Monthly Archives: April 2009

Asian Economic Development Model–Japan

April 27, 2009 By in Asian economic development, economics, Japanese development Tags: , , , , , Leave a comment

The Japanese Model

One of the most interesting economic phenomena of the past sixty years has been the emergence of Asian economic superpowers.  The most successful of them have all studied the development of post-WWII Japan and imitated large parts of it.  This is my take on how the Japanese model works:

Japan found itself at the end of WWII with a lot of its industrial infrastructure destroyed and many of its young adult population killed in the war.  Not endowed with lots of industrial raw materials, its major remaining  tradable economic asset was its labor power.  It had other pluses.  It had strong political cohesiveness,  through the belief in the pivotalposition of Japan in the world order and in the role of the Japanese emperor as the sole global mediator between the human and the divine.  The pre-war industrial conglomerates (zaibatsu), although legally banned, survived in all but name in the now famous post-war keiretsu, so the country had experienced administrators.  In addition, Japan had American help during reconstruction.

The essentials

What are the essential elements of the Japanese model? Continue reading →

Capital Asset Pricing Model (CAPM)

April 26, 2009 By in Academic Theories, Basic Concepts, Capital Asset Pricing Model (CAPM) Tags: , , , , , Leave a comment

The Capital Asset Pricing Model is the keystone of the academic Modern Portfolio Theory developed in the Fifties and Sixties.  Its leading lights, Harry Markowitz, William Sharpe and Merton MIller, received the Nobel Prize in Economics for their role in developing this theory.

Taking a very simple view, the main difference between the CAPM and what I described in my Alpha and Beta post is the explicit introduction of a “risk-free” asset, normally thought of as being treasury bills.

Here’s the Alpha and Beta equation:

stock return = α + β(index return) + ε,

where α is a constant, β is the multiplier that links stock return and market return, and ε is a random error term.  (Although the theory doesn’t require it, the “index” has typically been interpreted as a stock market index, like the S&P 500.)

If we argue that the stock return has two components, the risk-free return (rf)  + the return for taking risk, then the equation can be rewritten as:

stock return = rf + α + β(index return – rf) + ε,

where β (a slightly different β from the first equation, but the same general idea) is a measure of the volatility of a stock vs the market, and α (a different α, sometimes called Jensen’s alpha) is any return that remains, positive or negative. Continue reading →

Alpha and Beta

April 23, 2009 By in Academic Theories, Alpha and Beta, Basic Concepts Tags: , , , , 5 Comments

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 →