Applied AI in Investment Finance
الحصول على الكتيب
للحصول على كتيب بكامل محتويات الدورة برجاء ملء النموذج التالي ببياناتك الصحيحة
Program Professional Framework
Applied AI in Investment Finance
Program Overview
Asset managers, sovereign wealth funds and bank treasury teams are applying AI across the investment process, from research and signal generation to portfolio construction, risk monitoring and client reporting. This hands-on programme covers practical techniques: natural language processing of earnings calls and news, machine learning models for return and risk prediction, alternative data, factor analysis, portfolio optimisation and generative AI for research synthesis. Participants learn rigorous back-testing, how to avoid overfitting and look-ahead bias, and how to govern models under regulatory and fiduciary obligations. Examples include Gulf equity and fixed income markets and the large institutional investors that operate in the region.
Learning Outcomes
- ✓Apply natural language processing to extract sentiment signals from financial news or earnings transcripts.
- ✓Build and evaluate a machine learning model to predict returns or risk using structured market data.
- ✓Design a back-test that controls for overfitting, look-ahead bias and transaction costs.
- ✓Construct a portfolio using optimisation techniques and compare it with a benchmark allocation.
- ✓Use generative AI tools to summarise research while verifying sources and accuracy.
- ✓Define model governance, monitoring and documentation for AI used in investment decisions.
Target Audience
- Portfolio managers and investment analysts
- Quantitative analysts and data scientists in asset management
- Treasury and investment officers in banks and sovereign funds
- Investment risk and performance professionals
Detailed Program Modules
01AI Across the Investment Process
- Research, signals, portfolio and risk applications
- Machine learning versus traditional quantitative methods
- Data, tools and computing environments
02Data and Feature Engineering
- Market, fundamental and alternative data
- Data cleaning and survivorship bias
- Feature construction and factor exposures
03Machine Learning for Return and Risk Prediction
- Supervised learning models for finance
- Cross-validation for time series
- Interpreting model drivers
04Natural Language Processing and Generative AI
- Sentiment analysis of news and earnings calls
- Document summarisation and research assistants
- Verifying and controlling AI-generated insights
05Portfolio Construction and Back-Testing
- Optimisation and risk budgeting
- Robust back-testing design
- Transaction costs and capacity
06Governance, Ethics and Deployment
- Model risk management and monitoring
- Fiduciary duty and explainability
- Moving models into production
Training Methodology
Applied Project & Assessment
Applied Project
Participants develop a small AI-supported investment strategy using Gulf or global market data, documenting data preparation, model choice, a robust back-test with costs, portfolio results against a benchmark and a model governance and monitoring note.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.