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  • Adaptive Asset Allocation For Lazy Portfolios 22 comments
    Jul 30, 2012 2:48 AM | about stocks: SPY, EFA, TLT, VNQ, IWB, DBC

    Selection of a few of the ETFs from a basket on the basis of prior performance, together with allocation on the basis of risk parity, may improve the performance of some of the so-called lazy portfolios (see, e,g., seekingalpha.com/article/27167-lazy-etf-portfolios-inspired-by-the-gurus ).

    We consider the following portfolios (we note that we have used TLT for the bond components of the portfolios, and this may not conform exactly to the original recommendations).

    PortfolioComponentsWeights
    IvySPY, EFA, DBC, VNQ, TLTEqually weighted
    SwensenSPY, EFA, VNQ, TLT30%, 20%, 20%, 30%
    BernsteinSPY, IWB, EFA, TLTEqually weighted

    The performance of each of these portfolios starting in the year when all the component ETFs were first available is shown in the following table (the last column lists the minimum of the annual returns during the period).

    PortfolioPeriodCAGR

    Min

    Ivy2007-20124.3%-22%
    Swensen2005-20127.4%-16%
    Bernstein2003-20128.6%-20%

    For adaptive asset allocation we update the portfolio every two months as follows:

    1. Find the two top ETFs whose performance was the best during the prior three months.

    2. If any of the two top ETFs performed worse than TLT, replace it by TLT.

    3. Find the weights of the two top ETFs using risk parity and the daily return history of the ETFs during the prior quarter.

    Note that for only two assets the risk parity problem is quite easy to solve: the weights of the assets are inversely proportional to the standard deviation of the daily returns during the prior quarter.

    The performance of this strategy applied to the lazy portfolios is shown in the table below.

    PortfolioPeriodCAGR

    Min

    Ivy2007-201217.6%-0.77%
    Swensen2005-201214.1%1.3%
    Bernstein2003-201213.1%-3.3%

    The annual returns of these portfolios during the recent years are shown in the following table.

    PortfolioYTD1 Year3 Years5 Years
    Ivy14.4%31.9%18.5%18.9%
    Swensen14.4%31.9%20.4%14.3%
    Bernstein11.6%37.1%18.8%13.5%

    It is quite noteworthy that the Morningstar Fund Selector (screen.morningstar.com/FundSelector.html) does not yield a single mutual fund that beats all of these four recent returns of any of these lazy portfolios. Surprisingly, none of the portfolios at Foliovesting (www.folioinvesting.com/rtg/ready-to-go-folios-list.jsp ) can beat all of the metrics of any of these lazy portfolios with adaptive asset allocation.

    Clearly, there is a slight improvement in the performance at the cost of twelve round-trip trades per year. Of course for taxable accounts there will be additional considerations.

    Disclosure: I am long VNQ.

    Themes: Adaptive Asset Allocation Stocks: SPY, EFA, TLT, VNQ, IWB, DBC
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Comments (22)
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  • Very interesting!
    30 Jul 2012, 12:49 PM Reply Like
  • thank you varan. finally you have a new update.
    If I may speculate a bit, you do have spreadsheet available online to show the back-testing. If you do, can you please show us the link?
    30 Jul 2012, 11:19 PM Reply Like
  • Also, Varan ...Faber's "Endowment" allocation had the best performance. How might your strategy impact that allocation? Thanks, again.
    14 Aug 2012, 01:56 AM Reply Like
  • Varan, this looks smart. I would second that request for a link to a googledoc spreadsheet. And I'm not clear on point 3, above, regarding weighting. You wrote, " 3. Find the weights of the two top ETFs using risk parity and the daily return history of the ETFs during the prior quarter." Would you mind being a little more clear on that? Grateful in advance. Stephen
    14 Aug 2012, 02:04 AM Reply Like
  • Author’s reply » @harlin

     

    1. I am using only the Endowment ETFs, and not his allocation. Allocation is determined by risk parity - see below.

     

    2. Compute the standard deviation of the prior quarter's daily returns of each of the top ETFs . If the standard deviation of the first ETF is A, and the second one is B, then the weight of the first ETF will be B/(A+B), and the second one will be A/(A+B). This is derived from the fact that for two assets, risk parity is achieved by setting the weights as inversely proportional to standard deviations - you can write the two weights as [AB/(A+B)](1/A) and [AB/(A+B)](1/B).
    14 Aug 2012, 09:17 AM Reply Like
  • Great white paper on the "How To" and why for Adaptive Asset Allocation here:

     

    http://bit.ly/O62vYy
    14 Aug 2012, 09:22 AM Reply Like
  • Your reply much appreciated.
    14 Aug 2012, 09:30 AM Reply Like
  • Varan,

     

    I've now reviewed all of Butler and Philbrick's work (white paper and published research) on Adaptive Asset Allocation and fully agree with their perspective on risk parity.

