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  • Sell In May: It Ain't Over 'Til Its October [View article]
    See the following link for a good discussion of how sell in may doesn't work as an actionable strategy after accounting for market frictions.
    May 31, 2013. 01:00 PM | Likes Like |Link to Comment
  • Sell In May: It Ain't Over 'Til Its October [View article]
    I stand by my position. In my view, a market anomaly is only significant for practical purposes if it can be traded on profitably. You have a chart from CXO Advisory in your post that illustrates that the "sell in may" phenomenon systematically underforms a buy and hold approach. In fact, the sell in may approach only outperforms in one decade (the 1930's) going all the way back to the 1870's. Is an anomaly really significant if it provides little to no value in real world applications?
    May 29, 2013. 02:58 PM | Likes Like |Link to Comment
  • Sell In May: It Ain't Over 'Til Its October [View article]
    This type of analysis can be dangerous when it is performed without any probabilistic context. Using data on SPY, one can quickly find that roughly 60% of months have a positive price return while 40% of months have a negative price return. With this information, we can evaluate whether the statistical significance of the "win rate" is statistically significant or if it could have occurred solely do to chance.

    In this light, the 63.6% win rate of May is not really anything to pay particular attention to. In fact, there is a better than 30% chance of a month having a win rate higher than ~63% just by chance. Similarly, the "troubled" month of June, with a win rate of 51.5% is hardly troubled at all as there is a 25% probability of having a lower win rate again just due to chance. The July win rate of 42.4% is the closest to being significant in a rigorous mathematical sense. However, there is still a non-negligible probability of this being a statistical artifact with provides no insight into making investment decisions going forward. In fact, I would argue that the chances that this was just a chance occurrence are higher since there is really no fundamental economic reason why returns should be worse in July than in other months.
    May 29, 2013. 10:00 AM | Likes Like |Link to Comment
  • LeBron James, Sam Bowie, John Paxson And Portfolio Construction [View article]
    We are on the same page that capital allocation definitely depends largely on your investment horizon. My problem is that when products are marketed there is little discussion of how the product should be used (i.e. how does this fit into the portfolio of a 22-year old just out of college vs. a 45-year old with three kids). Instead, people tend to speak of the products in a vacuum and convince people to invest solely on the merits of the product alone with no discussion of portfolio fit or concrete performance evaluation metrics going forward.
    May 29, 2013. 08:49 AM | Likes Like |Link to Comment
  • Deceived By Correlations: A Quant Conundrum [View article]
    @Business Economics Analyst:
    Indeed, correlation prediction is very difficult. However, this is a problem we see time and time again: people assume that negatively correlated assets must have divergent trends and that positively correlated assets move in the same direction. By the definition of correlation, this need not be so.
    Given that many people utilize correlation as the basis for their diversification decisions, it is important to highlight how the strict statistical measure may deviate from their intuition -- and a quick and dirty method for correcting for it: namely, assume "means" are zero.
    Apr 9, 2013. 01:48 PM | Likes Like |Link to Comment
  • Deceived By Correlations: A Quant Conundrum [View article]
    Thanks for sharing!
    Apr 9, 2013. 01:37 PM | Likes Like |Link to Comment
  • Deceived By Correlations: A Quant Conundrum [View article]
    @change is the only constant:
    It is true that the independence of random variables necessarily implies zero correlation between those variables, though zero correlation does not necessarily imply independence of random variables.

    What we are trying to point out here is that the "trend" component is ignored because the variation occurs around the mean. Therefore, any measures of joint-deviation are going to be about the "noise" and not the underlying trends of the return distributions.

    By assuming a zero mean, our "trend" component gets measured as noise -- and therefore positive (or negative) correlations emerge.
    Apr 9, 2013. 01:37 PM | Likes Like |Link to Comment
  • Deceived By Correlations: A Quant Conundrum [View article]
    My apologies for not being clearer in this scenario. Our "intuition" is that these assets are negatively correlated (as their trends are in opposite directions) -- but the statistical measure says they are independent.
    Apr 9, 2013. 01:34 PM | Likes Like |Link to Comment
  • Bond Bubbles And Credit Quality [View article]
    @Princeton Ivy Management LLC, thanks for the comments and question. You've asked as very broad question, so I'm not going to go into a lot of detail around the issue; however I can provide you some starting points to formulate your own answers visa vie your own analysis.

    Remember the yield curve represents many aspects of investor expectations: one is a maturity premium that investors require to hold longer dated instruments, another is the inflation premium investors demand to hold assets paying constant streams of income (that aren't inflation adjusted). If you're able to more clearly isolate, given the scenario you're interested, *why* the changes in rates have taken place (inflationary expectations increase, term premiums increase, increase in supply of long-dated instruments, etc.) then that's a good place to begin your analysis.

