Less than two months after my detailed analysis on one pig of an ETN, and less than a week after reiterating the pathetic performance of the VXX as the worst ETN ever, comes this news from Bespoke and Clusterstock.

First, from Bespoke:

It appears as though VXX is yet another in a long line of poor performing ETFs that are based on futures products.

The futures contracts that VXX is based on are the CBOE/CFE’s S&P 500 Volatility Index futures. The futures themselves are somewhat tied to the CBOE’s Volatility Index, commonly known as the VIX. [I say somewhat tied, because in truth, the futures are specifically tied to the SOQ of the COs based on the HOSS of the EOB. Got that? Details here if you really want them.] While not perfectly tied to VIX, you rarely see the settlement price of VIX futures significantly deviate from VIX itself. For example, the last futures expiration was at the open on October 21. VIX opened at 20.90, while the VIX futures settlement based on that morning’s open was at 20.82.

One of the key factors with ODDS Correlation Trader is that it is based on adaptive indicators. The reason is that relationships change over time.

A classic example most of us are familiar with is oil. At times, lower oil prices are good for stocks, as the lower oil price reduces inflation, which improves consumer purchasing power and reduces business expenses. During times like this, oil and stocks are inversely correlated; as oil goes down, stocks go up.

You’ve probably seen charts that compare the current stock market to the stock market of the Great Depression. But have you seen a chart of the Great Depression stock market compared to “bubble market” housing prices?

Take a look at the chart below. [click all images to enlarge]

I was reading a blog post over at CXO Advisory Thursday. [Steve LeCompte who mans the shop there does outstanding work. If it's not part of your daily reading, it should be.] In that post, CXO did a quick analysis of a system based on P/E10 ratio. P/E10 is, as Steve explains it, “the ratio of the inflation-adjusted S&P Composite Index level to the average monthly inflation-adjusted 12-month trailing earnings of the index companies over the previous ten years.”

Steve used data from Robert Shiller to evaluate the system. That prompted me to take a fresh look at the Shiller data, which I had not seen in quite some time. The chart in the first worksheet, which I had ignored previously because I was only interested in the data, surprised me. It’s a graph of the S&P 500 Index adjusted for inflation going back to 1871 (not 1971, but 1871). Here’s my version of the chart (I changed some colors and used a logarithmic scale for the y-axis):

You knew this was coming. It’s one reason why I have yet to figure out why Buffett still owns any shares in this company.

I’ve written numerous times that the ratings agencies made mistakes on the ratings of all sorts of debt securities and their derivatives. Most of the mistakes were based on hubris. That is, the mathematicians had far too much confidence that their equations could handle any situation, only to find out that the models were ill-suited for the purpose which they were intended.

Implied volatility of out-of-the-money put options on Crude oil is higher than the implied volatility of comparable calls. In fact, the gap between the two is at a 5-year high according to Bank of America Securities-Merrill Lynch, quoted Monday on Bloomberg. That is, for the moment, put prices, and volatility, are skewed much higher than call prices. The indicator appears to be very similar to the CSFB Fear Index that measures skew in stock index options.

Here’s the question I have: what does a record-setting skew mean going forward for prices? Is the fact that puts are much more expensive a contrarian signal? That is, are the expensive puts a bullish sign? Or is the fact that option buyers are willing to pay a relatively higher price for puts than they are for calls a sign that the market is poised for a fall?

The S&P 500 climbed to more than 20% above its 200-day moving average. It’s the first time in more than 20 years since that has happened. Some say it’s bullish momentum that should carry the market ever higher. Others say it’s a very bearish overbought condition that could lead to a collapse. So who is right?

I’ll let you decide for yourself. But here is my take: there is absolutely no evidence whatsoever that this extreme reading has any value as a forecasting tool. Sometimes the market goes higher (1933). Sometimes it goes lower (1929). And sometimes it just sits still (1975). Like I said, it’s of little use.

Last week, the CFTC finally had enough and withdrew that no-action letter that allowed Deutsche Bank’s commodity ETF get around position limits by declaring itself as a hedger, instead of a speculator. At the time, I wondered what kind of spillover might occur, and now we’re seeing it, and not just in the foodstuffs.

