Our Approach

Investment Philosophy

We Differ from the Current Industry on the Definition of Risk

For decades, financial players, regulators, and academics have equated risk with uncertainty in the form of volatility or deviation around an expected mean return.  Standard deviation, the square root of variance, has been the predominant measure of this definition of risk.

Volatility both incorrectly penalizes out-performance and measures out- and under-performance as equal when mathematically it is not (absolute dollars gained versus lost).  In addition, all return distributions are not symmetrical, which is proven by empirical evidence.

Downside Risk Probability: Risk is loss of capital

When a client suffers losses, the capital base is eroded.  A loss of 50% on capital will need to earn 100% on that new lower base to get back to the original dollar starting point. We hold this as the core driver of our approach, focusing on the high penalty suffered when there are portfolio investment losses while at the same time not preventing our models from participating in portfolio investment gains.

Investment Process

Why Macro?Why ETFs?Why Tactical?Investment Process
PreviousNext

Why Macro?

Macroeconomics is the primary driver of our portfolio modelling process. Studies have demonstrated that macroeconomics has a significant influence on broad asset class behavior.

We apply behavioral economics to decision making with a focus on historic economic environments and regime shifts. We use this information to forecast future behavior of the asset classes that we use in our model portfolios. This behavior includes both expected returns of the asset classes and the correlations among the asset classes in the various environments. Traditional bottom up approaches do not factor in the impact of current and shifting macroeconomic developments.

Why ETFs?

We use ETFs to gain exposure to the asset classes that we model. ETFs are an efficient choice for an asset allocation model because of their benefits including:

  • Transparency
  • Low MERs
  • Tax efficiency
  • Diversification with a single trade
  • Accessibility—Traded throughout the day

Why Tactical Asset Allocation?

Forward looking, non constrained, fluid, downside risk aware.

Rather than relying on a backward-driven view generated by historical statistics, Frame Global formulates a forward looking view driven by macroeconomics.

We focus on economic regime based risk of probability of loss as it relates to asset classes and create diversification among asset classes accordingly. When it is appropriate, this approach will result in being 100 percent in or out of a single asset class or cash.

Asset allocation has historically been a static process for many institutional investors. We recognize the dynamic nature of markets and time asset allocation decisions that reflect shifts in expected behaviour in economic environments rather than a calendar basis.

We seek to explicitly measure and manage probability of suffering loss exposures.

Investment Process

Frame Global Asset Management determines asset allocations for multiple global tactical strategies of ETF-only portfolios following a disciplined Investment Process:

  1. Develop the macroeconomic forecast to form the monthly outlook that drives the asset allocation process. We proportionately time-weight possible economic environments over a 12-month horizon.
  2. Model the behaviour of asset classes under different macroeconomic environments. Modelled asset classes are global and include equity and fixed income, different market capitalizations and Gold.
  3. Using environment dependent data, run the portfolio optimizing software to determine the portfolio’s asset allocations constrained to a specific level of downside risk.
  4. Rerun the optimizer for different levels of downside risk to generate portfolio allocations that suit a range of risk tolerances from conservative to aggressive.
  5. Conduct ongoing research on the global ETF universe and select ETFs that are the closest proxy to the asset classes that were allocated to during the optimization.