Here I Go Again!

As many of you know, I'm in the midst of taking the CFA (Chartered Financial Analyst) exams. I had been registered for the Level 3 exam this last June, but dropped out because of the Unknown Son's health issues (and I'm glad I did, because the time was much better spent with him in his last days).

Well, I just re-upped and re-registered for the 2010 exam. So, I'm once again a Level 3 Candidate. At least this time I'm already familiar with well over half the material, so it shouldn't be nearly as stressful.

I might even start early and keep with it this time around. With luck, I might be done with my first pass through he material by January 1. While that seems early, if I shoot for January 1, I'll probably actually finish by March or so, and then I can focus on actually locking this stuff down. After all, the material is interesting, but I don;t want to take any more exams for a while after this.

Happy 40th Anniverary, Moon Landing

I remember watching/listening to the Apollo 11 moon landing as it happened on the television. Hard to believe, but it's been 40 years since .

And yet, some conspiracy theory whack jobs still doubt that it happened. One moonbat (sarcasm intended) named Bart Sibrel systematically harassed the Apollo crewmembers to see if they'd admit that the landing was a hoax. He made the mistake of calling Buzz Aldrin a liar. Click below to see what happened.

Man - I could easily keep clicking this all day like one of those experiments where they gave mice crack.

"Garbage Research" and The Equity Risk Premium

Instead of the CCAPM (Consumption CAPM), we now have the GCAPM (Garbage CAPM). Alexi Savov (graduate student at U of Chicago) finds that he can explain much more of the Equity Risk Premium using aggregate garbage production than he can using National Income and Product Account (NIPA) data. Here's the logic behind his research (from Friday's Wall Street Journal article titled "Using Garbage to Measure Consumption"):

In theory, one way to explain the premium would be to look at consumption, a broad measure of wealth. People should demand a premium from an investment that goes down when consumption goes down. That’s because the alternative — bonds — hold on to their value when consumption declines. Another way to put it: When you are making lots of garbage, you are rich. When you stop making garbage, you are poor. Unlike bonds, which continue to pay out whether you produce lots of garbage (and are rich) or not, stocks are likely to lose their value during bad times. Therefore, investors should want a large reward for putting their money in something whose value decreases at the same time as their overall wealth decreases.

Unfortunately, the data typically used to measure consumption (the US Government's figures for personal expenditure on nondurable goods and services category in the National Income and Product Account) don't have a lot of variation. So, they don't work very well as an explanatory variable. Savov finds that whe he uses EPA records on aggregate garbage production, they're exhibit a correlation with equity returns that are twice as high as the NIPA/Equity returs correlations. Here's the abstract of his paper (downloadable from the SSRN):

A new measure of consumption -- garbage -- is more volatile and more correlated with stocks than the standard measure, NIPA consumption expenditure. A garbage-based CCAPM matches the U.S. equity premium with relative risk aversion of 17 versus 81 and evades the joint equity premium-risk-free rate puzzle. These results carry through to European data. In a cross section of size, value, and industry portfolios, garbage growth is priced and drives out NIPA expenditure growth.

Read the whole thing here.

Asset Class Correlations Increase In Bad Times

It's a pretty well-known fact that correlations between asset classes increase in really bad markets. To get a sense of how much this effect matters in terms of portfolio diversification, read this Wall Street Journal piece (published Friday, 7/10) titled "Failure of a Fail-Safe Strategy Sends Investors Scrambling. Here's a snippet:

Correlation is a statistical measure of the degree to which investment returns move together. Between 1991 and 1994, the correlation between the S&P 500 index and high-yield bonds was low, at 0.2 or 0.3, according to Pimco statistics. (A correlation of 1 means returns move in perfect sync.) International stocks had a correlation with the S&P 500 of 0.3 or 0.4, and real-estate investment trusts had a correlation of 0.3, according to Pimco data. Commodities showed little correlation to U.S. stocks. By early 2008, investment categories of just about every stripe were moving significantly more in sync with the S&P 500. The correlation on international stocks and high-yield bonds rose to 0.7 or 0.8, and real-estate investment trusts to 0.6 or 0.7, according to Pimco's data for the previous three years

Read the whole thing here (note: subscription required).

