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.

Asset Allocation and ETFs: Pimco's El-Arian in 2008

Performance of this magnitude is a result of using mean variance and ill-fated risk metrics like VaR and Standard Deviation. I spoke at last years Schwab conference and warned of the dangers of using these older asset allocation methodologies and warned of the risk in the markets at that time (last Fall). Managing ETF's using newer theories like Extreme Value Theory would have resulted in losses of 13.2% YTD. Extreme Value Theory and its application to asset allocation, Dynamic Portfolio Optimization, would have prevented this level of loss because it uses recent Nobel winning concepts like GARCH instead of 50 year old ideas like MVO, and replaces normal distributions with stable distributions (log-based distributions with fat-tails that scale). Read Mandelbrots book 'The (Mis)behavior of Markets.

Tracking Mean Reversion After Bad Months

Our research concludes that you are on the right track but your focus is too linear. Mean reversion over the long-term is an academic boon for getting a Nobel Laureate designation but it does not translate into a workable application in the real world. For example, MVO demonstrates domestic equities have returned 10% over 80 years. Therefore, you should get a 10% return on average. In the real world, the domestic equity market is down over the past 1, 3, and 10 years; yes 10 years. Granted it worked in the 80’s & 90’s, but not the 60’s & 70’s, and definitely not this era. It’s like a broken clock that is right twice a day; it is devoid of market cycles.

Short-term MVO is very interesting and much more meaningful. The question is, and will always be, what time frame is best for analyzing the time series of data (aka, time parameter estimation). I think you are off track when you try to curve fit your data by selecting a particular number of months. Markets don’t move in a linear pattern like monthly. You will have much greater success by rebalancing when markets move by a defined level of volatility or price (or both). Take volatility as an example, last February the market hit an extreme level of volatility (and price drop); buying at the level would have been very profitable. It is these extreme moves (up & down) that create the fat-tails of distributions and are reflected in the extreme technical patterns like Relative Strength. A more scientific approach is to go with a Noble winning approach from 2002 (in effect tossing the MPT model from 1959) and incorporate Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) which examines the clustering of data; basically, a scientific approach to short-term mean-variance. The analogy is MVO works like the Farmer’s Almanac for predicting weather; whereas GARCH acts like the Doppler Radar. Alternatively, you can use price and volatility movement to create a poor man’s GARCH model to track short term mean-variance. Cheers -

Asset Allocation as a Method for Risk Management

This is an excellent illustration of the merits of diversification. Thanks for sharing -

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!

A Practical Demonstration of the Value of Portfolio Theory

Good effort but you are still trying to squeeze blood from a turnip. You have made assumptions about asset allocation that are very outdated. Have you every wondered why: 1) you don’t see outliers (black swans) in your models; 2) your models aren’t able to respond to current market conditions, or 3) your Monte-Carlo models are ineffectiveness at avoiding major sell-offs (thus being down so much in the time frame you examine)?

First and foremost is you are assuming a normal distribution in your analysis. Sharpe and Mandelbrot rebuke normal (arithmetic) distributions in favor of stable (logarithmic) distributions. In a normal distribution the odds of a 5σ (std. Dev.) event is one in 7000 years when in reality its one in every 3-4 years (as seen in a stable distribution). Any model using a normal distribution is blind to the real world and the results are merely academic.

The second major flaw is your attachment to Mean Variance Optimization (MVO). Who cares what the recommended asset mix is when its averaged over a long historical time frame. You can be in a bullish of bearish state for 20 years. To assume MVO works is akin to assuming a broken clock works because it properly tells time twice a day. Add three months of new data to an MVO model and tell me if it changes your recommended allocation. It doesn’t because what can 3 months of data do to a model when averaged with decades of information? Had you elected to use the works of more recent Noble laureates (not those from the 1950’s) you would come across the GARCH models (awarded in 2002). MVO is to the Farmer’s Almanac as GARCH is the Doppler radar.

The final obvious flaw is using Monte-Carlo in conjunction with MVO. What good is it when it relies on long-term data that has been averaged over time? Try using M-C simulations on Stable Student-t distributions combined with GARCH and you would find yourself at the start of this year in 50% 1-3 year Treasuries and other risk adverse securities after enjoying a banner 2006 & 2007. The worse thing to happen is that ‘old schoolers’ will call you a market-timer as you laugh your way to the bank.

I suggest you read ‘The (Mis) Behavior of Markets” by Benoit Mandelbrot to get you out of the 50’s and upgrade yourself to the 21st century.