As portfolio management becomes increasingly data-driven, AI is redefining how financial professionals construct, monitor, and rebalance portfolios. This course equips participants with cutting-edge AI tools and techniques for optimizing portfolio performance while accounting for risk, transaction costs, and market anomalies. Through hands-on labs and global best practices, delegates will learn how to build AI-powered strategies that deliver smarter, risk-adjusted returns across market conditions.
- Machine learning models for asset return prediction
- AI-based portfolio construction under constraints
- Deep learning for regime shifts and volatility forecasting
- Reinforcement learning for dynamic portfolio rebalancing
- Optimization algorithms (genetic, PSO, neural nets) for asset allocation
- Explainable AI (XAI) in investment decision-making
- ESG integration using AI sentiment analysis
- Stress testing and scenario modeling using synthetic data
- Sharpe ratio, Sortino ratio, and Omega ratio optimization
- Risk parity and AI-enabled factor investing strategies
- Apply machine learning for return estimation and risk forecasting
- Optimize portfolios using AI-based allocation techniques
- Use reinforcement learning to dynamically rebalance portfolios
- Integrate ESG and sentiment data into investment decisions
- Conduct stress tests with synthetic AI-generated market scenarios
- Interpret AI models with explainability frameworks (LIME, SHAP)
- Align portfolio strategies with risk-adjusted performance metrics
- Build end-to-end investment models using Python and AI platforms
- Portfolio managers and asset management professionals - Risk and investment analysts in banks, pension funds, and hedge funds - Quantitative researchers and data scientists in finance - Institutional investors and wealth management advisors - Fintech innovators and investment product developers
- Case-based learning with global hedge fund AI strategies - Live coding labs with Python (sklearn, PyPortfolioOpt, TensorFlow) - Interactive visualizations and dashboard creation - Reinforcement learning simulations with OpenAI Gym - ESG sentiment data integration using NLP APIs - Peer collaboration, real-time model evaluations, and expert feedback - Final project: build and present a risk-adjusted AI portfolio model
- Predictive models: linear regression, random forests, XGBoost
- Asset return modeling from historical data
- Data preparation, feature engineering for financial time series
- Lab: Return prediction with sklearn and yfinance
- Handling missing data and volatility clustering
- Real-world use case: AI in hedge fund alpha strategies
- Mean-variance and risk-parity concepts with AI extensions
- Advanced optimization algorithms: PSO, genetic, neural networks
- Transaction cost modeling and risk constraints
- Lab: Portfolio optimization using PyPortfolioOpt
- Building efficient frontiers and trade-off visualizations
- Integrating minimum volatility and diversification goals
- Fundamentals of reinforcement learning (RL) in finance
- Q-learning, DDPG, PPO for investment decisions
- OpenAI Gym environments for trading simulations
- Lab: RL-based portfolio rebalancing in volatile markets
- Reward design: return vs. drawdown trade-offs
- Evaluation metrics: Sharpe, Sortino, and max drawdown
- AI-generated scenarios for macro-financial shocks
- ESG data integration using NLP and sentiment models
- Deep learning for regime detection and crisis forecasting
- Lab: Build stress test engine with GAN-generated scenarios
- Real case: AI in sustainable investment strategies
- Scenario modeling aligned with PRI & SDG frameworks
- Explainable AI (XAI): SHAP, LIME for portfolio transparency
- Visualization and auditability for investment models
- Lab: Interpret model weights and feature influence
- Group project: develop an AI portfolio strategy with ESG inputs
- Present risk-return dashboards using Streamlit / Power BI
- Wrap-up: AI governance and compliance in portfolio strategies
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.