The Equity Risk Premium is one of the central concepts of finance theory and practice. However, when we teach it in class (usually as part of the CAPM), we tend to do a lot of hand-waving and tell students to use historical ERPs. Aswath Damodaran of New York University has an excellent piece on SSRN titled "Equity Risk Premiums: Determinants, Estimation, and Implications" that's a must-read whether you're a professor, student, or practitioner. Here's the abstract:

When I teach investments, there's always a section on market efficiency. A key point I try to make is that any test of market efficiency suffers from the "joint hypothesis" problem - that the test is not tests market efficiency, but also assumes that you have the correct model for measuring the benchmark risk-adjusted return.

In other words, you can't say that you have "alpha" (an abnormal return) without correcting for risk.

I'm teaching Corporate Finance again this semester. In the class, we spend a fair bit of time on the CAPM (yes, I know - it's not perfect. But it is a still pretty good). One of the big issues is what to use as the Market Risk Premium (or, as it's sometimes called, the "Equity Risk Premium). Looks like I'll be using this piece as background: The Equity Risk Premium in 100 Textbooks by Pablo Fernandez of the University of Navarra. Here's the abstract:

I review 100 finance and valuation textbooks published between 1979 and 2008 (Brealey, Myers, Copeland, Damodaran, Merton, Ross, Bruner, Bodie, Penman, Weston, Arzac...) and find that their recommendations regarding the equity premium range from 3% to 10%, and that several books use different equity premia in different pages.

Here's a paper by Bradford Cornell that I've had in my in box for a while. It's titled "Investment Research: How Much Is Enough?" Here's the abstract

Aside from the decision to enter the equity market, the most fundamental question an investor faces is whether to passively hold the market portfolio or to do investment research. This thesis of this paper is that there is no scientifically reliable procedure available which can be applied to estimate the marginal product of investment research. In light of this imprecision, investors become forced to rely on some combination of judgment, gut instinct, and marketing imperatives to determine both the research approaches they employ and the capital they allocate to each approach. However, decisions based on such nebulous criteria are fragile and subject to dramatic revision in the face of market movements. These revisions, in turn, can exacerbate movements in asset prices.

I'm in the process of putting together material for my Advanced Corporate Finance class. Of course, it has a module on capital structure and payout policy. One of concepts we'll get across is that holding extra cash often gives managers incentives to invest in negative NPV projects (the old "free cash flow" problem). So, according to agency theory, managers should lever up and pay out the excess cash to shareholders in the form of buybacks and/or dividends. Unfortunately, higher leverage and lower cash holdings exposes the firm to increased risk of financial distress.

Along those lines, I was going through my "clippings file" and came across this piece in the Economist. It discusses some of the costs of excess debt during recessions. Of course, it's always easy to look back after the fact and say that firms shouldn't have levered up so much, since it means they'll face distress costs during a recession (hindsight's always 20/20, after all).

Here's an interesting interview of Yale finance professor Robert Shiller on the Charlie Rose show. The early part of the clip is an interview of Winston Churchill's grandson - it's also interesting (I'm a Churchill fan), but if you want to skip it, Shiller starts around 14:15.

I try not to get too excited about short-term market movements. At the same time, I have to keep up since I'm the faculty advisor for Unknown University's Student-managed fund. Even so, it's been a pretty good week (and month and year) so far - almost every equity index I can think of is in the green for the last month (and even year to date). As an aside, our fund is up 11.4% YTD (but I'm sure that will change).

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.

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.

The more I read Long Chen's work, 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.

First, a little background on "factor models": The CAPM model for estimating expected returns is the oldest and most widely know of all finance models. In it, exposure to systematic risk (i.e. beta) is only factor that gets "priced" (i.e. that's related to expected returns).

Next, in 1993, Fama and French showed that a three factor model (the CAPM market factor plus a size factor and a value/growth factor), did a much better job of explaining cross-sectional returns.

One of the hot stories this last week was that the Obama Administration had supposedly pressured and/or threatened hedge fund managers who held Chrysler debt.

ABC News reports:

From today's Wall Street Journal:

Citigroup Inc. announced Tuesday a new program aimed at addressing the latest challenge facing the mortgage industry: unemployed homeowners.

It's not that often that I get to see two of my favorite topics (UCONN basketball and executive compensation) collide (and on Youtube, yet).

After a recent game, during the press conference, UCONN coach Jim Calhoun was asked a question by freelance journalist and political activist) Ken Kreyeske, who apparently working for some outfit called 'The Hartford News'. He asked Calhoun:

I'm not a big fan of market timing and/or technical trading rules. From what I've seen, the empirical evidence casts a lot of doubt on their effectiveness.

But I just read a very interesting paper titled "A Quantitative Approach to Tactical Asset Allocation," by Mebane Faber. Here's the abstract:

Here's a very interesting piece from the New York Stock Exchange's blog:

...Certainly, any failure to convince others was not due to lack of effort. Perhaps Mr. Markopolos lacked only an effective medium to communicate his warning. Here's a thought experiment: What would have happened if Mr. Markopolos had blogged his analysis? That is, what if he had posted the entire piece on a blog, under his name or a pseudonym?

One of the problems with bonuses is that they create asymmetric payoffs - there's typically an upside for some actions, but no downside (yes, I know, there's the settling up in the labor market, etc., but that's a story for another piece). To deal with this, at least one firm (UBS) has started using "maluses" along with bonuses

When the average Joe (or Jane) looks at transactions costs from trading, they typically focus on the commission charged by the broker. But in the case of some thinly-traded ETFs (exchange-traded funds), the bid-ask spread can add significantly to that cost. Here's a good piece on the topic from Morningstar:

No one has a very precise definition of liquidity, but it roughly boils down to how easy it is to buy or sell a particular security and how much agreement there is in the marketplace upon the security's fair value. The most liquid funds or stocks have miniscule bid-ask spreads, where the prices differ by only a penny. On the other side, a brand new ETF tracking a selection of more thinly traded mortgage-backed securities has a bid-ask spread near 0.80% as I write this. That means that buying and selling the fund at market prices, even without any commissions charges or price changes, would result in a 0.80% loss. Not exactly a terrifying loss, especially compared with what we all saw in 2008, but still an unwelcome drag on portfolio returns if it can be avoided.

Jim Mahar over at FinanceProfessor.com just linked to a fantastic explanation of Credit Default Swaps. Here's the opening lines:

Credit default swaps (CDSs) have been identified in media accounts and by various commentators as sources of risk for the institutions that use them, as potential contributors to systemic risk, and as the underlying reason for the bailouts of Bear Stearns and AIG. These assessments are seriously wide of the mark. They seem to reflect a misunderstanding of how CDSs work and how they contribute to risk management by banks and other intermediaries. In addition, the vigorous market that currently exists for CDSs is a significant source of market-based judgments on the credit conditions of large numbers of companies--information that is not publicly available anywhere else. Although the CDS market can be improved, excessive restrictions on it would create considerably more risk than it would eliminate.

There's a pretty substantial literature on biases in analyst's earnings forecasts. They usually define bias as a difference between the last estimated forecast of earnings and actual earnings.

Here's a somewhat related paper - on errors in implied volatility estimates. In their paper "Implied and Realized Volatility in the Cross-Section of Equity Options" Manuel Ammann, David Skovmand and Michael Verhofen examine whether implied volatilites differ systematically from realized vlatilities, and whether those differences are related to firm risk (beta) size (market cap),growth opportunities (market/book), and momentum. Here's the abstract: