Portfolio Optimization:
A General Framework
For Portfolio Choice

Better investment performance is a function of the accuracy of your return estimates, and optimal diversification1. Yet most investors eschew optimal diversification because they’ve been led to believe it’s too hard.

But the fact is, the choices you make about portfolio construction express strong views about your fundamental beliefs. This fact is inescapable. The only question is whether you want to express these views consciously or unconsciously.

The goal of this paper is to describe a stepwise framework for investors to choose the most optimal way to form portfolios, as a direct expression of their beliefs and assumptions. In particular, we provide:

  • Detailed descriptions of several methods to form optimal portfolios without explicit return assumptions
  • A Portfolio Optimization Decision Tree to choose the most appropriate optimization based on certain active views and assumptions about relationships between risk and expected return
  • Case studies of historical relationships between risk and return for global equities and asset classes, with strong implications for optimal portfolio choice

This guide is aimed directly at investors and practitioners. We are confident that investors who follow the Portfolio Optimization Machine framework will produce better performance, regardless of investment process. Download the report now!


1 Summarizing Grinold’s Fundamental Law of Active Management, which states that Information Ratio = Skill x SQRT(breadth), where breadth is the number of uncorrelated sources of information x rebalance frequency.