     

    Briefly, their "Darwin" Adaptive Asset Allocation is summarized by:

     

    The investment universe is comprised of the 10 major global asset classes. These include:

     

    Barclays 20+ Year Treasury Index
    Barclays 7-10 Year Treasury Index
    DB Liquid Commodities Index
    Dow Jones Global Real Estate Index
    Dow Jones U.S. Real Estate Index
    Gold
    MSCI Emerging Markets Index
    MSCI Japan Index
    S&P 500 U.S. Stock Mkt Index
    S&P Europe 350 Index

     

    At the end of every month, each asset class is ranked for near-term trailing returns. The portfolio only invests in the two top-ranked assets (equities and bonds) that month. (Five percent of net equity is held in cash or cash equivalents.)

     

    Allocation among equities and bonds is then derived from a minimum variance algorithm.

     

    Portfolio level volatility is managed by reducing position size when volatility exceeds a predetermined risk level. In addition, allocations are reduced in assets exhibiting erratic behavior while increasing allocations to assets exhibiting more predictable behavior.

     

    The portfolio is rebalanced monthly following near-term performance profiles.

     

    The authors' white paper show CAGR values significantly higher than the three gurus.

     

    - - - - - - - - -

     

    I am able to duplicate their core strategy's allocations, using a minimum variance algorithm, and I calculate expected returns which are quite close to their (published) actual returns. The strategy has many attributes, but frankly, I believe it could be improved a little bit.

     

    Notwithstanding, Butler and Philbrick's work and performance measures are the nicest I've seen using Adaptive Asset Allocation models.
    15 Aug 2012, 11:35 AM Reply Like
  • Author’s reply » This is not wholly surprising, since the universe of assets is larger and more comprehensive. My purpose was just to show that minor modifications can improve the performance of these portfolios. Similar improvements occur when you use this method for the lazy portfolios maintained at marketwatch.com which consist mostly of vanguard mutual funds and have been around for longer periods. I don't know about the transaction costs at vanguard, but it may well be that in tax free accounts one can get the performance improvement essentially for free with those portfolios. I will try to replicate my analysis for the Darwin portfolio, as I suspect that there might be room for some improvement by changing some parameters.

     

    Your use of minimum variance in the context of risk parity is quite intriguing. Generally one expects that in contradistinction to risk parity, the classical minimization of variance will lead to weights that may not include all the top assets unless you impose some additional constraints.

     

    Thanks a lot for your interest.
    15 Aug 2012, 12:18 PM Reply Like
  • Author’s reply » I have done an experiment with the portfolio of IEF, TLT, DBC, RWO, IYR,EEM, GLD, EWJ, SPY, and IEV.

     

    If you update the portfolio every 3 months by buying the top three of the month prior to the update, and compute the weights on the basis of standard deviations of the returns during the prior month, you get a CAGR of 17.9% during 2004-2011.
    17 Aug 2012, 01:33 AM Reply Like
  • Cool. Butler and Philbrick are publishing CAGRs in the 15 - 16% range, rebalancing monthly. IMHO, their principals are spot on; the strategy can be improved, however.

     

    For example, there are instruments which behave in bond-like fashion (in terms of mitigating risk), have negative correlations to equities, low realized volatilities, high liquidity - but are optionable and pay respectable dividends. Utilities are one. Volatility as an asset class is another.

     

    By pairing the equity side with optionable (but bond-like) instruments, one could increase income writing covered calls, further reduce risk with (costless) collars, initiate positions via writing puts, capture income with weekly option plays on volatility mispricing...etc.

     

    I'm currently working on models which take the two asset portfolio approach I described above ...use the minimum variance algo and the principals espoused by Butler and Philbrick ...but improve on what they call the "Portfolio Universe." Also, they are "anti-precise" and scoff at market timing for entry and exit.

     

    Timing has been shown to: 1) outperform (anti-precise models) 50% of the time, 2) improve risk-adjusted returns 66% of the time, and 3) reduce drawdowns 95% of the time by 50% (Faber, et. al.), 4) keep the investor invested 70% of the time, holding losses 15% of the time long, and 5) keep losers smaller than winners (M. Faber, et. al.).

     

    So I think that by ...keeping Butler and Philbrick's principals involving momentum, volatility, and correlation ...but widening the portfolio universe and systematically timing entries/exits ...one might improve on their already sound adaptive asset allocation model.

