Building Portfolios That Overcome Financial-Cycle Risk

Building Portfolios That Overcome Financial-Cycle Risk

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This is part two of a two-part series. Part one, Credit, Finance and Market Stability, was published on July 5.

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In my previous article, I examined why regime-based strategies should incorporate financial-stability considerations, despite the decision by mainstream macroeconomic models to ignore these (so-called “black swan”) risks. From an investment perspective, mainstream macroeconomic models (measures of GDP growth and inflation) are too narrowly defined and thus missed the 2007-2008 crisis. Many investors have twice lost 40% of their portfolios since 2000.

From an investment standpoint, those risks deserve our attention.

My motto is “better not to lose capital in the first place.” Once a portfolio has fallen by 40%, it must generate a return of 67% just to get back to even. I will illustrate how a regime-based framework can protect capital when markets deteriorate, while adding value when conditions improve. Building this framework requires that we understand how macroeconomic and financial stability risk interact. This challenge has become increasingly important since financial markets were liberalized and deregulated beginning in the 1980s.

Building a regime-based investment framework

To build a regime-based framework, we must first identify relevant data series that correlate with the macro-financial cycle. Rigorous statistical and quantitative methods can then be used to identify distinct market regimes based on these factors. In developing a regime-based framework, we can group macroeconomic and financial data series into five categories: (1) market sentiment, (2) interest rates, (3) private-sector balance sheets, (4) real economic factors, and (5) asset prices. I will apply a statistical process to the selected data series that generate a regime-framework.

I can illustrate the intuition behind this process by using a simplified example (see the chart below) that consists of three data series: (1) GDP, (2) VIX and (3) S&P 500. The chart below standardizes and plots quarterly data for GDP growth and the VIX, and uses actual returns for the S&P 500.

In the above chart, when GDP growth is strongly positive and the “fear index” (VIX) is declining (e.g., in 2003-2004), the S&P 500 index tends to increase in value. Conversely, when the VIX increases and GDP growth slows (most notably during the 2008 crisis), the S&P 500 declines in value. My framework is designed to adapt to changes in market conditions. Average quarterly correlations between the three variables throughout the 37-year period are provided in the table below (keeping in mind that correlations are not stable, which explains my preference for an adaptive regime-based framework):

Financial-Cycle Risk

Let’s examine the behavior of the three data series in the table below for the following periods:

  1. Boom: April 1993 to September 1996
  2. Tech Bubble/Recession: January 2001 to March 2003
  3. Housing Boom: July 2003 to March 2006
  4. Great Financial Crisis: January 2007 to March 2009

Financial-Cycle Risk

By John Balder, read the full article here.


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