Three Nobel Lectures, and the Rhetoric of Finance

It was my great pleasure – and honor – to attend this year’s Nobel prize ceremonies. It started with the Nobel prize lectures, which I found very thought provoking.

Shiller 

I’ll work backwards, as it was thinking about Bob Shiller’s talk that taught me the biggest lesson. Preview: this will start pretty negative, but I learn a big lesson by the end. Hang in there, Shiller fans.

I thought I thought we had reached a consensus on volatility tests. Shiller (and others) brought us volatility tests, while Fama (and others), starting in 1975, showed that all sorts of returns are forecastable at long horizon. After sturm und drang, we – including Campbell and Shiller, but also a wider literature (I wrote a few papers) – realized that volatility tests are exactly, mathematically equivalent to return forecasting regressions. Expected returns (true measure) vary over time, a lot, and fully account for volatility tests.

The remaining question is whether time-varying expected returns are connected to macroeconomic quantities through marginal rates of transformation and substitution, or whether people misperceive probabilities and don’t know about time-varying expected returns.

There is a joint hypothesis theorem – probability and marginal utility always enter together in asset pricing formulas – so no amount of staring at prices will ever solve this interpretation question. We need models.  Economic models (such as habit persistence) give a somewhat successful answer, but are also rejected. The great challenge for behavioral finance is to produce similar, scientific - looking models that tie irrational expectations to other data in a rejectable way, and thus rise above ex-post story telling.

Volatility tests were a deeply important, Nobel-worthy part of this story. They showed the economic importance of time-varying expected returns – and the as yet incomplete effort to understand those returns – in a way that t stats and R2 values did not.

Well, that’s what I thought the consensus was. What I found remarkable is just how much of that consensus Bob completely abjured.

At 1:12 Bob starts right in:

What is a bubble? You [Gene Fama] said nobody defines it. So I will define it. A speculative bubble is a fad. People get excited sometimes. Too excited… Prices start going up, they start talking, the newspapers start writing about it, more and more people pile in to a market and they push prices up more and it goes on for a while. eventually it breaks and the bubble bursts.

That’s not a “definition.” That’s an explanation, a theory. A definition tells you in an operational way what pattern in the data describes “bubble.” An explanation is a theory that predicts the defined phenomenon.

That doesn’t answer Gene at all. Gene asked Bob how to measure a price above “fundamentals.” how to measure that a “fad” is underway? For example, in a previous podcast, Gene had offered to believe in bubbles if Bob could show a method that reliably forecast a negative market expected return.

Bob pointedly did not take even that olive branch, that chance to agree on a common language.  If we can’t get straight what a definition is vs. an explanation, maybe the physicists are right that they shouldn’t give out economics Nobels. We’ll surely be at this another 35 years.

Next, Bob put up an update of the famous volatility graph, where he contrasts actual prices with ex-post dividends discounted at a constant rate. (1:15:45)

He called the dividend line “the actual market if everyone knew the future” and the “true value.”

(A minor thought. Really? Would the world really be working right if that’s what stock prices had all the return and no risk? If we have an equity premium puzzle now, imagine what it would look like with no risk! If prices have no risk so we should discount dividends with riskfree rates, the major failure of today’s markets is not the volatility of the price-dividend ratio, it’s the level, which should be many times higher?)

Admitting briefly that efficient markets allow some return forecastability, he showed us some graphs discounting dividends with interest rates and consumption growth raised to a power.

On this evidence,  he concluded that we are  "seeing repeated fads and fashions" though they are “integrated with the economy” in a  way that is “difficult to understand.” Nonetheless, we can conclude that “The market is too volatile, people are a little crazy, there is a social psychological component.”

How do we we get from the failure of one model (constant expected returns, or power utility) to the failure of any possible model, to “people are a little crazy?”

More deeply, in the face of the joint hypothesis theorem, how do you get to claim victory for any view without a model at all?

More deeply, we’ve all been over and over this.  The subsequent literature answered all this years ago. How could Bob not know that or even mention it?

At 1:23, he described the Campbell-Ammer variance decomposition, concluding “only about a half or a third of the fluctuations in the stock market could be explained by evidence about future dividends,” and concluding, “so most of the market doesn’t make sense”

This was really revealing. Bob’s Campbell-Ammer slide says “excess [expected] return variation two to three times that of [expected] dividend innovation” His words were “most of the market doesn’t make sense!”

Add this up and it’s all eye-popping. Bob is basically denying the 20 year old theorem that volatility tests are equivalent to time-varying expected returns. I listened to the lecture and carefully to the video. You won’t find an admission of that theorem, or that mechanically time varying expected returns account for these plots. That’s especially astonishing given that the Nobel committee cited him for discovering long-run return forecastability, ignoring Fama’s role! For example the Nobel poster said

“Beginning in the 1960s Eugne Fama demonstrated that stock prices are extremely difficult to predict in the short run. .. If Fama’s results are right, then shouldn’t it be even harder to make predictions over several years? The answer is no, as Robert Shiller discovered in the early 1980s.”

Bob is denying the joint-hypothesis theorem that probability and marginal utility always enter together, so we need a model of either to say anything. And Bob is denying the essence of what it means to supply a definition.    

Bob closed with an overview of psychology and sociology concepts that inspire his views,

He urged economists to incorporate more ideas from psychology, sociology and other fields, “I think that in understanding speculative bubbles we have to be eclectic. .. population biology… epidemiology, neuro economics.. To understand complex phenomenal we need to take account of every kind of expertise.”

OK,  "listen to psychologists" is good advice. Economics has benefitted from intellectual arbitrage many times in the past. But Nobel prizes are supposed to be given for past successes (typically, long-past!) not “maybe you can do something with this in the future.”