    Happy (insight) hunting!
    Apr 3, 2013. 06:20 PM | Likes Like |Link to Comment
  • Bond Bubbles And Credit Quality [View article]
    @briandp32, thanks for your comment, great question. The links to the data provide you with all the necessary tools do an analysis to test your hypothesis. No one would argue the Fed has an arduous task of unloading a massive balance sheet onto a bond market concerned about your very question. One could reasonably conjecture that greater misalignment between expectations and outcomes implies (1) greater rate volatility and (2) greater changes in rates. However, I would still expect the basic premise of "lower credit quality to depreciate less than high credit quality during rising interest rate environments" to hold -- all else equal.

    It's worth noting that there were several periods used in this analysis where consecutive cumulative changes are close to 2% (see the x-axis of the scatter plots from the first graph series), which could be construed as information release grossly misaligning with expectations. So although the analysis isn't entirely based upon "big surprises," one could argue there are a couple data points in the sample where that is the case.
    Apr 2, 2013. 02:43 PM | Likes Like |Link to Comment
  • Bond Bubbles And Credit Quality [View article]
    @the social scientist: Thanks for your comment. Cumulative changes in bond prices are plotted against cumulative changes in interest rates. You are correct, autocorrelation is introduced in the process. This makes tests of statistical significance and explained variation meaningless, which is why these values aren't provided or included in the post. It does not however, prevent legitimate illustration of sensitivity towards some factor, which is what the scatter plots illustrate.

    I'm not sure where you're getting the "conclusion" that junk bonds are uncorrelated to high quality. Junk bonds are highly correlated to high quality, but they tend to fall less than high yield during rising interest rate environments as credit spreads compress (the conclusion of the article). The statement about interest rate risk and credit risk moving inversely is not meant to imply that fixed income of differing credit quality are not correlated -- it is a statement about correlation of underlying risk factors.

    A quote from Swedroe, "while junk bonds have a relatively low positive correlation to equities on average, that correlation has a nasty tendency to dramatically increase at exactly the wrong time -- when equity risks show up."

    So, if you foresee large equity declines in the near future, you're exactly right that you can expect a similar behavior between High Yield and Equities. However, if look at 6 month rolling correlations between HYG (High Yield Bonds) and IWV (Russell 3000) since October 2007, the correlation has moved between 0.20 and 0.89, so your logic (as Swedroe would agree) should be applied only under certain scenarios.
    Apr 2, 2013. 01:10 PM | Likes Like |Link to Comment
  • Visualizing Increased Correlation During Market Crashes [View article]

    We appreciate the comment. The example we used in the article was just that, an example, and not a recommendation by any means. Your recommended portfolio of SPLV, DBA and TLH is an interesting one. In particular, we like the use of SPLV in order to take advantage of the, well-documented at this point, premium offered by low volatility equities. However, this portfolio still has a drawdown of ~22% during the credit crisis.

    While this is indeed a large improvement in risk-adjusted returns, it may still be highly damaging depending on the type and purpose account. The main point we meant to highlight is that for investor's that need more return than that offered by a very conservative portfolio, but that are also highly sensitive to drawdown (think retiree's with a relatively long retirement horizon remaining) tactical management is one of the only answers that is cost-effective (assuming you pick the right manager).
    Mar 20, 2013. 03:00 PM | Likes Like |Link to Comment
  • Understanding The Perils Of Mean-Variance Optimization [View article]
    xinge2010: The peril of unconstrained MVO derives from the risk that low eigenvalue (low variance) principal components (principal portfolios) may have high relative expected excess returns, which will lead to the low variance principal portfolios dominating the resulting portfolio.

    Consider the case where the majority of principal portfolios have an expected excess return to variance ratio of ~0.5, but the lowest variance portfolio has an expected excess return to variance ratio of 10. When it is leveraged, it will become the single dominating "factor" in the portfolio.

    The risk here is that Random Matrix Theory tells us that the principal components with the lowest variance (eigenvalues) are likely dominated by sampling noise, meaning that they are meaningless portfolios. From one rebalance period to the next, not only will the portfolio exhibit instability, but expected excess return and variance estimates may be fairly meaningless.
    Feb 27, 2013. 09:07 AM | Likes Like |Link to Comment
  • Understanding The Perils Of Mean-Variance Optimization [View article]
    A quick math correction: The scaling factor in the SPY & AGG example should be 7.11, AGG's gamma should be 0.2844, and the optimal weights are 21.95% for SPY and 78.04% for AGG.
    Feb 26, 2013. 09:27 AM | Likes Like |Link to Comment
  • The Importance Of Benchmarking In Strategy Evaluation [View article]
    One problem with R-squared is that it is going to rely very much on the time period that you choose for the calculation. Say you had a product that tactically allocated between SPY and AGG such that the product was either 100% SPY, 100% AGG, or 50%/50% SPY/AGG. I would argue that a static 50/50 portfolio would be a decent starting point for a benchmark since this is the "base" allocation around which the product rotates. However, if the product is 100% AGG for a significant period of time, the R-squared to this 50/50 benchmark may be low. R-squared is one criteria to evaluate the choice of a benchmark, but it should be used in conjunction with other quantitative and qualitative measures.

    As a point of reference, the R-squared between EFA and the strategy in the article is 0.95, even though I think EFA is not the right benchmark.
    Feb 14, 2013. 09:46 PM | Likes Like |Link to Comment