SmartETF

Checking In on the All-ETF Portfolio

You used the term correlation; not I. Correlation refers to a form of dependency model, better known as linear correlation. If this is not the forum to express sophisticated methodologies, then why is it the forum to showcase simplistic ones? The theme was to highlight the de-coupling of indexing and correlation. That is what I was addressing.
I have read your past posts and we obviously differ in several areas; but only because I’ve been in the business 25 years, have built models since the mid 80’s that have put me in the top 1% for two decades, and mostly read whitepapers, not trade magazines. In the future I would welcome a debate on your previous posts. In previous articles you cited LTCM as the exception, not the rule, I disagree. You note the underperformance of active managers, yet who determines who is active or who is a closet indexer? Two Yale professors recently came out with a new model that determines if a manager is truly active (based on 23 years of research). Truly active managers soundly beat the index after fees and expenses! BTW, I do not consider active managers to be the same thing as market timers. Taleb may be egotistical but he is right none-the-less.
I will love to resume this conversation in 10 years after you’ve experienced what is to come. My word of caution is to avoid absolutes, avoid getting sucked into the academic theories, and be careful who you choose to debate. I’ve chosen to fight the popular opinion my entire career; it’s a tough road. My best word of advice is for you to never stop asking ‘why’. As Kennedy once said “How could I have been so wrong as to trust the experts?” Don’t let a little criticism stunt your progress. I applaud your efforts and cherish your enthusiasm.

Checking In on the All-ETF Portfolio

Geoff, I meant no offense, my goal is to help investors universally.

First, Mandelbrot wrote his book four years ago and we had already built the same concepts several years before. So the fact he hadn't seen the models work doesn't mean they don't exist.

Second, I like your idea’s but I’m highlighting the weaknesses in your models and offering improvements to help; not to be critical. You choose to indirectly advertising QPP so I choose to freely suggest how models can be improved.

So the question is:
a) Do you believe normal distributions are better than stable distributions? I welcome this discussion.
b) Do you believe linear correlation is better than copula dependency? I welcome this discussion.
c) Do you believe a concept from 1952 called mean-variance can possibly be better than another Nobel winning formula created this decade? I welcome this discussion as well.

I’m not advertising our firm but will share our asset allocation mix as of July 22, 2008, the date of our last rebalancing.

2.00% Vanguard Health Care VIPERs VHT
2.00% Vanguard Industrials VIPERs VIS
5.00% Short QQQ ProShares PSQ
3.00% iShares MSCI Brazil (Free) Index EWZ
2.00% iShares FTSE/Xinhua China 25 Index FXI
3.00% iShares S&P Latin America 40 Index ILF
2.00% BLDRS Emerging Markets 50 ADR Index ADRE
3.00% PowerShares DB Agriculture DBA
2.00% iShares Dow Jones US Real Estate IYR
3.00% iShares GS $ InvestTop Corp Bond LQD
71.50% iShares Lehman 1-3 Year Treasury Bond SHY
1.50% Money Market Fund MMA


Because asset allocation models all follow a 3 step process I will describe the advantages in each step:

1) Step 1 (univariate model): a Stable ‘t’ distributions with GARCH features better determines the current risk & forecasted return of a security as opposed to a rear-view mirror approach based on normal distributions.
2) Step 2 (bivariate model): a copula dependency more accurately examines the current relationship between two securities as opposed to linear correlation based upon long-term averages.
3) Step 3 (multivariate model): ranking the bivariate models with Monte Carlo modeling to forecast produces an optimal mix based on current market conditions as opposed to Monte-Carlo modeling based upon long-term averages.

So you can see from our current allocation the risk/return trade-off for securities is unattractive in the current market conditions and the recent volatility is keeping investment out of securities which have traditionally low or negative correlation.
So you see I have only upgraded the basic attributes of asset allocation and continue to follow the same 3-step process as you and the other solution providers.

Again, I wasn’t trying to change the conversation over to EVT but was trying to address the blindness in risk modeling and offering a solution to the problem.

