Advanced Factor Investing & Asset Allocation with Machine Learning: Using a database of one billion data points for nearly ten thousand economic indices, funds, and financial instruments, design and scientifically test investment strategies using modern methods that far exceed traditional econometric studies. We explore conditional probabilities and real-world details (like currency controls, taxes, liquidity, and market impact) that are the failing of most academic studies and make-or-break practical efforts.
Michael Robbins (https://www.linkedin.com/in/michaelrobbins/) has decades of experience investing multi-billion dollar portfolios. He has been the Chief Investment Officer of four large firms and Chief Risk Officer of a large retirement system. With a background in nuclear physics, he employs an evidence-based “quantamental” and econometric philosophy.
A preliminary list of topics includes:
Investment Process Overview
Financial Data
Quantamental & Econometric Models
Fundamental Models
Market Models
Forecasting in Practice
Performance Measurement & Risk
Objective Functions & Condition States
Features
Modelling Using Machine Learning
Building Portfolios
Implementing Portfolios
Case Studies
A preliminary list of resources include:
A Big Data Database of Factors
Bloomberg Laboratory
Columbia App
Optional Research Project
MATAB Apps
MATLAB Code
Python Code
KDB+ Database
KDB+ Code
