Smart ETF

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    • Mon Apr 14th 01:47 AM | Rating: 0 0
      Commented on:
      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!
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    • Thu Apr 3rd 13:27 PM | Rating: 0 0
      Commented on:
      Relative Returns By Equity Asset Class
      Thank you for sharing; always nice to see performance from a multiple time-frame perspective!
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    • Fri Mar 28th 11:29 AM | Rating: 0 0
      Commented on:
      Bear's Active Bond ETF Defies Market Turbulence
      Good reporting, facts with no fluff on a timely topic. Thanks
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    • Tue Mar 11th 11:45 AM | Rating: 0 0
      Commented on:
      A Quant Approach to TAA: Winning by Not Losing
      I have no idea how a 20% mix allocation per asset class can be constured as Quantitative but the results are compelling. I will read the link to the whitepaper.
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    • Mon Mar 10th 13:29 PM | Rating: 0 0
      Commented on:
      Bill Schultheis: Simple Portfolio Design is Best
      The 60/40 mix was the optimal mix in only one decade of the past 50 years. Although I agree there is much worse things one can do than diversify among the major asset classes, I am always amazed that investment professionals insist on using the KISS principal to invest. If investing were so simple everyone would be making money. Wake up Wall Street and use a scientific approach!
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    • Fri Mar 7th 12:16 PM | Rating: 0 0
      Commented on:
      Talking Investment Principles and ETF Strategies with Richard Kang
      An intelligent Life form! Thank you for sharing your thoughts. I enjoyed the article, even though I disagree with the SPIVA Report Card that active managers under-perform. A study called the Active 50 out of Yale removed the closet indexers from the list of funds and found the remaining active managers actually outperformed (over the 20 year period).

      I share your outlook on the future of ETFs and appreciated your description of the Beta/Alpha continuum. Keep writing!
      View article »
    • Mon Feb 11th 14:11 PM | Rating: 0 0
      Commented on:
      Rebalancing Can Be Hazardous to Your Portfolio
      Thank you for your response. I meant to ask you to run an MVO model during the 60’s and 70’s, not since the 50’s. Running MVO since the 50’s gives you the positive returns of the 80’s & 90’s.

      The point I was stressing on Monte-Carlo was its ineffectiveness in combination with MVO models. I am a big believer in Monte-Carlo in Extreme Value Theory models; thus recommending 100,000 simulations over 10,000.

      You are correct there is a big difference between Fama’s 3 & 4 factor models. My main point is there are many asset allocation solutions, such as MPT (MVO, CAPM, I-CAPM, C-CAPM), Arbitrage Pricing Theory (MacroAPT, Multiple APT, Black-Litterman), and Extreme Value Theory (Dynamic Portfolio Optimization), and the type solution determines the effectiveness of rebalancing. MPT models are completely static, APT models are semi-static (tilting MVO models to favor market conditions), whereas EVT models are completely dynamic (letting market conditions predict results). I agree that rebalancing an MVO model is wrong (but I argue MVO is wrong; even its founders are admitting its flawed). I would say that rebalancing an APT model is a good thing and rebalancing an EVT model is a mandatory event.

      The second point I’m stressing is that we need to scrub all discussion on MVO models and cease the perpetuation of this myth. Sharpe & Mandelbrot state that normal distributions miscalculate risk, whereas, stable distributions allow for fat-tails that scale. Sharpe quotes CAPM is ready for a make-over; that MVO assumes all investors have the same beliefs about the market and the relationship among different assets. Mandelbrot pretty much trashes all of it. BTW, Mandelbrot was Fama’s PhD advisor!

      All I ask is that we up our discussions to new methodologies and quit making headlines that represent all asset allocation models.
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    • Sun Feb 10th 20:24 PM | Rating: 0 0
      Commented on:
      Rebalancing Can Be Hazardous to Your Portfolio
      You are so right – when using an academic model from 1960. However, we are not in Kansas anymore. You fool yourself by believing in an out-dated model known as Modern Portfolio Theory (MPT) and by using Mean Variance Optimization (MVO) to provide your asset allocation and rebalance solution; circa 1952 & 1959.

