
Building an AI-Powered Market Model
- The AI workflow: data, model selection, training, testing, and deployment
- Evaluating predictive accuracy and financial usefulness
- Explainability and interpreting model outputs
- Avoiding overfitting and ensuring robust out-of-sample performance
- computer Workshop: Running and evaluating an AI market model
Practical Session 1 – Using AI Models
- Loading and exploring real market datasets
- Running ready-built AI models for market prediction and analysis
- Interpreting forecasts, probabilities, and model outputs
- computer Workshop: Adjusting model parameters and comparing results
Practical Session 2 – Generating Trading and Investment Insights
- Translating AI predictions into trading, investment, and portfolio allocation decisions
- Constructing and backtesting AI-driven strategies
- Evaluating returns, Sharpe ratios, drawdowns, and transaction costs
- Comparing AI strategies against conventional approaches
- computer Workshop: Generating signals, testing strategies, and evaluating performance
AI for Risk Management
- AI applications in volatility forecasting and risk assessment
- Identifying market anomalies and changing market regimes
- AI-assisted scenario analysis and portfolio stress testing
- Using AI insights to support portfolio risk decisions
- computer Workshop: Evaluating portfolio exposures under different market scenarios
AI in Investment Research and Decision-Making
- Using LLMs to analyse financial reports, earnings announcements, and economic data
- Retrieval-Augmented Generation (RAG) – combining AI with trusted financial information
- AI agents for automating financial research and analysis
- Prompting techniques for effective financial analysis
- Validating AI-generated research and recommendations
- computer Workshop: Using AI to analyse financial documents, conduct research, and prepare an investment briefing
Responsible and Safe Use of AI in Finance
- AI governance, accountability, and regulatory expectations
- Hallucinations, unreliable outputs, and model limitations
- Data confidentiality, security, and intellectual property
- Model monitoring, auditability, and human oversight
- Risks associated with autonomous AI agents
Capstone Exercise
- Select a financial objective and build on the models and datasets used earlier
- Apply an AI model or analytical tool
- Generate and evaluate a trading, investment, or risk-management recommendation
- Present findings, including financial implications, limitations, and risks
Wrap-Up and Next Steps
- Key lessons and practical applications
- Emerging developments in AI for financial markets
- Integrating AI into everyday financial workflows
- Opportunities for further development
When and Where
30 Nov 2026 - 3 Dec 2026
a.m. sessions – 08:00 to 12:00 (for Asia / EMEA participants)
p.m. sessions – 13:00 to 17:00 (for EMEA / Americas participants)
All times are GMT/BST (London time)
Virtual
Data Science
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Other Dates and Locations
Search for AI in Finance in our course schedule for alternative dates and locations where this course is offered.