AI For Asset Managers

Overview

1 day practical course on the latest cutting-edge artificial intelligence (AI) applications by asset managers in their investment strategies across a range of asset classes.

Learning Objectives

How supervised, unsupervised and deep learning machine learning methods are being used by asset managers to construct trading strategies with superior risk-adjusted performance
Using AI for stock selection, asset allocation (including for multi-asset funds), timing and implied volatility signals
Using geolocation, credit card and other proprietary fundamental data in equity and credit investment processes

Who the course is for

Asset managers
Portfolio managers
Analysts
Quants

Day One Morning

Introduction

Alternative data sources – from individual activity (e.g. social media), business processes (e.g. credit card transactions) and sensors (e.g. satellite imaging, smartphone GPS-based geolocation, internet of things)
AI vs machine learning (ML)

ML Methods In AM – Supervised Learning

Regression. Advantages of ML regression vs ordinary least squares. Penalised regression techniques - Lasso, Ridge and Elastic Net. Non-parametric regression – K-nearest neighbours and LOESS regression
Case Study 13 – Using penalised regression techniques to forecast returns for multi-asset trend-following strategy across S&P 500, USTs and gold
Case Study 14 – Using non-parametric regression to generate optimal risk-premia weighted asset allocations in multi-asset portfolio
Case Study 15 – Using XGBoost to forecast returns of long-short equity ETF sector strategy based on multiple macro risk factors. Comparison to S&P 500
Classification. Generation of buy and sell signals. Classification of volatility regimes. Logistic regression, Support Vector Machines (SVM) decision trees, Random Forest and Hidden Markov Models (HHM)
Case Study 16 – Using logistic regression to improve equity call over-writing strategy
Case Study 17 – Using SVM to generate implied vol buy and sell signals in FX options
Case Study 18 – Using Random Forest in stock selection
Case Study 19 – Using HHM for marketing timing decisions on S&P 500

Day One Afternoon

ML Methods In AM – Unsupervised Learning

Clustering for regime identification to improve asset allocation (e.g. high or low volatility; rising or falling interest rates). Clustering algorithms - K-Means, Ward, Birch, Affinity, Spectral, MiniBatch, Aggregate, HDSBscan and ICA
Case Study 20 – Using clustering algorithms to identify inflation regime to support asset allocation
Factor analysis
Case Study 21 – Using Principal Component Analysis to explain variation in EURUSD implied volatility surface

ML Methods In AM – Deep Learning

Deep learning vs classical ML
Neural networks
Case Study 22 – Multi-Layer Perceptron Neural Network to construct equity sector long-short strategy and predict its performance
Long short-term memory (LSTM)
Case Study 23 – Using LSTM to construct S&P 500 timing strategy
Case Study 24 – Using Restricted Boltzmann Machine to construct FX trading strategy