The banking system is a machine to transform risk: people put their money into a bunch of risk-free-ish-or-so-they-think banks, and those banks lend that money to risky businesses, and the banks make money on their ability to price that risk appropriately and/or on their ability to get a government bailout when they price it inappropriately. From this description you can rough out some boundary cases – a bank that loaned money only to risk-free businesses wouldn’t make very much money or serve very much purpose, while a bank that was a massively levered conduit for moving depositor money into Ponzi schemes would also not serve much social purpose – but that leaves a broad middle ground where banks need to evaluate risk carefully enough to avoid blowing up but not so carefully that they constrain promising but risky investment.
But that middle ground is where the action is so you get papers like this one, from the Bank for International Settlements, about one flavor of lending and two flavors of banks. The lending is syndicated lending, and the banks are “bailed out banks” and “not bailed out banks,” and here are some suggestive charts:
The restrained conclusion in the BIS paper is “Although the riskiness of loan signings started diminishing across the board in 2009, we do not find consistent evidence that rescued banks reduced their risk relatively more than non rescued banks during the crisis.” And in particular, in 2010, banks that had been bailed out still had more levered, higher-yield syndicated loans than banks that had avoided a bailout. Read more »
We’ve talked a bit before about how there’s a booming academic business in papers finding that investment managers do or do not add value versus non-managed alternatives like passive indexing or keeping your money under your pillow and just burning a constant percentage of it every month. Part of why that’s a thing is that the data can be prodded, smooshed, or cherry-picked to say many different things, and so they are. I enjoyed this paper about mutual funds by Stanford GSB profs Jonathan Berk and Jules Van Binsbergen (NBER today here, SSRN in April here) in part for its discussion of data problems, which starts with the fact that they used the industry-standard (in the academic-papers-about-mutual-funds industry) CRSP database and compared it to Morningstar data because “even a casual perusal of the returns on CRSP is enough to reveal that some of the reported returns are suspect.” Suspect like:
We then compared the returns reported on CRSP to what was reported on Morningstar. Somewhat surprisingly, 3.3% of return observations di ffered. Even if we restrict attention to returns that di ffer by more than 10 b.p., 1.3% of the data is inconsistent. An example of this is when a 10% return is accidentally reported as “10.0” instead of “0.10”.
That is one way to get alpha. Anyway they look at the data using a (strangely) unusual metric of dollar value added, which is roughly alpha (gross excess return over some investable benchmark, in this case a Vanguard index fund) and multiplying it by assets under management, the intuition being that making 1% excess return on a $10bn portfolio is more impressive than doubling your $10 bet at the craps table. And they find that mutual fund managers are better than controlled money burning by the thinnest of margins: Read more »
One of the more fertile areas of academic finance is explaining why M&A is so bad – mergers seem to be on average value destructive, so why do they keep happening? Are CEOs just stupid? Are bankers just evil and persuasive? Here’s one answer that may be worth considering, which is that it looks like a good idea to acquire a successful company run by a talented and dedicated founder-CEO, but then things go pear-shaped because that founder-CEO either (1) departs, voluntarily or otherwise, with his giant bags of loot, or (2) suddenly loses interest in running his or her company when it’s owned by someone else and that someone else is now the founder-CEO’s boss:
Bidder gains are lower for acquisitions that involve targets with a founder CEO than for acquisitions of targets without a founder. The difference in bidder gains between takeovers of targets with founder CEOs and those without a founder is statistically significant, economically material, and robust to several model specifications that include a wide variety of deal- and firm-level control variables, like form of payment, competition, relative size, as well as some characteristics of the target CEO, such as his cash flow and voting stakes, age, and tenure.