The problem with portfolio diversification is that it is typically implemented using historical correlations (actually, on covariances, but the two are essentially the same). To provide optimal diversification, portfolio allocations should be made based on "forward looking" correlations. In practice, some managers adjust historical correlation estimates to reflect their views of future relationships. But that becomes far more complicated than simply using historical estimates and assuming that they'll continue unto the future.

Note: if you don't have an online subscription to the Journal, try searching for the article using Google News - if you click on the link there, it works around the WSJ subscription filter (however, not all WSJ articles can be accessed this way).

Getting Your Data Straight

I've made progress on the paper I'm working on. Unfortunately, this week has been a good illustration of a quote from McCloskey: I believe it went something like "90% of writing is getting your thoughts straight, and 90% of empirical work is getting your data straight."

Unfortunately, my data wasn't straight - I realized that I had used the wrong data code (a certain type of dividend distribution) from CRSP. So, my previous analysis was basically crap (that's a technical term for the unitiated) and had to be redone using the proper data set.

Luckily, it looks like my primary results after using the proper code, but with a few minor changes. For now, I'm still doing the preliminary descriptive stuff. Since I did the initial version of the paper in a hurry (hey - it was a conference deadline), I took a few shortcuts. This time, I'm going back to step 1 and going over every line of code, and (just as important), making sure I know how the sample changes at each point. As a result, I'm much more confident with my data this time around.

But doing the descriptive statistics is still (to me) about the most boring part of the paper. Still, it's gotta be done.

The Limits of Models

Here's an excellent piece on the Psi-Fi Blog, titled "Quibbles With Quants." Here's a choice part:

What the models failed to capture was that humans don’t behave in simple, predictable and uncorrelated ways. It’s impossible to overstate the importance of the way these models cope with correlation of peoples’ psychology. To sum it up: they don’t. Let me know if that’s too complex an analysis for the mathematical masters of the universe.

Anyone who’s ever been to a nightclub, a football game or even a very loud party will know that there are situations where we don’t act as individuals, buzzing about doing our own thing. These are occasions when we all suddenly stop being individuals and start doing the same thing – usually involving large quantities of drugs and some very bad singing. Although these sorts of events are specifically designed to trigger this behaviour – which is probably a deep evolutionary adaptation to sponsor group behaviour, useful when it comes to running down tasty antelope and dealing with giant, carnivorous sabre toothed beavers – it can also happen in other situations. Most stockmarket booms and busts are generated by similar group effects.

In general, people behave in an uncorrelated fashion right up until the point they don’t.

Read the whole thing here.

Momentum Effects and Firm Fundamentals

The more Long Chen's work I read, the more I like it. I recently mentioned one of his pieces on a new 3-factor model. Here's another, on the momentum effect, titled "Myopic Extrapolation, Price Momentum, and Price Reversal." In it, he links the well-known momentum effect to patterns in firm fundamentals. Here's the abstract:

The momentum profits are realized through price adjustments reflecting shocks to firm fundamentals after portfolio formation. In particular, there is a consistent cross - sectional trend, from short-term momentum to long-term reversal, that happens to earnings shocks, to revisions to expected future cash flows at all horizons, and to prices. The evidence suggests that investors myopically extrapolate current earnings shocks as if they were long lasting, which are then incorporated into prices and cash flow forecasts. Accordingly, the realized momentum profits can be completely explained by the cross - sectional variation of contemporaneous earnings shocks or revisions to future cash flows. Importantly, these cash flow variables dominate the lagged returns in explaining the realized momentum profits. As a result, the realized momentum profits represent cash flow news that has little to do with the ex ante expected returns. In fact, the ex ante expected momentum profits are significantly negative.

So, in essence, he finds that investors ignore mean-reverting patterns in firm earnings, and over-weight recent earnings shocks.

Very nice.

On an unrelated note, the Unknown Family will be traveling the next few days for a family reunion in West Virginia (the Unknown Wife's father grew up their, and that fork in the family tree has a get-together every year). So, unless I schedule a few pieces to post automatically, posting will likely be slim for the next few days.