Conceptual sketch for an "All-Weather-Portfolio" by deploying Adaptive Risk Parity

As stated in the title, the nature of this posting is conceptual. It is a survey for designing a portfolio that generates stable profits during every type of economic environment the investor is faced with. What is about to follow is partly a compilation of the information found in several sources* along with suggestions and idea's put forward by co-researcher "Ram". This post is also an open invitation to anybody who is willing to share valuable insights for improving the model.

The goal of this quest is not about generating the highest possible returns. Instead it is about creating a portfolio with a risk profile as close as possible to cash, but with yields much higher than cash. The idea is to reduce the portfolio's overall volatility by investing in assets that naturally move in opposite directions. One of the largest hedge funds in the world, Ray Dalio's Bridgewater, applies this investment philosophy in their "All-Weather-Portfolio".

AWP is based on four premises:
  • The future is unknown and impossible to predict.
  • Assets are responding to two drivers: economic cycle (expansion/contraction) and inflation (high/low).
  • Asset classes thrive not equally during each of the four scenario's.
  • Risk is distributed equally over the four scenario's.
Instead of allocating equal amounts of capital to each asset class, the AWP-model assigns equal buckets of risk to the distinctive asset classes. Thus "risk parity" for each quadrant is accomplished to match equal odds for the next economical and inflationary condition. AWP assumes the economy transitions randomly from regime to regime and thus the AWP needs to have one or more asset classes working well in each regime. So AWP seeks for one or more asset classes generating decent returns in each quadrant. For instance:
  • Stocks for economic expansion and low inflation.
  • Bonds for economic contraction and low inflation.
  • Commodities for economic expansion and high inflation.
  • Inflation linked bonds for economic contraction and high inflation

Economic contraction

Economic expansion


25% of risk:
- inflation linked bonds
- precious metals

25% of risk:
- stocks
- commodities
- real estate


25% of risk
- government bonds
- inflation linked bonds

25% of risk:
- stocks
- real estate
- corporate bonds
    Table I  

How To Time The Market? Stochastics Rulez!

What if there was an indicator signaling a market turn ahead of time instead after the fact?
How about "Sell into strength and buy into weakness"?
Sounds impossible and a fools play?

Actually, applying an extra ordinary market filter is the way to go. Utilizing a stochastic crossover strategy in an unconventional setup yields a 60+% probability of a reversal signal. In a "long only" play, the win rate even jumps close to 85%. What is to follow is all about compounding and risk management.

Updated chart

Made in Switzerland: a Global Market Rotation Strategy for MDY, IEV, EEM, ILF, EPP and TLT

[Update added: See Postscript]

On Seeking Alpha Frank Grossmann published his Global Market Rotation Strategy (GMR). The goal of the GMR strategy is to achieve above average returns while avoiding big losses during market corrections. To that extent TLT is added as a "safe haven" during roaring bear markets. The GMR Strategy switches between 6 globally distributed ETF's on a monthly basis:
  • US Market (MDY- S&P MidCap 400 SPDRs)
  • Europe (IEV- iShares S&P Europe 350 Index Fund
  • Emerging Markets (EEM- iShares MSCI Emerging Markets)
  • Latin America (ILF- iShares S&P Latin America)
  • Pacific region (EPP - iShares MSCI Pacific ex-Japan)
  • US Treasury Bonds (TLT- iShares 20+ Year Treasury Bond ETF)

The algorithm of the GMR model is quite similar to Kevin McGrath's TAA model posted in Februari 2013, except that the GMR model offers the possibility to allocate assets based on weighting not only past performance, but volatility too.