All the best -

Checking In on the All-ETF Portfolio

One contributor wrote: “it is impossible to ‘predict’ fat-tail events”. Yes, using traditional asset allocation models. I have used Extreme Value Theory for four years and have avoided all the major sell-offs, including 9-11, and the fun year of 2008. Our growth models range from +2% YTD to minus 4.5% YTD depending on the fund universe. Check out the latest books by Benoit Mandelbrot or if you prefer heavy reading, any of the recent books on EVT.

Checking In on the All-ETF Portfolio

Finally an intelligent string of correspondence!

Yes, the MPT, APT and follow-up portfolio theories rely on normal (arithmetic) distributions, henceforth ignoring fat-tail events. Converting to a stable (log) distribution exposes the tails to more accurately define the actual risk.

These traditional models rely on mean-variance which is why they predict 10% annualized returns (based on the past 80 years. It is ~ 7% over the past 180 years). Using mean-variance, your clients should have doubled their money since 1998; but in fact the S&P 500 is still down after 10 years. Making a bet on the law of large numbers is great if you have 50 years for a pay-off; I don’t. Markets move through economic cycles that reward equities positively on average 18.5 years (bullish) and penalize equities negatively on an average 17 years (bearish). It can take up to 20 to 30 years for an investor to break-even if invested at the market highs.

An upgrade can be made to mean-variance with a newer Noble prize winning formula (circa 2003) called GARCH (Generalized Auto-Regressive Heteroskedacity) that tracks the clustering of data.

The third problem with traditional models is that they assume markets are static as well as correlation. We all know that correlations increase as markets become more volatile. Why not have real time correlation models? You can upgrade linear correlation to a dynamic correlation by switching to a copula dependency model.

Upgrade your 1) risk analysis using stable distributions, 2) time-series using GARCH, and 3) correlation dependency using copula dependency. These upgrades significantly add to performance and are the underlying concepts of Extreme Value Theory.

What Is Diversification Worth?

Good try but you have an archaic application to asset allocation. Asset allocation relies on four basic attributes: Risk, Return, Dependency (Correlation), and Data Management. These four attributes are managed in a 3 step process: the first step, called a ‘Univariate Model’, measures the risk & return of an asset, the second step, or ‘Bivariate Model’ measures the dependency between two securities, and the third step ranks the bivariate model in what is called a ‘Multivariate Model’ to create the efficient frontier. Your model contains four critical flaws:
First you use the most simplistic measure of dependency to diversify a portfolio with the use of correlation. Correlation assumes a fixed relationship between two securities over the sampled time period and is purely academic. Correlation increases dramatically during extreme events, in fact during any volatile market. If you want a lesson in correlation look to the merry band of MPT disciples at Long-Term Capital Management for a classic case study, or look at Merriweather’s current performance! As the adage goes ‘the only thing that goes up in a down market is correlation’. Trash the static correlation model and move to a dynamic correlation model like Copula Dependency.
Second, you measure risk using standard deviation (σ). Standard deviation, semi-variance and Value-at-Risk are all hyper-flawed because they all rely on normal distributions. Do you really think a 5σ event will only occur every 7000 years or an 87’ magnitude crash will only happy once in every three lifetimes of the universe? Enlighten yourself to the world of Stable Distributions using logarithmic, not arithmetic distributions. You will find 5σ events really occur every 3-4 years. I recommend you convert to Expected Shortfall as you new method of risk measurement.
Third, how can you forecast using any of the methods you suggest? Running a simulation model using Black-Litterman (an Arbitrage Pricing Theory model) or the other solutions are simply band-aids on the old MVO model; the only difference is you are trying to tilt the results to more of a bullish or bearish state. This doesn’t solve the problem it just makes it less damaging. Why not take a scientific physics approach and use a data management tool like GARCH (that won the Noble Prize in 2002) instead of relying on Markowitz and his methodology from 1959? You do know you have faster processors and electronic data exchanges and advanced math models; why not upgrade after 40 years?
Since this article is ostensibly an advertisement for Quantext, I feel its fair game to point out the inherent flaws in your model as well as other suggested models using old mathematical applications and theories. I’m happy to unconditionally prove the superiority of newer models and their specific attributes and will cite the works of Benoit Mandelbrot and Extreme Value Theory as a comparative solution. Set yourself free from averaging thinking!