Smart ETF

37 Comments

    • Active Funds Not Better In A Bear Market [view article]
      Has anybody studied S&P's data to see if their active manager database actually includes closet indeexers? A recent study from Yale (over a 23 year time frame) showed only 27.5% of managers are true active managers. I bet if you could extract the closet indexers from the list it would dramatically change the results in favor of the active manager. Also, are we discussing active managers or market timers, not the same thing to me. Lastly, how have they done this year? Aug 07 02:34 PM
    • Checking In on the All-ETF Portfolio [view article]
      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.
      Aug 06 01:48 AM
    • Checking In on the All-ETF Portfolio [view article]
      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 -       Aug 05 03:21 PM
    • Checking In on the All-ETF Portfolio [view article]
      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. Aug 05 01:34 PM
    • Checking In on the All-ETF Portfolio [view article]
      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.       Aug 05 01:18 PM
    • The Volatility Index Family Tree [view article]
      Unique homework, a capital idea! Thank you for this contribution. Parallel to this is the Family Tree of Risk. The early ETF's mirrored the broad market indices. The next wave was a land grab for sector indices; increasing the risk anywhere from 1.2 to 8.3 times the volatility of the broad markets. Then came the sub-sector ETF’s (example, biotech), inverse funds and leveraged funds; risk for these are in the nose-bleed section. Not until mid-2006 did we see a reversal in risk with the introduction of non-equity based ETF’s such as fixed income, real estate and commodities.

      It would be nice to have a volatility index that targets each asset class and sector in each country. We do this using Extreme Value Theory which incorporates fat-tail events (using Stable Distributions) and GARCH to properly weigh the data. Short of this complexity, a basic volatility index would at least demonstrate the general level of volatility.
      Aug 05 12:38 PM
    • Managing Portfolio Allocations With ETFs [view article]
      Where are your asset classes? You have 11 industries, yet you have 10.8% in financials. What about foreign securities? What about real estate, commodities, or alternatives? Secondly, where is the investment selection process? I don’t see how any asset allocation software could deliver the asset allocation mix you own. Thirdly, where is your risk management? If you change your risk model to Conditional VaR (Expected Shortfall) and weight your data using GARCH you would have avoided financials all together.
      Diversifying assets following the concepts asset allocation is not true portfolio optimization. You might enjoy reading Mandelbrot’s book ‘The (Mis) Behavior of Markets to better understand the newer asset allocation strategies like Extreme Value Theory. Cheers
      Jul 16 04:49 PM
    • Target-Date Funds Examined [view article]
      Target date funds and Lifestyle funds are the great marketing hoax of the decade. They assume long-term historical trends make good investment strategies and that bonds are safer than stocks; these are only several of the many faulty assumptions.

      If these funds existed in 1960 your recommended asset allocation mix, per age, would look very similar to the mixes recommended today. Over the next 20 years those accounts would have been devastated as interest rates sky-rocketed (killing bond prices) and stocks plummeted; both due to hyper-inflation. No combination of stock and bonds would have delivered positive results; in fact, it would have taken another decade for your portfolio to get above water had you invested at the top of the market in 1960 (in other words 30 years).

      This market just completed the longest bull market run in America’s history and these models refuse to believe it’s over because they look at past performance to predict future results. If you haven’t noticed the market is down over a 10 year time frame; how accurate are those assumptions?

      The logic of target-date funds is flawed. Target-date funds assume younger investors should be weighted more heavily in equities since they have more time and older investors should be invested more in fixed income. Let’s examine this year, the market tanks and your younger investors are getting crushed, why is that a good thing? A 20% drop requires a 25% increased to break-even. Some day, in the not to distant future, interest rates are going to increase and older investors will get laid away as their portfolio values plummet. Why is that a good thing? They might not have 30 year to break-even. Investors will no meet or exceed their targets or meet their lifestyle needs except in bull markets.

      A study by Elton, Gruber & Blake in December of 2004 determined that 63% of all 401(k) plans failed to offer enough fund choices to properly diversify a portfolio. They calculated the plan participants of these inadequate plans lost more than 300% of their terminal wealth over a 20 year period. This is a good argument for any kind of asset allocation model, including target-date/ lifestyle funds.

      However, the reliance on antiquated mean-variance models is just as appalling as Wall Streets aversion to taking on the fiduciary responsibility. Placing the responsibility onto the heads of plan participants is the ultimate scapegoat from being an investment professional; shame on our industry. Our marketing engines are no different than our politicians; they practice the KISS principal by selling to the lowest common denominator (low lowest IQ). Don’t think Clinton, Bush, or Obama speak to their peer group the same way they speak on TV. Do you think Bush keeps repeating to his son “Stay the coarse” when he gets in trouble or Obama constantly tells his wife “We need a change”. Wake up America and see you are being sold another faulty product from the marketing departments of the financial industry.
      Jul 16 02:14 PM
    • Plausible Negative Scenarios by Fund Type [view article]
      Richard, I enjoy your work. To address the fat-tail problem you can do two things, first, convert from measuring the variance of the distribution using standard deviation (in a normal distribution setting) to measuring the distribution's left-side probability of losing money using the concept of Value-at-Risk (VaR). Then replace VaR (based on normal distributions) what is known as CVaR or Expected Shortfall (based on a 'Stable' distribution). The core difference is that normal distributions are arithmetic and stable distributions are logarithmic.

