Smart ETF

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    • Wed Jul 9th 14:37 PM | Rating: 0 0
      Commented on:
      3 Reasons For the Continuing Dollar Rally
      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.
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    • Wed Jul 9th 14:24 PM | Rating: 0 0
      Commented on:
      Rally ETFs: Panic Selling Will Open Door
      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.
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    • Wed Jul 9th 11:13 AM | Rating: 0 0
      Commented on:
      Diversification Can Be Everything
      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.
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    • Wed Jul 2nd 13:47 PM | Rating: 0 0
      Commented on:
      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 -
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    • Sun Jun 22nd 13:54 PM | Rating: 0 0
      Commented on:
      The Problem With Designer ETFs
      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.
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    • Fri Jun 20th 19:07 PM | Rating: 0 0
      Commented on:
      An All-ETF Hedge Fund? You've Got to Be Kidding
      Now Jimmy boy, I see your point and at the surface it does sound and look ridiculous; especially after the performance fee. But just to shed light on a different perspective, most ETF models are passive and a hedge fund clearly is not; thus the opportunity to out-perform. There is a new breed of physics-based models, like Extreme Value Theory (put that in Amazon or Google Scholar and check out the math). You will find newer methods to manage risk and the time-series of data. These advanced models can be used in both asset allocation models and technical analysis. Add this to the growing number of short funds and alternative ETFs and you can brew up quite a mix of out-performing assets. The downside is the lack of volume in most ETFs and as you know this creates huge risk. In addition, the bigger volume ETFs are associated with the major market indices, which are more efficiently priced; not the sort of investment most active managers seek. My concern is that this fund will be constrained by the volume associated with the more inefficiently priced ETFs. Regardless, I applaud your brazen opinion, a stance most investment professionals are challenged to attempt.
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    • Thu Jun 19th 22:48 PM | Rating: 0 0
      Commented on:
      Kenneth French Disdains Active Management
      An exhaustive and detailed study was released in 2007 by two Yale professors who prove active managers do add significant alpha after fees and expenses. They came up with a way to track active managers, called the Active Ratio, just as Tracking Error measures an index funds performance. Once you remove the index funds and closet indexers from the fund universe you can truly measure the ‘real’ active manager’s performance. Unless French can empirically prove index managers and closet index managers are scrubbed from his data set then I’m suspect of his findings; Garbage In Garbage Out. In other words, the addition of index and closet index managers to the overall manager universe make his findings a self fulfilling prophecy.
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    • Tue Jun 17th 14:25 PM | Rating: 0 0
      Commented on:
      Defining ETF Risk: Does It Pass the "Smell" Test?
      Matt Hougan wrote about this very thing on June 8th and lists the exact same information plus 'tax risk' and 'counterparty risk' (in the case of commodity and leverage funds). I think you are missing the two most important risks: 'Expected Shortfall risk' and 'volatility risk'.
      Expected Shortfall is the extra (fat-tail) loss that is ignored using a normal distribution. By converting to a 'Stable' (logarithmic) distribution you can actually see the true risk of a frequency distribution. In other words, it is a Value-at-Risk (VaR) model that better describes the tails of a distribution. With VaR, with may think you stand to lose 3% of the portfolio value on a given day, one percent of the time (at a 99% VaR). With conditional expected shortfall (or conditional VaR) the actual loss 1% of the time may actually be 6%; like what happened this past February.
      Volatility Risk is the extra risk you assume by investing in less diversified asset classes. This is a big deal with ETFs. The cause of this problem stems from the sudden interest in ETFs and the need for ETF manufacturers to gobble up their stake in the ETF real-estate game. As the land-grab for ETF shelf space continues so does the increase in volatility. The first ETFs were broad-based market indices, like the S&P 500. The next wave of ETFs was the industry sectors (health care, financials, basic materials, etc.). Because they are less diversified the risk on one industry, in terms of volatility (measured in standard deviation) is 1.3 to 8.6 times the volatility of the S&P 500. Having seized the industry sector space the ETF manufacturers went to the sub-sector frontier to build their niche (such as bio-tech); and henceforth more risk. Not to be out done, competing manufactures launched inverse funds and leveraged funds; again, more risk. Only since June of last year has the risk in new ETF’s subsided with the introduction of fixed income, real estate and some commodity ETF’s. The largest risk in managing a portfolio of ETF’s is in choosing the proper fund universe; then comes the ardent task of fundamental research and asset allocation.

      Expected Shortfall is the extra (fat-tail) loss that is ignored using a normal distribution. By converting to a 'Stable' (logrithmic) distribution you can actually see the ture risk of a frequency distribution. In other words, it is a Value-at-Risk (VaR) model that better describes the tails of a distribution. With VaR, with may think you stand to lose 3% of the portfolio value on a given day, one percent of the time (at a 99% VaR). With conditional expected shortfall (or conditional VaR) the actual loss 1% of the time may actually be 6%; like what happened this past February.