In an important blog post almost exactly a year and a half ago, I noted that these ETFs made it easy for fixed income investors to switch just a portion of their money devoted to bonds and other investments into the commodity asset class. Because of the dollars involved in the bond market, a minor shift out of bonds would be a tsunami into commodities. I later wrote that the position limit exemptions were not causing commodities to go up, but they were causing the size of the increase to be larger than it would be if there was a limit to the amount of money the ETFs could take.

Statistical Correlation

Correlation: In statistics, it’s the degree to which two or more variables show a tendency to move relative to each other. In finance, it’s the tendency of two or more securities, sectors or asset classes to move together.

Correlation values range from -1 to +1. If one stock moves in perfect lockstep with another, the correlation is going to be +1. If they move exactly opposite one another, the correlation is going to be -1.

On Monday, Bespoke Investment Group, who probably has one of the most impressive stock market databases around (similar to our ODDS Online, that has one of the most impressive options databases around), put together an interesting report and graphic showing how the market has performed during different earnings seasons. What caught my eye is that when I glanced at the numbers, I got the impression that if you added them up, it would turn out to be a whole lot of nothing. So, I took the average of all the months, and indeed the average return of the S&P 500 during earnings season was a meager -0.11%.

I then compared that average return to the average monthly return for the market, as they are both about 30 days in duration. Knowing that the S&P 500 is pretty much right where it was in October 2001, I knew that the market’s return wouldn’t be that much either. Turns out, it’s a minuscule -0.02%. So there’s really not that much of a difference month-to-month … at least when you look at the average.

I’ve gotten a bit of criticism from some know-it-all traders who are big fans of the leveraged ETFs. Those who defend the funds say that leveraged ETFs are perfect for those who know how to use them. They derisively state that anyone holding these funds for the long term is an uneducated lame-brain. They claim that the leverage stated is clearly meant for the funds’ daily performance, not for the long term. Well, it is true that the funds state that the multiplier is calculated off of the daily returns.

But here’s where their supportive claims fall flat. These users of the ETFs also state that the funds adequately and clearly disclose that leveraged performance is isolated only to the daily performance. Further, leveraged ETF supporters say that anyone who uses these funds for a long-term bet is idiotic for not knowing that leverage claim made by the ETF fund companies only applies to the daily tracking.

One-month correlation among the nine large stock market sectors reached the highest level since the data became available in 1997. The high correlation is similar to what we saw October 2008 and March 2009 when everything went down together. Only this time, everything has been going up together. The key word in those two sentences is “together”.

This is an older article that I haven’t commented on because I didn’t have the data necessary to agree or refute. The lead paragraph begins:

Components of the index of leading economic indicators are signaling the worst U.S. recession in five decades may be over now, not three to six months from now.

Talk about poor timing. While one brokerage firm after another either bans, or considers banning, leveraged ETFs, Proshares launched a couple of new ETF products that provide triple the daily returns of the S&P 500. As I wrote before the market collapse last year, I’m not a fan of these leveraged ETFs because the way the leverage is calculated practically guarantees losses if there is any gyration in the price action of the underlying, or if the holding period is longer than 24 hours. Since then, things have only gotten worse.

With that in mind, amazingly, these newest leveraged ETFs are in big demand already. If you have any inclination to get more information on these things from an unbiased source, there’s none better than here.

Traveling these days, but had a little bit of time to check in on the news of the day, and this little bombshell from The Wall Street Journal.

Strange Traded Funds

I marked each prior instance where the NASDAQ posted 12 consecutive daily gains. You’ll need to click the chart to make it larger to see them all. The data encompasses the period from 1971 till now. But there were no runs of 12-straight up days prior to 1978 or after 1992, at least till today. So those time periods are not shown.

I also marked the one time there were 12 consecutive daily losses.

This is interesting. From Falkenblog. How frequently should we see a 10-day advance in NASDAQ over a 10-year period? The following graph helps answer that question.

Click to enlarge:

As I’ve said in prior posts, humans have a tendency to try to find patterns in everything. But sometimes, a pattern is nothing more than a series of outcomes that could easily have been produced at random. An example of a pattern that is utter garbage, a study on baseball says that:

… players whose first or last name begins with “K” strike out more than those without “K” initials.