      In a Normal Distribution environment, a security move from $1 to $2 equals a 100% gain, but a move from $2 to $3 is only a 50% increase, $3 to $4 is 33%, $4 to $5 is 25%, and so on, the ratio shrinks exponentially; this is why standard deviation moves the probability of return from 1 standard deviation (σ) at 68.3% to 2 σ at 95.4%, and 3 σ at 99.7%. This methodology suggests a 5 σ event will only occur once in 7000 years when it actually occurs every 3 to 4 years. The 87’ crash should have only occurred once in 3 lifetimes of the universe; normal distributions pervert reality.

      Stable distributions (logarithmic based) solve this anomaly because a data is properly managed. A security price change from $1 to $2 is a 100% gain, then from $2 to $4, $4 to $8, $8 to $16, all stay at 100% gain. If charted, it would look linear, not exponential.

      The second thing you can do is change the way you manage the data to make newer information more valuable; thus avoiding the monster flaws in mean variance. The simplest of choices are to use a rolling moving average or an Exponentially Weight Moving Average (EWMA); the more sophisticated method is GARCH (2002 Noble Laureate winning formula). Keep up the good work!
      Jul 14 12:46 PM
    • A Look at New Asset Allocation and Hedged Equity ETFs [view article]
      New asset allocation strategy? Don’t drink the punch! There is nothing new about this asset allocation strategy or methodology; nor is this quantitative based. First, Funds of ETF’s exist, there is nothing unique about an asset allocation fund of ETF’s. Second, New Frontier Advisors, the distribution arm of Nothfield, does not use a quant model to come up with their solution. It’s built on the same platform as all the other archaic asset allocation solutions designed in the 1950’s; what you call Mean Variance Optimization (MVO), Modern Portfolio Theory (MPT), and in some cases its updated versions called Black-Litterman Model (BLM) and Arbitrage Pricing Theory APT), such as you will find in the software offered by Zyphyr Associates.

      Third, the methodology of resampling was developed by Richard Michaud in 1999 and adds little, if any, value to a generic asset allocation model. It only serves to average efficient frontiers that are based on averages (maybe the fund should be called the average of averages). MVO-Resampling works in 3 steps like this:

      Step 1: a) Estimate returns, risk (using standard deviation) and dependency (linear correlation) for a set of assets using historical data. b) Run a Monte Carlo simulation to create a new data set. Calculate the return, risk and the dependency of the new data set.

      Step 2: Create an efficient frontier using the new inputs. (Repeat steps 2 and 3 500 times).

      Step 3: Calculate the average allocations to the assets for a set of predetermined return
      intervals. This is the new efficient frontier (Oh yes, slap a U.S. Patent #6,003,018 on this methodology to make it look new).

      In New Frontier’s whitepaper they highlight the ‘Fallacies of Mean-Variance’, yet they still rely on it. Why put a band-aid on a broken model? The REAL NEW STUFF is called Extreme Value Theory (EVT) and firms using this methodology have significantly out-performed your average of averages model. EVT is based on Noble Prize winning concepts from this millennium (2002), not from Markowitz’s work from 1952 & 59’ (yes, 50 years ago). Phillip Anderson, another recent Nobel Laureate in Physics, states “Much of the real world is controlled as much by the “tails” of distributions as by means or averages: by the exceptional, not the mean; by the catastrophe, not the steady drip.” “We need to free ourselves from “average” thinking”. In summary, it’s better than doing nothing…….in a rising market. These models suggest you will earn 10% a year based on 80 years of historical data; the average over that time frame. Note the domestic market is down for the past 10 years; so much for being average.
      Jul 13 08:24 PM
    • 3 Reasons For the Continuing Dollar Rally [view article]
      You take the cake for the most posts (negative posts). I'm sure the readers will accept your faulty reasons due to your youth (unless the picture is 20 years old). You need to understand that no Fed Chairman or President of the US can correct the wrongs that have accumulated over the past decades; nor can they change the enivitable shift in economic powers due to market forces (labor costs, resources, gov't control, etc). The lesson here is to ignore the lip service of politicians and talking-heads and do some real research/homework. That way if you get shot down you have some real ammo to fire back; and not rely on the opinions of others.

      In other words, we are fed up with opinions and white-noise filler articles; we seek sound reasoning from intelligent sources.       Jul 09 02:37 PM
    • Rally ETFs: Panic Selling Will Open Door [view article]
      Jim, sounds like good ole Kondradiff cycle theory; too bad more investors dont study market history and inflation cycles.

