SmartETF

Testing Forward Looking Asset Allocation

Your right, our SMA accounts have demonstrated substantially higher returns with a lot less risk; nothing special there. Meanwhile, you're looking at the allocation on one specific day in one specific fund. Brillient -

Testing Forward Looking Asset Allocation

Ah yes, you've found me, congratulations. Yes we are at 40% fixed income but a week ago we were 78% in SHY (1-3 year treasuries) and have been heavily invested in short term treasuries all year. Note: this is only an asset allocation model which rebalanced monthly. Our models are still very skeptical and it still prefers to be partially short, but I’ve over-ridden the model because the current valuations are worthy of extending our equity position. Also of note, we are not 60% equities but have a 3.5% in commodities, 7% in real estate, and 36.5% in S-T treasuries and cash. The model recommends 60.5% S-T treasuries and cash, 7% RE, 3.5% Commodities, and 29% equities with a 5% short position.

Out TIAA-CREF accounts are only 2% equities while the Paclife funds I mentioned earlier hold 14% in equities. If you would like to continue this bout I suggest we take it offline because it is counter to my goal of educating and is totally unproductive. Signing off -

Testing Forward Looking Asset Allocation

I was trying to support your claims, not tarnish. I was not attacking but rather educating; I wish you could see it in its true light. I don’t print my results because I don’t want to come across as self-promoting. What Mandelbrot wrote 4 years ago was already in use and has been improved upon. If you want proof that Extreme Value Theory works check out my fund Aston Smart Portfolios Allocation Fund 'ASENX'.

The TIAA-CREF overlay account since inception (9 funds: June 2006 – Sept 2008) on their family of funds are up 5.98% with a std. dev. (SD) of 8.85% compared to an index (35% S&P 500, 35% EAFE, 30% Lehman Agg) of 0.01% and SD of 9.91%. The S&P 500 returned -1.73% with SD of 12.55% and the NASDAQ returned –0.95 with SD of 16.79%.

The Pacific Life overlay account for VUL (includes all insurance costs and loads from June 2005 to Sept 2008) delivered 6.76% vs. 1.11% for the S&P 500, 1.2% for Nasdaq, and 2.61% for the same index. The risk (measured in SD) for the same were PacLife account 5.44%, S&P 500 5.05%, Nasdaq 6.39%, and index 4.61%.

If you want to see the numerous SMA accounts we manage I'd be happy to show them to you but I doubt you would change from the QPP punch you’re drinking. If you want proof that ‘Expected Shortfall’ is far superior to standard deviation and Value-at-Risk I can prove that all day long. If you want proof that GARCH is superior to mean-variance that too is a no brainer. If you really think linear correlation is superior to copula dependency then god help you and your clients. I have always commented that your model is better that the typical MPT/MVO model; I still do. The only times I’ve been defensive with you is when you try to defend your models against newer methodologies that you obviously have no desire to learn or embrace.

Last year I warned investment professionals at many of the major investment conferences where I spoke (Schwab, FPA, CFA, NAAIM, etc) that the market was set for a major decline; but they hung onto the sinking MPT ship. I’m in the business as an advocate for investors and I’m obviously passionate about educating advisors about the new models to help them help their clients; why else would I share all this info without listing the firms which use our models? What is your motivation? If you want to go toe-to-toe bring it on. Personally, I’d rather work together to increase the awareness of better models to help investors and improve the reputation of our industry. Your call. But as long as you write about asset allocation in public forums I have the right to enlighten advisors and protect investors.

Testing Forward Looking Asset Allocation

Can we finally kill Modern Portfolio Theory? How much pain does one need? If it worked why are all of your accounts so decimated? Why is Meriwether on the verge of another Long-Term Capital explosion and where are your 10% annualized returns?

Flaw #1: MPT relies on normal distribution which by defaults ignores risk outside of 3 standard deviations (sigma 1=68.3%, sigma 2=95.4%, sigma 3=99.7%), or .15% for each tail. So what are the odds of a 5 sigma event? It’s one in 7000 years. So tell me why have we had a half dozen 5 sigma events or more this year? The use of normal distribution is simply wrong. Changing to a log-based distribution called a ‘stable distribution’ exposes the tails to better see risk.

Flaw #2: MPT uses linear correlation. This static approach illustrates the average relationship of 2 assets over time. This is crazy because as Geoff Considene accurately points out the relationships change (becoming more correlated) as volatility increases. The old adage is the only thing that goes up in down markets is correlation. So why not use a dynamic correlation model that adjusts for volatility. It exists and it’s called a ‘copula dependency model’.

Flaw 3: MPT is based on long-term averages. If in 2000 you used 40 years of history to model equities returns you would expect returns of 10%. Using 15 years you would return 15%, using 3 years you would model 30%. So the key in most models is know how much data to use and how best to weight the data. The author here is weighting new data as being more valuable; which is actually true. The danger in doing this however is during cycle changes (like 2000) and it disregards historical patterns. So exponentially weighting may be better than mean-variance but EWMA’s have its main flaw is its inability to appreciate historical data (patterns like a 60’s or 70’s market) because it works like a present value model that heavily discounts old data.

Using a physics concept called GARCH takes the historical data and examines its recent clustering effect; think Doppler radar instead of MVO’s Farmer’s almanac approach. The GARCH concept won its inventors the Nobel Prize in Economics in 2003, a bit more recent than the MPT concept developed 50 years ago.

Flaw #4: Rebalancing more than once a year is considered inefficient to the MPT user; which is true. That is because new data is meaningless. Adding a month of new data into a data set of 40 years is like throwing a bucket of water into a pond; meaningless. This is why asset allocators always hold fast to their 60/40 mix range. If you could value newer data as more valuable you would rebalance more often to capitalize on the change in market conditions (risk & return). This is why the QPP model is trumping MVO/MPT because it rebalances more than the buy & hold and it ostensible values newer information as more valuable.

The bottom line is MVO models just don’t work except in long up-trending markets, but you’ll never outperform. Mandelbrot, Sharpe and several of the other inventors of MPT/CAPM express their issues about their creation and yet these words go unheeded because it is in the text books and accreditation programs. Mandelbrot recommends Extreme Value Theory (EVT), a dynamic approach to asset allocation. Using EVT for the past four years you would have had nearly twice the returns of the basic 60/40 mix in the up years and been down only half that amount this year. That is serious alpha. So let’s bury MPT/CAPM and these mean-variance models and take a scientific approach to investing.

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.