      For starts, show me one security or portfolio that has a normal distribution; yet this is what you are using in your asset allocation model; therefore your risk measurement is fatally wrong from the start.

      Next, you consistently refer to the 60/40mix; I’ll bet you can’t tell me where the 60/40 mix originates! I’ll bet none of your MVO brethren can tell me where the 60/40 mix comes from……just what I thought. It came from 10 years of historical data during the 1950’s; the time period known as the Nifty Fifties (the biggest Bull market prior to the recent decade). Try running your 60/40 mixes for the 20 years after the 50’s (60’s and 70’s) and tell me if any of your clients survived. The answer is no because no combination of stocks or bonds made positive returns for that 20 year period. MVO used past performance to predict future returns, and lost.

      In your fantasy world of long-term ‘mean-variance’ you are correct to assume rebalancing is a waste of transaction costs and most likely inefficient. But what happens when you add 3 months of new data to your model; a model that is built on decades of data (let’s guess 40 years))? Absolutely nothing! The 3 months of new data gets averaged out over the 40 years so it has no effect on the portfolio. In other words, your mean-variance model ignores the last three months of this market decline because it really doesn’t matter in your world (but it does in mine!).

      Next, what good is Monte-Carlo modeling on a mean variance model? It provides little if any assistance because the recommendation always tracks to a mean. Now try it using a M-C simulation model on a dynamic asset allocation solution, like Mandelbrot’s Extreme Value Theory, and you will get vastly superior outcomes. BTW, you will want to run 100,000 simulations once you upgrade to a more sophisticated model.

      Lastly, you refer to a seven asset class portfolio as complex; a seven class portfolio is ANYTHING but complex; maybe for my Blackberry, but not any meaningful asset allocation solution.

      I have patently read your articles but it is time you move on in your learning curve. I recommend you read Benoit Mandelbrot’s ‘The (Mis) Behavior of Markets. Welcome to the brave new world of portfolio optimization where rebalancing does matter!
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    • Wed Feb 6th 13:34 PM | Rating: 0 0
      Commented on:
      PowerShares' Active ETF Launch Gets SEC Green Light
      It’s hard to determine which rebuttle against Active ETF’s is more ridiculous: that Active ETF funds could be “more risky and more expensive than some low-cost, low-turnover actively managed mutual funds" or that “many observers remain skeptical that the new crop will significantly boost returns compared to market-cap size index funds”

      Who sketched in stone ETF’s had to be low cost, low turnover or less risky? The risk of ETF’s have increased by default. The first wave of ETF’s were built on broad based indices like the S&P 500. The second wave was the sectors like financials, health care and basic materials. Since sectors are less diversified than a broad based index the risk in sector ETFs increased from 1.3 to more than 8 times the volatility of the S&P 500 index (not to mention the slippage in bid/offer spreads and tracking error). The third wave of ETF’s shot risk to the moon with the introduction of sub-sectors (such as biotech), inverse funds and leveraged funds.

      It’s not until the introduction of this latest wave of ETFs do we see the risk subside with the introduction of fixed income, commodity, and hedge fund ETF’s. Not are all are less risky, such as commodities, but they do allow for less than positive or even negative correlation that further reduces portfolio risk in an asset allocation model. Add to this the new wave of high-tech asset allocation models, such as Extreme Value Theory (EVT) and you see significant improvements in risk-adjusted returns over the cap-weighted indices.

      The Market-cap issue is the great façade. In December 2002, the small cap index was comprised of 70% value stocks and 30% growth. Exactly three years later the ratio reverse with only 20% of the index value stocks and 80% growth. How could a value manager or small cap value index possibly beat the small-cap index during these three years? This explains why the fundamentally weighted indices under-performed the cap-weighted indices last year. Market-cap weighting is a market timing index by default! This is why it’s hard to beat. The irony is the buy & hold camp is holding a market timing instrument; now that is humor.

      Furthermore, if you strip out the index funds and closet index managers you will find that active managers soundly beat the indices over time. A study out of Yale, called the ‘Active 50’ by Cremers & Petajisto introduces the Active Share ratio. The Active Share ratio follows a scientific method to demonstrate that true active managers out-perform. Read it and weep proponents of the buy & hold.