      Volatility Risk is the extra risk you assume by investing in less diversified asset classes. This is a big deal with ETFs. The cause of this problem stems from the sudden interest in ETFs and the need for ETF manufacturers to gobble up their stake in the ETF real-estate game.
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    • Mon Jun 16th 14:55 PM | Rating: 0 0
      Commented on:
      William Koehler Takes the Big Leap Into ETF Portfolio Management
      A man with historical facts and market knowledge; I thought they were extinct. Kudos for sharing and best wishes with your new fund -
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    • Mon Jun 9th 18:37 PM | Rating: 0 0
      Commented on:
      ETF Investment Risks
      An overlooked metric is volatility risk. As the land-grab for ETF shelf space continues so does the increase in volatility. The first ETFs were broad-based market indices, like the S&P 500. The next wave of ETFs was the industry sectors (health care, financials, basic materials, etc.). Because they are less diversified the risk on one industry, in terms of volatility (measured in standard deviation) is 1.3 to 8.6 times the volatility of the S&P 500. Having seized the industry sector space the ETF manufacturers went to the sub-sector frontier to build their niche (such as bio-tech); and henceforth more risk. Not to be out done, competing manufactures launched inverse funds and leveraged funds; again, more risk. Only since June of last year has the risk in new ETF’s subsided with the introduction of fixed income, real estate and some commodity ETF’s. The largest risk in managing a portfolio of ETF’s is in choosing the proper fund universe; then comes the ardent task of fundamental research and asset allocation.
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    • Fri May 23rd 14:33 PM | Rating: 0 0
      Commented on:
      The ETF-Squared: It Reallocates For You
      Wake up world, yes rebalancing frequently is inefficient (IN YOUR MODELS) using mean-variance models such as Modern Portfolio Theory (circa 1950’s) and Arbitrage Pricing Theory (circa 1970’s), ala Black-Litterman. How can one year of new data possibly effect a change on more than 50 years of data? The real questions is who would want to rely on the average price, risk, return and correlation of a security or asset class based on the average of 40 or more years? Can you name one company whose 40+ year historical average is currently performing the same? Why do you think Fama, Mandelbrot and now Sharpe have all debunked MPT? Only Markowitz is holding onto his dream.

      I find it hard to believe people still put stock in these theoretical models, but then again you also believe in Normal Distributions. May I suggest you read The (Mis) Behavior of Markets by Mandelbrot or any of Taleb’s books.
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    • Tue May 13th 16:00 PM | Rating: 0 0
      Commented on:
      Asset Allocation as a Method for Risk Management
      This is an excellent illustration of the merits of diversification. Thanks for sharing -
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    • Mon Apr 28th 13:39 PM | Rating: 0 0
      Commented on:
      Talking Fixed Income Investing with Ron Ryan
      I'm an ex-Drexel guy and I'm continually impressed with Ron’s research. Some of his most important work include: 1) Defining the massive inaccuracy in the Ibbotson Long-Term bond index (that most of Wall Street still has tacked up on their cubicles), 2) Demonstrating that most equity indices have underperformed the risk-free rate of return (using a comparable time-adjusted risk free rate such as a 20 year zero coupon Treasury), and 3) that investing, for the most part, should be managing assets to liabilities, not performance (in other words, manage risk to the date the monies needed). Bravo Ron and thank you Heather for bringing some original thinking and brainpower to print.
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    • Mon Apr 28th 13:08 PM | Rating: 0 0
      Commented on:
      What Is High Implied Volatility?
      I agree with the most recent posts that IV is problematic. The first problem is: defining what is the best time frame of data to analyze. The second problem (not necessarily pertaining to options) is determining how best to weight the data, and the third is how best to forecast the data. Because there is no right answer, we have developed a solution using GARCH models to weight the data using ‘stable’ distributions (not ‘normal’) and then run a Monte-Carlo model (simulation) for forecasting. This more accurately describes historical volatility, negates the need for IV, and replaces theoretical volatility by forecasting volatility. It works well for us, perhaps it will help one of you -
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    • Tue Apr 15th 21:41 PM | Rating: 0 0
      Commented on:
      Momentum Model: Timeless Alpha Rooted in Human Psychology
      Good Stuff, but the question yet solved is what is the optimal time series for analysis?

      We know that Fama proved that using 3 or more years of performance data actually increased the probability of underperforming and that short-term data (monthly returns) increased the probability of over-performing; our research suggests this is mainly due to both the momentum effect and style drift.

      A more scientific method to manage the time series is to examine the clustering of data using Generalized Auto-Regressive Conditional Heteroskedacity (GARCH). Give GARCH a go if you are looking for more accuracy -
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