      I disagree with the second post, one has to accept the time frame of the writer. If he is a trader then he is looking for short-term entry/exit points in which the VIX and Put/Call ratio are valid tools; it's simply a matter of different strokes for different folks. I don't use those tools, but then again, I have a my own strategy using a variable time-frame.       Jul 09 02:24 PM
    • Diversification Can Be Everything [view article]
      Good stuff Jim. I don’t believe folks have issue with asset allocation; but rather its reliance on mean variance; note that your chart uses a rolling 36 month moving average. Had you used traditional linear correlation and regression you would have not identified the dramatic swings in correlation over time. In fact, if you had used shorter intervals your swings would have been much larger; especially in times of extreme events (as correlations move towards +1); as the saying goes: the only thing that goes up in a down market is correlation.

      Linear Correlation falls under the family of Dependency models. A more sophisticated dependency model that better represents a dynamic marketplace is a method called Copula Dependency; think of it as a dynamic correlation model that continually test the relationship between two securities.

      The advantage of using a copula dependency model is that it would identify the increasing volatility in the marketplace (in conjunction with a GARCH model) and recognize that correlations would be advancing during large market moves and invest accordingly. In other words, it would recognize that assets that are traditionally non-correlated may become highly correlated during extreme events and therefore opt to invest in short-term treasuries as an alternative.

      The mean-variance disciples use the laws of large numbers to forecast performance. Over the past 80 years the domestic equity market has returned 10% annually. Note that the market is down over the past 10 years (and 3 years, and 1 year, and YTD); just as it was from 1800 – 1815 (15), 1835 –1843 (8), 1852 – 1861 (9), 1880-1896 (16), 1907 – 1921 (14), 1930 – 1949 (19), 1968 – 1981 (13), 2000 – today (8). The down markets caused by deflation over the past 200 years lasted, in sequential order: 8, 16, 19, and so far 8 years. So mean-variance is like a clock that is right twice a day, even if broken. If an investor enters the market at the top of one of these long-term cycles it could easily take up to 30 to 40 years to break-even. Let mean-variance R.I.P. and take a look at Extreme Value Theory.
      Jul 09 11:13 AM
    • Tracking Mean Reversion After Bad Months [view article]
      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 -
      Jul 02 01:47 PM
    • The Problem With Designer ETFs [view article]
      You make an argument for market cap weighting but I question several of your assumptions: 1) that Wall Street hires the best minds, 2) market distributions are accurate, 3) and that market cap represents the market. I’ve met thousands of investment professionals and most follow the heard and few do their own homework. For example, look at the bulk of the asset allocation models that use off the shelf models, like Ibbotson. These models are ridiculously flawed; in fact the founder of CAPM, Bill Sharpe, even admits that. Just because everyone is making a bet that history will forecast the future (based on a regression to the mean) doesn’t make it right. This leads to point two, most distributions fall under the Normal Distribution category which by default ignores the fat-tails. But more to your point why is following the heard a good thing? Cap-weighting is a form of market-timing because it is momentum based, like it or not. Someone is over-paying for an asset because you have more buyers chasing an asset as the price is rising. Regardless of what you believe Market Cap is a strategy and who is to say which is right? I think Rob Arnott has more than proven his point. You discuss the statistical distribution of money but you are only looking at the effect and not the cause. Much more insight can be garnered by analyzing the distribution of institutional (block) vs. non-block trading volume. Doing so you soon realize you can have more buyers than sellers and yet the price can still fall if some of those sellers are big block institutions. Watch how often the big boys are selling into the strength of the smaller volume buyers; not a pretty sight. In other words, it’s the human judgment that you endorse (perhaps based off of investor sentiment) that leads to the over-bought or over-sold conditions. I have the most trouble with your comment ‘You can’t outsmart the market by basing your distribution of money between stocks on a rigid computer analysis of part of the data.’ I totally disagree; tell this to Simons, Tudor, Robertson or most of the quant hedge fund mangers. I build models that have out-performed for decades. Maybe you should have added: ‘…using traditional models like Modern Portfolio Theory’ or something to that sort. If it doesn’t make sense to you feel free to contact me and I’ll demonstrably spell it out. More confusing to me is that you defend market cap and then later in your article start making a case for a secondary weighting to adjust for human behavior (market sentiment); isn’t this contrary to your point? Market Cap is not a function of democracy; it is a greater fool theory. If it is a democracy it is one like most countries where a few fat cats pull the strings behind the curtains and influence the tide (institutional block money flow, or worse (if you’re into the conspiracy theorist thing)). My suggestion is to not drink the random-walk cool-aid. Maybe a nice book by Benoit Mandlebrot or Nassim Taleb or anyone else that can live outside the traditionalist will change your mind. Cheers. Jun 22 01:54 PM
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