Abstract: Why GameStop? Why January 2021? We analyze a setting in which a risky asset is traded by two types of investors: some are all in and buy up to their margin limit and some buy and sell based on the asset's fundamental value. A higher price of the asset increases all-in investor wealth and they borrow against this wealth to buy more shares. All-in investor demand for shares is therefore upward sloping. If all-in investors have (i) enough wealth, and (ii) access to at least 2:1 leverage, then aggregate demand for shares can be upward sloping and an equilibrium price therefore unstable. If P0 is an equilibrium price, then there exists a price P1>P0 that also clears the market. P0 is unstable and P1 is stable. Unfortunately, if the price actually is P1, then it will be unstable and there will exist a P2>P1 that clears the market and is stable. This is true for any arbitrarily high proposed price. When some investors are all in, therefore, the sky's the limit. Access to leverage above 2:1 is only possible in the United States using stock options and options trading has only been available for inexperienced retail investors recently. These investors could only trigger unstable equilibria if the company that they targeted was small enough for their wealth to matter but large enough to have traded options. Our theory therefore answers the two questions posed at the start of this abstract.
I am a founding member of the Labor and Finance Group, a group of researchers across economics and finance dedicated to the study of the intersection of labor economics and finance. The group's work, upcoming conferences, and working paper series can be found here: https://sites.google.com/site/laborandfinancegroup/.
I am a director of the Financial Research Association: https://fraconference.com/
University of Colorado Boulder website: Ed Van Wesep at Leeds School of Business
I can be contacted at edward dot vanwesep at colorado dot edu.
SSRN Research page: SSRN author page
Video from the 2018 ASSA Humor Session: A Unified Theory of Nerdiness
Video from the 2020 ASSA Humor Session: Musings of a Depressed Economist
If you use these data, please understand the caveats in the set's construction, as explained in the associated paper. Feel free to email me to better understand why/how certain people are included or excluded.
I don't believe that most people are conniving or manipulative most of the time. I also don't believe that most economists have this view. Nonetheless, most economic theory research in the last 40 years has centered around what we call "strategic" behavior. In large part, I suspect that this is because non-strategic behavior is assumed to be uninteresting. Welfare will be maximized. Everyone will get along. A substantial share of my work is dedicated to showing that there are many interesting areas to explore that have nothing to do with strategic behavior. Indeed, first-order facts about the world that have never been researched in Economics or Finance are easily explained by people generally being good and helpful.
Compensation policies reflect a lot more than moral hazard and adverse selection:
Communication practices reflect a lot more than the struggle to overcome divergent interests:
5) Different settings feature different patterns of language: referees are harsh, recommendation letters are kind, grades are inflated, my aunt is hyperbolic. This is consistent with saving the "breadth" of the language for the situations in which it most matters that the listener/reader understand what's being said.
This way of thinking about my work is, I hope, helpful, but it's not standard. An alternative way to categorize my work is by topic. I primarily study financial compensation of rank-and-file employees. Past research has concerned the use of signing bonuses, severance pay, incentive pay, stock options, and pay timing as motivation, retention, and screening devices. I'm currently studying compensation practices in the finance industry and the basis of discrete reviews and ratings (think stock analyst ratings, film critic reviews, student grades, and Yelp ratings). Other research interests are corporate governance, short sales, shareholder voting.
1) What happens to academic productivity post-tenure? The facts for economists appear in the figure below (full paper and details can be found here: https://papers.ssrn.com/Sol3/papers.cfm?abstract_id=2781693). The solid line plots the average number of publications per year for economics and finance faculty across a wide set of institutions. The number is adjusted so that an author is credited with 1/N publications for an N-authored paper. The dashed line plots the number of publications per year that are among the top 10% most-cited of all publications in that year. In both cases, productivity rises until the researcher is granted tenure, falls abruptly in the following two years, and then drifts down for the remainder of the decade following tenure.
2) One might imagine that the problem is confined to academics who are barely able to get tenure. Perhaps the best of the best are immune. In the figure below, we separate the sample by how many years it took the researcher to get tenure and plot the average annual number of publications pre- and post-tenure, adjusted for the number of co-authors on a paper. Clearly, those who are tenured earlier are stronger, both before and after tenure. Those who are granted tenure in less than five years are especially productive. But, in all cases, productivity declines substantially after tenure.
3) Do academics branch out into new research areas after getting tenure? These regressions have a paper-author pair as an observation, and have as dependent variables whether the author's paper was with a new co-author, published in a new journal, or published in a new research area. One dependent variable is a dummy for whether the researcher has tenure. We find no evidence that researchers branch out after tenure.
Not all interesting questions are about academic tenure! Here are a few others.
4) Do CEOs and other executives tend to give to the same politicians as the firms that they control? We calculate, for each executive and each firm in each year, whether the person/firm gave more in aggregate to republicans or democrats that year. If the person/firm gave more to republicans, then that person/firm-year is coded as "1" and if the person/firm gave more to democrat, then that person/firm-year is coded as "-1". We then calculate, in each year, the correlation between CEO giving and the giving of other top-five executives, the correlation between CEO giving and firm giving, and the correlation between top-five executive giving and firm giving. On average, CEOs and other top executives tend to give to similar parties. The correlations of both groups with the firms that they control is low. Deeper analysis shows that firms tend to give to whichever party controls the US congress (though with a substantial bias toward republicans) while executives tend to give to republicans regardless of who controls congress.
5) What determines how often we get paid? When I was in graduate school, I was paid quarterly. Let me just say that it took a lot of planning to be sure that I had money at the end of each pay period! Most people are paid a lot more often. Pay timing is not random, as Chris Parsons and I show in the paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1706206. Paying workers more often reduces the volatility of their consumption streams, making them better off in the long-run. Not surprisingly, pay tends to be more frequent for workers who might have more trouble smoothing consumption on their own. As shown below, workers with less education tend to be paid more frequently. Acquiring an education requires planning and long-term focus, both of which correlate positively with the ability to smooth consumption. Workers who own stock or certificates of deposit are also paid less often.
6) There's momentum everywhere! Case and Shiller showed in 1989 that there's substantial price predictability in US single family homes. That remains true today: since 2000, if prices went up 10% in your hometown last year, then they will rise about 6% to 7% this year, on average. But it turns out that there's momentum in lots of other places too (details can be found here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2867730). There's momentum in other types of property. Here are contemporaneous and lagged monthly returns for apartment buildings and core commercial office buildings:
Here are contemporaneous and lagged quarterly returns for turboprop airplanes and large jets:
Here are contemporaneous and lagged monthly returns for New York City taxi medallions:
What we really need is a theory that can explain all this...
7) Speaking of taxi medallions, here are two plots of price and volume for corporate and individual medallions. I have monthly data going back to 1992 and annual data back to the mid-1960s. Transaction volume was high in the early years, but prices were low. As prices have risen over time, medallions traded hands less often.
Prices more than triples from 2003 to 2013, peaking above $1,000,000. Since the introduction of ride-sharing in New York, volume has slowed to a trickly and prices have fallen. Many of the sales since 2015 have been foreclosure auctions.