     

    (I have coded a calculator to obtain relative weights for a two asset allocation, embedding the minimum variance algo, and can simultaneously calculate portfolio volatility and expected returns.)

     

    One last note: Butler and Philbrick use IEF/TLT to mitigate equity risk. Between the two, IEF functions far better than TLT. During the last year, there were only 72 trading days when you'd be glad you held TLT, based on correlation and strength. In other words, TLT is usually a drag on performance.
    17 Aug 2012, 11:48 AM Reply Like
  • Varan, by any chance do you still have the spreadsheet that does these calculations. My intent is to use your spreadsheet and following same steps/calculations, do it in R. I would be happy to share the R program later with your readers. Regards.
    17 Aug 2012, 05:03 PM Reply Like
  • Hi Steph,

     

    The paper you linked above only says top half by momentum. Where is the top 2-ranked assets part? Can you please share the link to the white paper?
    25 Aug 2012, 01:45 AM Reply Like
  • But RWO and DBC dont go back to 2004. So, from 2008 on, I get ~6%. Whats up?
    28 Aug 2012, 04:20 PM Reply Like
  • Author’s reply » DBC starts in 2007, and RWO in 2009.

     

    During 2007-2012 I get 13% CAGR. 2009-2012 yields 14%. But for both, much of the returns comes from the 32% of 2009. More specifically;

     

    2007 34.2%
    2008 9.3%
    2009 32.2%
    2010 9.9%
    2011 7.1%
    2012 4.3%
    28 Aug 2012, 04:44 PM Reply Like
  • So your basket does not contain RWO in 2008, 2009 and you add that in along the way?

     

    No wonder my results havent been matching...
    29 Aug 2012, 03:36 PM Reply Like
  • Author’s reply » yes.

     

    Whenever it is available it is used, otherwise not.

     

    thanks.
    29 Aug 2012, 04:18 PM Reply Like
  • Interesting strategy and comments. Thanks. In recent years pair stocks and bonds have low correlation. On other hand in 90's I think bonds and stocks have much higher correlation. Another example would be current high correlation scenario in Spain between bonds and stocks. So it might be useful to have correlation level as one of the factors.

     

    Adaptive asset allocation is an interesting paper. I tried to recreate but couldn't get similar results. So currently searching to see if there is an excell/R implementation on net. I agree with their perspective on risk parity. On other hand I think performance cost associated with volatility targetting as described in paper might be more useful to planners then individual investors. Reason being for financial planners/fund managers, the clients treat the amount they see in month end statement as cash in bank even if though it is likely an open equity. So for planners/managers it is important that open equity curve is smooth even if it costs little bit on performance side. On other hand, someone investing their own money likely be ok with ignoring open equity and use closed equity as cash in bank.

     

    Regards,
    Contact-(http://bit.ly/OCZqow)
    17 Aug 2012, 12:18 AM Reply Like
  • UBS just rebalanced their global asset allocation recommendations, increasing weights in equities and bonds:

     

    45.1% Equities
    27% Government bonds
    11.6% Corporate bonds
    7.3% Commodities
    5% Real estate
    1.25% Inflation-indexed bonds
    1.75% Cash

     

    Looks benchmark.
    17 Aug 2012, 06:55 PM Reply Like
  • Varan
    Do you use ETF Replay to do the analyses you do or something else?
    22 Apr 2013, 01:14 AM Reply Like
  • Varan
    In your comments made about 15 Feb 2012 (not sure what site I found this on),
    "For this basket, a simple relative strength strategy works very well. If you buy the top two (among SPY, GLD, TLT and SHY) on the basis of the returns of the previous three months, and hold for three months and repeat, you get the annualized (2005-2012) return of over 16% with no year of loss, maximum 5% drawdown, a Sharpe ratio of over 2, and the three factor alpha of 14%. Pretty good. Much better than buying and holding, and probably also superior to the 10 month SMA approach, as you have no more than eight round trip trades a year."

     

    What platform did you use in arriving at these back-test results ?
    ETF Replay or something else?
    22 Apr 2013, 01:27 AM Reply Like
  • Author’s reply » Thanks.

     

    I use a platform that I have developed.

     

    For the backtest you mentioned, here are the results:

     

    2005 16.80%
    2006 11.81%
    2007 17.24%
    2008 13.97%
    2009 17.78%
    2010 16.05%
    2011 25.46%

     

    CAGR 16.95%

     

    NET 2.99

     

    For 2012, the result was not that good. 2013 is still current and we shall see.

     

    2012 -2.82%
    2013 3.55%
    22 Apr 2013, 01:44 AM Reply Like
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