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
High inflation	25% of risk: - inflation linked bonds - precious metals	25% of risk: - stocks - commodities - real estate
Low inflation	25% of risk - government bonds - inflation linked bonds	25% of risk: - stocks - real estate - corporate bonds

    Table I  
The optimal AWP-model utilizes a basket of assets with a low correlation. Key is to select a set of asset classes in such a way that the portfolio will generate returns for any combination of economical and inflationary conditions. For this survey the next heuristically selected ETF's will be used: VTI, VEU, IYR, RWX, HYG, DBC, GLD, IEF, TLT and TIP.

	Economic contraction	Economic expansion
High inflation	- 12.50%: TIP - 12.50%: GLD	- 4.17%: VTI - 4.17%: VEU - 8.32%: DBC - 4.17%: IYR - 4.17%: RWX
Low inflation	-   6.25%: IEF -   6.25%: TLT - 12.50%: TIP	- 4.17%: VTI - 4.17%: VEU - 4.17%: IYR - 4.17%: RWX - 8.32%: HYG

  Table II  
Ranking and selection
Re-balancing of the AWP may take place weekly, bi-weekly or monthly. For each quadrant the best performing ETF is selected based on a 3 month benchmark of the performance of each asset in the portfolio (ROC(63)). If all assets in a particular quadrant go down at the same time AWP rotates to cash for the involved risk quarter. So if worst comes to worst and mayhem hits all corners of the market, AWP will move into cash for 100% as preservation of capital is of utmost importance.

Allocation
Allocation is grounded on volatility and correlation. For correlation AWP uses the static weights from Table II above. For volatility a combination of target volatility (TV) and historical volatility (HV) is used. Thus the allocation % for a each asset is:

Regime Risk Weight % * (TV/HV) Position Size = -------------------------------------------                           Σ (numerator for each asset)

where Regime Risk Weight is the static weight available from the AWP table, TV is an input value for Target Volatility (default 10%) and HV is the 20 day volatility averaged over 3 months (63 days).

For example, when aiming for a TV of 10%, then the allocation position size for TLT is thus calculated: PositionSize(TLT) = RegimeRiskWeight(TLT: 0.0625) * (10/HV(TLT)) / Σ numerators. 

Alternative (?)
Instead of selecting one asset from each quadrant, one could choose to simply pick the top 4 performers disregarding the quadrant it belongs to. For this variant it is necessary to sum the static weight for every asset class:

	Economic contraction High inflation	Economic expansion High inflation	Economic contraction Low inflation	Economic expansion Low inflation	Total
Stocks Real estate Corporate bonds Commodities Precious metals Inflation linked bonds Government bonds	12.50% 12.50%	8.33% 8.33% 8.33%	12.50% 12.50%	8.33% 8.33% 8.33%	16.67% 16.67%  8.33%  8.33% 12.50% 25.00% 12.50%

  Table III  
The allocation process matches the previous one except for the different static weights, which are in this case derived from summed total for the separate asset classes.  So using TLT again as an example, the numerator is calculated: RegimeRiskWeight(TLT: 0.125) * (10/HV(TLT)).

Experimental
Due to the experimental nature of this survey, this is an evolving post. As time allows updates may be expected. Hopefully interested readers will come forward with remarks, comments and new insights.
Next stop is building the code for a mechanical allocation algorithm based on the concept discussed.

*Sources
Especially the the comments made by Stefan Solomon are very informative:
http://discuss.morningstar.com/NewSocialize/forums/p/308106/3270831.aspx
http://tradersplace.net/forum/thread/141/quot-risk-parity-weighting-quot-side-effects/

Seeking Alpha contributions by MyPlanIQ:
http://seekingalpha.com/article/878251-bridgewaters-all-weather-portfolio-vs-harry-brownes-permanent-portfolio

http://seekingalpha.com/article/1224741-bridgewaters-all-weather-portfolio-with-risk-parity

and by David Cretcher:
http://seekingalpha.com/article/1242401-build-a-reliable-all-weather-portfolio-with-4-etfs

LearnBonds contribution by Marc Prosser:
http://www.learnbonds.com/all-weather-portfolio-ray-dalio/

thinkscript
The thinkscript studies used for the presented charts are available for review and copy/paste in the comment section.