ETF Prophet

Most asset return processes can be characterized as containing a primary trend, along with mean-reversion around that trend, as well as a certain amount of random noise.  Econometricians classify these elements using a Hurst Exponent as either : 1)black noise (trending/positive autocorrelations- Hurst>.5) 2) pink noise (mean-reverting/negative autocorrelations- Hurst<.5) or 3)  white noise ( no trend/mean-reversion, low/insignificant autocorrelations- Hurst=.5). [...]

The Minimum Variance Algorithm was compared to several standard optimization methods and algorithms in a recent set of tests done by Michael Kapler of Systematic Investor.  Michael created a webpage for MVA to review some details of these tests and also to summarize some of the background information.  We plan to release a whitepaper on [...]

The Minimum Variance Algorithm (MVA) follows much of the same logic as the Minimum Correlation Algorithm (MCA) and differs primarily in the objective function which is to minimize portfolio variance versus correlations. Both are “heuristic” algorithms that seek to approximate the results of more complex methods that require employing quadratic optimization. In a recent whitepaper, [...]

Often readers ask about methods for approximating minimum variance portfolios. In practice the minimum variance portfolio can be calculated in closed form only for long-short portfolios, and requires a quadratic optimizer to solve for long-only portfolios. Source code and examples for  long-only minimum variance can be found at Systematic Investor - a very good blog that [...]

The recent popularity of “tactical” investment strategies has given rise to a dizzying array of new terminology and strategy descriptions. Most investors and investment professionals lack a deeper understanding of the core nature of such strategies. They can hardly be faulted for all of the marketing material floating around that often obfuscates the difference between [...]

In the last post, we introduced the “All-Weather” Sector Portfolio which was developed using data from Fidelity Asset Allocation Research. I created a heuristic approach to integrate a variety of factors (length of  stage, sector performance ranking by stage) in order to create the final portfolio allocation. It is obviously very interesting to examine the [...]

The central concept of the “All-Weather” portfolio is balance: having an allocation that will perform equally well across different economic regimes.  The original portfolio balances portfolio risk and performance with broad asset classes to be neutral to changes in  economic growth and inflation.This basic concept can be extended to create an “All-Weather” equity sector portfolio. [...]

The All-Weather Portfolio was designed by Ray Dalio (and clearly influenced by Harry Browne of the Permanent Portfolio) as a robust static allocation that can be used by investors to deliver consistent performance over time. The logic of the portfolio construction is to be neutral to risk/uncertainty with respect to inflation or economic growth–the two primary [...]

In the last post we looked at the performance of static versus dynamic clusters on Dow 30 stocks. It is also logical to look at the same comparison on multiple asset classes. Michael Kapler of Systematic Investor ran the same set of tests on major market asset class ETFs for comparison.  To avoid distortion in [...]

A natural comparison for an allocation method that makes use of dynamic clustering is to use a static clustering method. An example of the use of static clustering are the sector classifications made by large index firms. Typically clusters are formed based on the type of business or industry associated with a company (ie utilities, energy [...]

Here is a backtest that was done using a dynamic clustering method introduced by Michael Kapler at  Systematic Investor combined with multiple allocation schemes: 1) equal weight within and across clusters 2) risk parity within and across clusters and 3) cluster risk parity (CRP):  equal risk contribution (ERC) is used within and across clusters. For comparison purposes, [...]

Here is a  cluster representation  of some of the major markets that are traded internationally. The groupings were formed using data over the past year with a  clustering algorithm that is proprietary (correlation is used as a distance metric). What is interesting is that this particular cluster grouping has persisted  without much change over the [...]

In the paper we wrote on The Minimum Correlation Algorithm we introduced the Composite Diversification Score (CDI). The purpose of this measure was to demonstrate how well a set of portfolio weights has minimized the average portfolio correlations and also balanced the risk contributions from each asset as  measured using the Gini coefficient of inequality. The [...]

Cluster Risk Parity (Varadi, Kapler, 2012) is a method to improve upon the deficiencies of Risk Parity and Equal Risk Contribution: a) the need for manual universe selection  (see All-Weather and Permanent Portfolio) and b) imbalanced risk exposure as a function of the universe selected. To highlight the latter issue it is worthwhile to take [...]

The following graphic is borrowed from a static risk parity approach via Salient Capital Advisors: http://www.theriskparityindex.com/static/pdfs/Salient-Risk-Parity-Index-White-Paper.pdf.  The visual is useful for readers to understand the nuances and relative merits of a Cluster Risk Parity (CRP) approach. In their approach the individual assets and clusters are defined in advance, and thus there is no dynamic clustering method used. However, the [...]

One of the concepts that I have developed with Michael Kapler at Systematic Investor : http://systematicinvestor.wordpress.com/ is a  method of passive portfolio allocation (omitting expected or historical returns) that captures the true spirit of diversification. It is a more elegant but also more complex than our heuristic algorithm: Minimum Correlation. This new method is called “Cluster Risk [...]

The All-Weather Portfolio was introduced by Ray Dalio- the founder of Bridgewater -which is arguably the largest and most successful hedge fund in the world. His landmark concept was to create a portfolio that would have roughly equal risk in four different economic regimes: 1) rising growth 2) falling growth 3) rising inflation  and 4) falling [...]

This graphic is designed to help readers understand the logic and assumptions embedded in the Permanent Portfolio model by Harry Browne. It is also a useful framework for understanding how to construct  regime-based portfolios. The results are re-published from an earlier article written by Corey Rittenhouse at Catallactic Analysis: http://catallacticanalysis.com/permanent-portfolio/. It was a very good post [...]