      The best part of issuing active ETF’s is it will remove the strangle hold on investors from the latest fad of early redemption penalties imposed by the mutual funds. An active ETF is the killer app against these unwarranted restrictions and penalties. What happened to the fight for fairness, transparency and liquidity?
      View article »
    • Wed Feb 6th 12:51 PM | Rating: 0 0
      Commented on:
      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.
      View article »
    • Mon Feb 4th 17:04 PM | Rating: 0 0
      Commented on:
      Fundamentally Weighted ETFs: Mixed Performance in '07
      I think it is time to see through the veil of what a cap-weighted index really is……a glorified momentum index. WAIT! That is comment is sacrilegious! Defend yourself!
      Okay, one only needs to look at the sector mix of an index to see the change in market weight caused by the momentum effect. As an example, In December of 2002 the E-Trade Russell 2000 Index Fund composition was approximately 70% small-cap value/ 30% small–cap growth. Three years later (December 2006) the index was approximately 20% small-cap value/ 80% small–cap growth. Imagine trying to beat the small-cap index as a small-cap value manager during those three years!

      Let’s take it a step further; pretend you were a small-cap growth manager during this booming three year run. Your track record looks good as you capitalized on this momentum and you soundly beat the small-cap index. On the wings of good fortune you get hired by the institutions and investors. Then the enviable happens, the sector rotates back to small-cap value (and/or some other asset class) and your performance drops and you fall out of favor.

      In this example its evident indexing small-cap stocks using a cap-weighted approach capitalizes on the change in momentum while fundamental indexing would have given a more accurate description of how the small-cap securities actually performed.

      I don’t have enough data points to judge weather fundamental indexing is better or worse than cap-weighting indexing but I do believe the momentum effect may favor cap-weighting, albeit with more volatility. So the trade-off of risk-adjusted returns is open for debate. What is clear to me is that cap-weighting is nothing more than a momentum strategy masked in the guise of a passive strategy. I’m sure if this were a chat log I’d burn in flames! I applaud Rob Arnott’s work on fundamental indexing and appreciate anyone challenging the norms of convention wisdom.
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    • Wed Jan 30th 15:24 PM | Rating: 0 0
      Commented on:
      SocGen and the Perception of Risk
      As you touch in the last sentence the root of the problem is in measuring/understandin... risk. The problem is deeper than the realizing most traders don't know Value-at-Risk (VaR) or Standard Deviation (σ); it didn't seem to help LTCM. The problem stems from the foundation of risk measurement, Normal Distributions. All three popular risk modeling techniques (VaR, Standard Deviation, Semi-Variance) are all measuring risk using a normal distribution (arithmetic calculation); this is the flaw (should be a Stable Distribution which is logarithmic).

      Using a normal distribution the probability of return at 1σ is 68.3%, 2σ is 95.4% and 3σ is 99.7%; so what is 5σ? The odds of a 5 sigma event are one in 7000 years! In reality they occur every 3-4 years on average and we have had many outliers outside the sacred 3σ level many times in the past 12 months. Furthermore, a 22σ event like the 87’ crash should occur once in three lifetimes of the universe. Normal distributions are the staple at all the major financial and educational institutions and professional associations like PRMIA, GARP, CFP, FPA and IMCA.

      Had these managers used Stable Distributions they could have seen the actual risk more accurately; especially in conjunction with Student-t and through more sophisticated data management techniques such as GARCH (Generalized Auto-Regressive Conditional Heteroscedasticity).

      It’s time for risk managers and money managers to open there ears, the oracles of these original models and techniques are claiming its time to change. William Sharpe states “the CAPM Theory is ready for a makeover”, “tail-risk is ignored by Mean-Variance Analysis”, and “the new approach doesn’t rely on a normal distribution”. Benoit Mandelbrot, co-founder of Chaos Theory and creator of Fractal Geometry, combines the best of breed mathematics to create Extreme Value Theory; a substantial upgrade in risk measurement and portfolio optimization. Maybe its time to revamp what we teach and preach!
      View article »
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