On Friday I attended the NBER Asset Pricing meeting (program here) in Chicago, organized by Adrien Verdelhan and Debby Lucas. The papers were unusually interesting, even by the high standards of this meeting. Alas the NBER doesn't post slides so I don't have great visuals to show you.
Lars Hansen started with the latest in the Hansen-Sargent ambiguity / robustness work,Sets of Models and Prices of Uncertainty. Stavros Panageas gave a beautiful discussion, complete with power point animations. He characterized the paper as a major advance, for reducing the range of models over which an ambiguous agent looks for the worst case scenario, and for making that range state-dependent.
In the application, the agent worries that the mean growth rate of consumption and the AR(1) coefficient might be wrong; a more persistent consumption growth process is hurtful, and that pain is more in bad times.
I haven't followed this work closely enough. I still wonder what the testable implcations are -- how different is the asset pricing model from one in which the true consumption growth process is just a bit different from our estimate, in the worst possible way?
Still, it's nice to see a Nobel Prize winner leading off a conference, and with easily the most technical paper at that conference, with another one (Rob Engle) in the audience. That tells you something about the seriousness of this group. Also, this is serious behavioral finance by any metric -- a disciplined model of probability misperceptions, which is nice to see.
Robert Novy-Marx presented Testing Strategies Based on Multiple Signals, discussed by Moto Yogo. We're all familiar with the phenomenon that if you try 10 characteristics and pick the best few to forecast returns, t statistics are biased and performance falls out of sample.
Robert pointed out that if you put those best 3 in a portfolio, they diversify each other, reducing the in-sample variance of the portfolio, and boosting Sharpe ratios and t-statistics even further.
Many ``smart beta'' funds are doing this, so the fall-off in performance from backtest to real money is relevant beyond academia.
The extent of this bias is impressive. Here is the distribution of t statistics that results when you pick the best three of 20 completely useless signals, and put them in a portfolio. Critical values of 4 and 5 show up routinely in Robert's calculations.
Laura Veldkamp presented her work with
David Lucca, and
Laura Veldkamp, Taking Orders and Taking Notes: Dealer Information Sharing in Financial Markets. Discussed ably (of course) by Darrell Duffie. Is it a problem that the dealers who are the prime bidders at treasury auctions have been caught talking to each other ahead of the auction? Surprisingly, no: The Treasury can come out ahead when dealers share information with each other, and investors can potentially come out ahead too.
This warms my contrarian economist heart. We know so little about how markets work, and regulators are so quick to jump on supposedly bad behavor, it's lovely to see a clear and convincing model, that explains the kind of second-order and equilibrium effects that economists are good at.
Brian Weller presented Measuring Tail Risks at High Frequency, discussed nicely by Mike Chernov. Brian's basic idea is to run cross-sectional regressions of bid/ask spreads, normalized by volume and depth, on the cross-section of factor betas. Since spreads are larger when dealers are more worried about big jumps, this produces a measure of time-varying probability x size of such jumps. The measure correlates well with the VIX.
Michael Bauer presented his paper with Jim Hamilton Robust Bond Risk Premia discussed very nicely by Greg Duffee. (My discussion of a previous presentation). This paper is really about whether macro variables help to forecast bond returns. We're used to "Stambaugh bias:'' if you forecast returns with a persistent regressor, and the innovation in the regressor is strongly negatively correlated with the innovation in the return, then the near-unit-root downward bias in the regressor autocorrelation seeps over into upward bias of return predictability. But macro variables forecasting bond returns have innovations nearly uncorrelated with the returns, so that's not much of a problem. Michael and Jim show another problem: with overlappping returns, t statistics can be biased down too.
This led to a pleasant reassessment of bond return forecasts. Some points that came up: econometrics aside, many return forecasters don't do well out of sample. Many of the issues are specification issues orthogonal to this econometric point. For example, evaluating the huge forecastability of bond returns from a combination of level and inflation documented by Anna Cieslak and Pavol Povala, where the forecasters look a lot like a trend, is really about specification and interpretation, not econometrics. I held out the view that the important part of my paper with Monika Piazzesi is the single-factor structure of expected returns, not whether small principal components help to forecast returns. We had a pleasant interchange on whether it's a good or terrible idea to run one-year horizon forecasting regressions. I like them, because they attenuate measurement error. Raising a weekly autoregression to the 52nd power yields junk. Greg likes them, and gave a stirring reminder of Bob Hodrick's point that you can include lags of the forecasting variables instead.
Nick Roussanov presented his paper with Erik Gilje and Robert Ready, Fracking, Drilling, and Asset Pricing: Estimating the Economic Benefits of the Shale Revolution with Wei Xiong discussing. They track the reaction of stock prices to the shale oil boom. In particular, they showed that stocks which rose on a huge shale announcement subsequently rose even more as more good shale news came in. Until, as Wei pointed out, prices collapsed.
Nick also used stock market value to try to get at an estimate of the economics benefits of fracking. It's a worthy effort, but let's remember the difficulties. In a competitive no-adjustment cost world, profits are zero and there are no abnormal stock returns. Stock capitalization may rise, as firms issue stock to invest. But that measures the value of capital invested, not the consumer surplus of shale. Still, the general idea of mixing asset pricing, energy economics, and making economic measurements from stock prices is intriguing.
Jonathan Sokobin, Chief Economist, FINRA presented "An Overview of FINRA Data" which I alas had to miss. I'm delighted anyone from the government wants us to use their data!
The AP meeting has a nice tradition. Usually the most boring part of a conference is the author's response to discussant. The AP meetings do away with this -- or rather, the author can respond if someone in the audience raises his or her hand and says "I'd like to hear your response to x." That actually happened! But by and large the AP meetings preserve time and a tradition of very active participation and discussion, and this one was no different.