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## Sell In May: It Ain't Over 'Til Its October [View article]

http://bit.ly/138j7cs

## Sell In May: It Ain't Over 'Til Its October [View article]

## Sell In May: It Ain't Over 'Til Its October [View article]

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.

## LeBron James, Sam Bowie, John Paxson And Portfolio Construction [View article]

## Deceived By Correlations: A Quant Conundrum [View article]

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.

## Deceived By Correlations: A Quant Conundrum [View article]

Thanks for sharing!

## Deceived By Correlations: A Quant Conundrum [View article]

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.

## 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.

## Bond Bubbles And Credit Quality [View article]

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!

## Bond Bubbles And Credit Quality [View article]

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.

## Bond Bubbles And Credit Quality [View article]

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." http://cbsn.ws/Xo9A1w

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.

## 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).

## Understanding The Perils Of Mean-Variance Optimization [View article]

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.

## Understanding The Perils Of Mean-Variance Optimization [View article]

## The Importance Of Benchmarking In Strategy Evaluation [View article]

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.