That’s from this paper by Nandu J. Nagarajan, Frederik P. Schlingemann, Marieke van der Poel and Mehmet F. Yalin. It doesn’t clear up the whole mystery – non-founder mergers have also destroyed some value here and there, though I guess statistically less so – but I found it kind of fun. Read more »
This paper proposes a theoretically sound and easy-to-implement way to measure the systemic risk of financial institutions using publicly available accounting and stock market data. The measure models credit risk of banks as a put option on bank assets, a tradition that originated with Merton (1974). We extend his contribution by expressing the value of banking-sector losses from systemic default risk as the value of a put option written on a portfolio of aggregate bank assets whose exercise price equals the face value of aggregate bank debt. We conceive of an individual bank’s systemic risk as its contribution to the value of this potential sector-wide put on the financial safety net. To track the interaction of private and governmental sources of systemic risk during and in advance of successive business-cycle contractions, we apply our model to quarterly data over the period 1974-2010. Results indicate that systemic risk reached unprecedented highs during the years 2008-2010, and that bank size, leverage, and asset risk are key drivers of systemic risk.
A “theoretically sound and easy-to-implement way to measure the systemic risk of financial institutions” sounds pretty good! Is it easy to implement? Well let’s implement it to find out. [Pounds head against Google Spreadsheets.] Umm. Okay, I guess it was easy? I don’t know, I can’t fully replicate their numbers; tell me where I’m wrong in the comments. Or don’t. Read more »
There are probably some things that bankers could advise companies to do that are unequivocally bad. Obviously if I were Bank X’s Executive Director and Global Head of Lighting Money on Fire, and I went around showing companies a pitch book that was all “signalling benefits of lighting money on fire,” and I got a bunch of companies to do it, and it became a thing, and academics and industry groups did studies on it, I suspect that they would consistently report that the trade was NPV negative at a 1% level of significance. But maybe not, because, industry groups. Anyway though you can probably imagine some of these things existing outside of silly stylized examples – in hindsight, CDOs of mezz ABS look pretty close to lighting money on fire – but not too many of them. Because if a thing is always bad through the cycle then you’d quickly run out of people to do it, for some value of “quickly.”
Is it possible that mergers are such a thing?
No, it is not!
There, that was easy. Nor, obviously, are they the sort of thing that is unequivocally good – I suppose there are such things?* Instead, mergers are like, I dunno, hedge funds. You can ask the specific question of “will this merger be good for this company?,” and the answer will mostly be “maybe” but said with DCFs and stuff. Or you can ask the general question of “are mergers mostly good or mostly bad?” and the answer will be entertainingly indeterminate. Thus mergers are unequivocally good for academics, QED. Anyway this is a strange paper: Read more »
Let’s not stop there with the clichés.* Here’s a great one: “never attribute to malice that which can be adequately explained by stupidity.” In applied form: your model of all the AAA mortgage CDOs that were maybe not so AAA could be “ratings agencies were paid by banks so they were venal and corrupt and sold the banks good ratings on products they knew were bad.” Or it could be “ratings agencies created medium-dumb criteria to make a thing be AAA, and bankers who were smarter than medium-dumb arbed those criteria to make more things be AAA than should have been AAA.” The incentives model has good economic theory behind it, and some suggestive evidence; the stupidity model has that lovely cliché but also some evidence, about which more later.
Companies disclose favorable news just before CEOs leave for vacation and delay subsequent announcements until CEOs return, releasing news at an unusually high rate on the CEO’s first day back. When CEOs are away, companies announce less news than usual and stock prices exhibit sharply lower volatility. Volatility increases immediately when CEOs return to work. CEOs spend fewer days out of the office when their ownership is high and when the weather at their vacation homes is cold or rainy.
Except their ski chalets? I don’t know. Also this sentence should win some sort of award: “However, a bivariate probit model presented below indicates that news disclosures appear to be linked to CEOs’ vacations even after using weather variables to control for endogeneity of the vacation schedule.”*
The basic result is not that surprising; if I learned one thing from this paper it’s less “CEO activities are an important part of the short-term news relating to a stock” and more “it is possible to use vacation home property records and FAA databases to find out when CEOs visit their vacation homes, and that that is what your Stern tuition is paying for, which, okay!” Still here are two passages to maybe think about: Read more »