On Market Efficiency: Market Fair Value Estimates and the True Cost of Capital

In the world of investing and corporate finance, the Efficient Market Hypothesis (EMH) casts a long shadow. EMH states that a sufficiently liquid market reflects the “correct” price at all times. Since efficient markets factor in all known and relevant information at all times, it is therefore practically futile to attempt to predict the future direction of market prices. In other words, a blindfolded monkey throwing darts at the Wall Street Journal has about the same chance as beating the market averages as any professional investor. At one extreme, the founder of Vanguard Investments Jack Bogle revolutionized the mutual fund industry around cheap indexing, which he posited as the solution to efficient markets. At the other, Warren Buffet’s seminal essay, The Super-Investors of Graham and Doddes-ville, defends the notion that right-headed investors can carve out a significant edge [1. The Super-Investors of Graham and Doddes-ville]. In the middle, you have the greater majority of investors who will likely cede that both extremes contain some amount of the truth. Even 2013 Nobel Laureate Eugene Fama, of the University of Chicago Booth School of Business, who is credited with developing EMH, has stated that “[asset prices] are typically right and wrong about half the time” [2. The Super-Brainy Quote]. Being able to determine when they are right and when they are wrong is the holy grail to traders and investors alike. In order to investigate how correctly assets prices reflect all known information, we must develop an intuition and methodology for estimating the fair value of an asset. As we will discuss, just because a methodology is descriptive does not mean it is predictive (i.e., correlation does not imply causation).
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UPDATE: US Employment

Greetings, all.

My father just responded to my June 13, 2013 post, “US Employment: What’s Wrong With This Picture?“, with the following WSJ article: “The Hidden Jobless Disaster”.

Especially salient to my analysis on incentives is the following passage:

…research by the University of Chicago’s Casey Mulligan has suggested that because government benefits are lost when income rises, some people forgo poor jobs in lieu of government benefits—unemployment insurance, food stamps and disability benefits among the most obvious. The disability rolls have grown by 13% and the number receiving food stamps by 39% since 2009.

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The Labor Force: Who’s Leaving It?

In my last post, “US Employment: What’s Wrong With This Picture?“, I showed the workers of leaving the US Labor Force at an unprecedented rate. But who is leaving and why?

Obviously, workplace dynamics have changed dramatically (e.g., workers are retiring later in life and women have increasingly become a workforce with which to be reckoned). But how and by how much?

Specifically, what remained unclear was why the labor force, defined as the sum of employed and unemployed working age (25 – 54 y/o) adults, had undergone a secular increase from the 1940’s into the 2000’s and is now apparently reversing course. Is this due to a great dislocation of our perceptions and expectations? Perhaps there are other factors at play?

I can speculate all I want, but ultimately I need data to back my assertions. Fortunately, Quandl is making my data-life easier.

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US Employment: What’s Wrong With This Picture?

I would like to thank Quandl for providing me with this idea in their recent “Dataset of the Day“.

Often I have heard the Bureau of Labor Statistics’ (BLS) and the Federal Reserve’s (Fed) numbers on unemployment are misleading. Even though they show that unemployment is down, it now is obvious that employment is not necessarily up. A shrinking labor force may be a disruptive trend, and is surely worth watching.

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