AI in Smart Beta & Quant Strategies

Modern quantitative investing is being transformed by AI techniques that uncover deeper market patterns and refine factor based strategies. This course focuses on how machine learning can enhance Smart Beta design, factor timing, portfolio construction, and alpha forecasting. Delegates will explore advanced AI tools and backtesting frameworks that bring traditional quant strategies into the next generation of data driven investing.

TRAINING TOPICS

• AI enhanced factor discovery (value, momentum, quality, low volatility)
• Machine learning for multi factor model construction
• Smart Beta portfolio optimization under risk constraints
• Forecasting factor returns using supervised learning
• Unsupervised learning for clustering stocks and regimes
• Time series analysis of factor rotation and macro sensitivity
• Backtesting and performance attribution for Smart Beta portfolios
• Integrating alternative data into Smart Beta signals
• Explainable AI in factor model transparency (SHAP, LIME)
• Deploying quant models into production with MLOps

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

• Construct Smart Beta portfolios using AI enhanced factor selection
• Apply machine learning models to forecast factor performance
• Use clustering and unsupervised learning to segment investment universes
• Interpret model predictions and contributions using XAI techniques
• Conduct rigorous backtests with performance attribution
• Apply risk based optimization techniques with AI
• Integrate macroeconomic and alternative data in factor models
• Build scalable ML pipelines for quantitative investment strategies

TRAINING IS TAILORED TO

• Quantitative portfolio managers and Smart Beta strategists
• Risk and investment analysts at asset management firms
• Data scientists and AI professionals in finance
• Product developers in index and ETF construction
• Fintech teams building algorithmic investment platforms

TRAINING METHODOLOGY

• Instructor led technical lectures and real world case studies
• Hands on Python labs using finance and ML libraries
• Guided projects for Smart Beta portfolio design
• Peer discussions and group based problem solving
• Code walkthroughs and real time factor modeling
• Evaluation via live model performance dashboards

DAY ONE:

INTRODUCTION TO SMART BETA AND AI APPLICATIONS

• Smart Beta: Concepts, objectives, and current strategies
• Traditional vs AI enhanced factor investing
• Overview of AI tools for portfolio design
• Factor exposures: academic vs empirical approaches
• Lab: Basic Smart Beta portfolio in Python
• Case study: Smart Beta ETF performance breakdown

DAY TWO:

FACTOR IDENTIFICATION & AI BASED FORECASTING

• Feature selection for factor returns using ML (Lasso, RF, XGBoost)
• Target variables: Return, risk, Sharpe ratio forecasts
• Building supervised learning pipelines
• Cross validation and model tuning
• Lab: Build and evaluate AI based factor signal models
• Case: AI vs traditional factor rotation models

DAY THREE:

UNSUPERVISED LEARNING FOR FACTOR CLUSTERING

• Clustering stocks by factor profiles (K means, DBSCAN, PCA)
• Dimensionality reduction for investment universe mapping
• Regime classification using clustering and HMM
• Lab: Build factor clusters and regime aware strategies
• Visualization techniques for clustered factors
• Use case: Building diversified Smart Beta baskets

DAY FOUR:

PORTFOLIO CONSTRUCTION & BACKTESTING

• Risk budgeted optimization frameworks
• Smart Beta with ESG or alternative signals
• Backtesting with transaction costs and constraints
• Performance attribution across multiple factors
• Lab: Portfolio optimization and strategy backtest
• Tooling: PyPortfolioOpt, Backtrader, QuantConnect basics

DAY FIVE:

EXPLAINABILITY, AUTOMATION & STRATEGY DEPLOYMENT

• Explainable AI: SHAP & LIME in Smart Beta models
• Automating factor pipelines with MLFlow & Airflow
• Monitoring model drift and retraining schedules
• Dashboarding Smart Beta performance (Power BI, Streamlit)
• Final project: Build and present AI powered Smart Beta strategy
• Feedback, wrap up, and future tools in AI for quant investing