What a Hectic Couple of Weeks!

On top of finishing the two courses I was teaching this term, the past two weeks have been the busiest ones I've had research-wise this year, with deadlines to submit papers to two of the three major conferences.

The first one was for the European Finance Association meeting in Copenhagen in August 2012. The second meeting, whose deadline is today, is the American Finance Association meeting in San Diego in January 2013.

In the end I managed to submit three papers.

Here they are: 

The Role of Institutional Investors in Voting: Evidence from the Securities Lending Market

  (with Reena Aggarwal and Jason Sturgess, both from Georgetown University)

The Big Short versus The Right Short: Short Selling Activity and Housing Prices Predictability (with Carles Vergara-Alert from IESE)

Ownership Structure, Limits to Arbitrage and Stock Returns: Evidence from Equity Lending Markets

Thanks to all of my co-authors for the hard work. Let's get those babies in!

Now is back to revising them all and submit them to journals.

100 Years of the American Economic Review: The Top 20 Articles

Via Brad DeLong's blog, here are the top 20 articles in the AER's first 100 years.

I've read about 6-7 of them, but the one I often use on my research is the Grossman and Stiglitz's one:

Grossman, Sanford J., and Joseph E. Stiglitz. 1980. “On the Impossibility of Informationally Efficient Markets.” American Economic Review, 70(3): 393–408.

So Close... And Yet So Far

Perhaps THE biggest question in Finance is what makes expected returns vary from one security to the other. The idea that getting higher expected returns cannot be generated without bearing more risk has driven the revolution and spurred a million papers, either trying to show that it works or it doesn't.

Avanidhar Subrahmanyam has just released a paper on SSRN in which he reviews many variables and methods that people have used to predictor returns (like P/E, size, liquidity, etc.)

He writes "our learning about the cross-section is hampered when so many predictive variables accumulate without any understanding of the correlation structure between the variables, and our collective inability or unwillingness to adequately control for a comprehensive set of variables."

With so many people, testing so many variables, with so many different methods, it is often difficult to really know whether a given variable can truly predictive future returns or it is just reflecting correlation with something else.

It would be nice to see a paper trying to run a "horse-race"of lots of variables at the same time (I mean a lot, not just 4-5).

PS - The article cited that shows that garbage production in the US is a better proxy for consumption than standard measures is really cool.