daily volatility, non-correlation …and beta

My wife and I are in the process of hiring a financial planner.  While I think this is important to do, our search has brought me back into vivid contact with some of what I consider the nonsensical jargon of academic finance.  I want to write about the general idea of “non-correlated assets,” but I’m going to start by writing about beta.


In the early days of computer-driven finance, just after WWII, economist Harry Moskowitz proposed beginning to assess the risk of a portfolio by analyzing the interrelationships among individual stocks in it.  That task proved too daunting for the computers of the day for anything but small numbers of stocks.  Others suggested correlating everything to one standard, an index like the S&P 500, for instance, instead.

The regression that would do this has the form of y = α + β(S&P).  This is how beta, the correlation between a given stock’s price movement and that of the market, was born.

So far, so good.

…and gold stocks

One day, people discovered that there was a class of stocks–gold stocks, in particular– that had a beta of 0.  This spawned the idea, encouraged by the gold-bug prejudices of the day, that one could lower the beta of a portfolio just by adding gold stocks.  One could add, say, technology stocks with a beta =2 and offset the risk by adding gold stocks in the same amount.  Simple math said the combination had a beta = 1, or risk exactly equal to that of the market.

Some institutional investors actually bought the theoretical argument about the “magic” property of gold and altered their portfolios in the way I just described.

By doing so, they exposed themselves to the 20-year bear market in the yellow metal that lasted from 1950 to 1970.  They lost their shirts.

They realized only afterward that a beta of zero did not mean that the asset in question had no risk.  It meant instead only that the zero-beta asset did not rise and fall in price in line with the stock market.  In this case, the “uncorrelated” price went straight down during a period when the S&P gained 500%+.  So much for non-correlation.

More tomorrow.




uncorrelated returns: hedge funds as the new gold

Every stock market person knows what beta is.

It comes from a regression analysis, y = α + βx, where y is the return on a stock and x the return on the market).  It shows how a given stock’s past tendency to rise and fall is linked to fluctuations in the market in general.  A stock with a beta of 1.4, for example, has tended to rise and fall in the some direction as the market, but move 40% more in either direction; a stock with a beta of 0.8 has tended to exhibit only 80% of the market’s ups and downs.

The professor in a financial theory course I took in business school asked one day what it meant that gold stocks had, at the time, a beta of zero.

The thoughtless answer is that it means they aren’t risky, or that they don’t go up and down.

A consequence of this thinking is that you can lower the beta, and therefore the risk, of your investment portfolio by mixing in some gold stocks.What’s interesting is that in the early days of beta analysis that’s what some institutional portfolio managers actually did with their clients’ money.

That didn’t work out well at all.

What should have been obvious, but wasn’t, is that the zero beta didn’t mean no risk–or that gold stocks are/were a good investment.  It meant what the regression literally indicates–that none of the movement in gold stocks could be explained by movements in the stock market in general.

The riskiness of gold stocks is there, but it came/comes in other dimensions, like:  how mines develop new supply, the ruminations of the gnomes of Zürich (in today’s world, Mumbai and Shanghai), the potential for emerging country craziness, the propensity of the industry to fraud.

Why write about this now?

I heard a Bloomberg report that institutional investors as a whole are upping their exposure to hedge funds, despite the wretched performance of the asset class.  Their rationale?   …uncorrelated returns.

It sounds sooo familiar.

Admittedly, there may be a deeper game in progress.  It’s impossible to say your plan is fully funded by projecting a gazillion percent return on stocks or bonds.  But who’s to say that a hedge fund can’t do that?



what is “smart beta”? (l): alpha and beta

I’m going to write about this in two posts.

Today’s will give some basic background. Tomorrow’s will look at smart beta itself.

alpha and beta

Right after WW II many professional investors, and academics as well, were eager to apply newly emerging computer technology to analyzing the stock market.  Harry Moskowitz, an IBM scientist, was the first.  He suggested using computers to record and analyze the interrelations in price action among all the stocks in the market.  But measuring the reciprocal influences on even relatively small numbers of stocks proved too daunting for the computing machines of the day.

That led to the idea that the task be simplified by not relating each stock in a universe like the S&P 500 to each of the other 499.  Study, instead, how they each behave in relation to some common standard–in fact, relate each to the index itself.  un a regression analysis that correlates the daily price change in each stock with the price change in the index.

An equation showing the results for a stock “y” would be in the form:

y = α + βx + an error term that can be ignored

So the price change “y” for any stock can be broken down into two elements:

systematic,return, or beta, the portion due to market fluctuations.  For academics, this is a constant “β” derived from the regression, multiplied by “x,” the price change in the market  and,

a non-systematic term or alpha,, an “extra” return, that can be either positive or negative.

By definition, the β of the market = 1.0 (the sum of all the returns of the market components = the return on the market).

In the strange world of academic financial orthodoxy, it’s impossible to achieve a positive α. The only was investors can achieve a higher return than the market is by arbitrage–by borrowing money and buying what amounts to an index fund.

The popularity of this view–whose only virtue as I see it is its simplicity–shows itself in industry jargon.  Active managers are said to be “seeking alpha.”  Pension plan sponsors routinely separate their equity assets into active and passive, the latter being moneh invested in “safe” index products.

“Smart beta” is a marketing approach by active managers to e a portion of their “safe” index assets to the “seeking alpha” pool.  Apparently they’re successful, although the essence of their pitch is semantic—-they label their active managing activity as being “beta,” not alpha.  It’s the equivalent of the old junk bond pitch, “all the safety of bonds, all the high returns of stocks.”  We all know how that ended.

More tomorrow.

Alpha and Beta

You can often hear an investment professional say, “That’s a high-Beta stock.”  Less frequently, you may see the claim, normally in writing, that someone “is searching for alpha.”   Here’s what they’re talking about:

How It Started

After World War II, as the first commercial mainframe computers were developed, an economist named Harry Markowitz proposed using this new computational power to calculate the risk/reward characteristics of portfolios of stocks.  With this information in hand, you would be able to determine the best portfolio for you to own, i.e., the highest-return portfolio for a given level of risk.

Markowitz used a statistical concept, variance–a measure of how far away from a trend line a stock’s price might temporarily drift–as his definition of the riskiness of a stock (this is a terrible definition of risk, in my view, although it’s, even today, the academic standard).  From this definition, it follows mathematically that the riskiness of a portfolio is the riskiness of the individual stocks in it plus a factor (covariance) of their tendency to move together in herds.

The general idea was that you could quantify, and therefore compare, the riskiness of different portfolios that offered the same return, as well as the returns of portfolios that had the same risk.  So you can “optimize,”  that is, eliminate unnecessary risk by picking the best portfolio–highest return for a given level of risk, or  lowest risk for a given level of return.

There’s a practical problem, though.  If the universe has only two or three stocks in it, calculating this information is straightforward.  If the universe is the S&P 500, however, figuring out all the interrelationships among all the stocks becomes a real pain in the neck.

(There’s a much bigger problem, though.  The virtues of short-term price volatility as a measure of risk is that the data are easily available for many stocks and that variance is part of an established mathematical framework.  So it has been widely adopted by academics and consultants.  Unfortunately, it’s otherwise not very informative, I think.  It’s like saying that the risk in an airplane flight should be measured by the amount of air turbulence en route.  By this measure, the plane that recently took a smooth ride into the Hudson River would be classified as a safe flight.) Continue reading