In an entire lecture, Bob did not give a single concrete example of how “listening to psychologists” produces one concrete positive step to understanding “bubbles.”

(There was a lot more in Bob’s speech, including description of his innovative work with Case in  constructing a real estate price index. Curiously, he showed how today’s forward prices are forecasting another “bubble” – this market price correctly forecasts “fundamentals,” unlike all the others? And he closed,  advocating more markets, such as GDP futures, admitting they will have bubbles and fads too, but that they are useful anyway. “What I’ve done is present imperfect evidence…with the conclusion that’s maybe radically different about bubbles, but not about the general importance of our financial markets.”)

Deep Breath. Another view

It slowly dawned on me though, that this is much too harsh an evaluation and an unsatisfactory theory. Bob is a smart and thoughtful guy.  The theory that he doesn’t know the difference between a definition and an explanation, hasn’t read Fama’s 1970 definition of “efficiency” or “joint hypothesis,” doesn’t understand that volatility is exactly the same as return forecastability, and so on, just doesn’t make sense. I remembered my Kuhn (Structure of Scientific Revolutions) and McCloskey (Rhetoric of Economics). (If you’re an economist and haven’t read these, do so now.)

I realized just how deep and audacious  Bob’s project is. He is telling us to abandon the “scientific” pretense. He wants us to adopt a literary style, where we look at the world, are inspired by psychology, and write interpretive prose as he has done.  When he says that the definition of a a bubble is a fad, he isn’t being sneaky and avoiding the argument. He means exactly what he says and wants us to think and write this way too. A bubble, to Bob, is defined as any time a time that he, writing about it, informed by psychology, and reading newspapers, thinks a “fad” is going on. And he invites us to think and write like that too. A model is, to Bob, wrapped up in one person’s judgement and not an objective machine. If I complain that this is ex-post story telling, he might say sure, stop pretending to be physics, write ex-post stories. If I complain that there are no rules and that this is no better than “the gods are angry,” he might say, no, read psychology not ancient theology, and the rules are you have to couch your story telling in their terms. He does not want us to try to construct models, either psychological or rational, that make quantitative predictions.

He wants to fundamentally remake how we do finance, how we talk about finance, how we write about finance. He wants to define a new rhetoric of finance. When he says we should read psychology and social psychology – and, implicitly, not physics or economics – he means exactly what he says. He (obviously) isn’t going to fall in the trap of writing rejectable models, making predictions and so forth. That’s like speaking Greek, and at his party, we speak Latin.

I am by nature a listener, an integrator. I wrote a paper on how volatility tests are the same as Fama French regressions. Bob has no interest at all in listening or integrating. He wants to redefine how we do things in his own style, as pure and simple as possible.

This is what scientific revolutions are all about. This is what Nobel Prizes are all about.  They give them to people who strike out, write a novel language and methodology for conducting research, and convince others to follow and do it their way and talk their language. All previous revolutions – successful or not – have had these interminable debates where we can’t even seem to agree on the meaning of simple words (“efficiency,” “definition”, “model”) and talk past each other. The salient facts and classic tests are only written ex post by the winners. Bob wants a revolution of that sort, and listening to economists is the last way to accomplish it.

Now that is an audacious project! And Bob has collected a lot of people who talk and write his way.  Not me, so far – only one in ten attempted scientific revolutions catch on, and I’m placing my bets elsewhere. I still like to talk like a physicist. But I think I understand the audacity of the project, and why it is we seem to talk to cross purposes and not even agree on basic questions like what constitutes a definition, what’s a theorem, and whether the absence of quantitative rejectable behavioral models that tie expected returns to other data matters or not. And why trying to debate – to ask for a definition of bubble, for a quantifiable measure of “fundamentals”, to ask for a quantiative model of distorted expectations – will get nowhere.

Hansen

With that thought in mind, I came to a similar different view of Lars Hansen’s talk. Lars isn’t in the middle of Gene and Bob;  Lars is way off on the other end of Bob.

Lars chose to talk more about his current research and less about the research that got him the prize, a good technique for these lectures. He’s working on “ambiguity,” how to handle the fact that we don’t really know what the right model is, and, even more interestingly, how to construct models in which the people in the models don’t really know what the right model is. Typically for Lars, this is a very deep research program, which may lead to a fundamental difference in how we think about risk and information in economics.

At one point he described which he described models with  "twisted expectations.“  Here’s the slide

In the first equation S with a tilde on it represents marginal utility, consumption to the gamma power in the usual formulation, X represents an asset payoff, and Q is then the price. This is the standard present value formula – except Lars wants to think about E as a "distorted” expectation. Following the usual theorems, in the bottom equation we can represent the same idea with the real expectation and an extra M term multiplying the stochastic discount factor. (Yes, everyone else uses M for Lars’ S, and P for his Q.) This is essentially the risk neutral valuation trick, that we can introduce a new “discount factor” M to represent the probability “twist.”

Seeing this, I would have been tempted to position it between Gene and Bob. Gene thinks of “efficiency” with true or rational expectations E. Bob thinks of inefficiency as “fads” meaning irrationally optimistic and pessimistic expectations. But Bob doesn’t show us how to link those irrational expectations to data. So I would have said this M, which Lars’ models do link to data, is a structured way to incorporate the non-rational distorted expectations that Bob thinks he sees into models, but in a disciplined, rejectable way.

Lars didn’t do that. In fact, when I suggested he position the talk as halfway between the “rational” and “behavioral” debate in this way, he said something deep, to the effect of he wished the whole rational-behavioral debate would just go away. Since it hasn’t gotten far in 35 years, he has a point.

But with Shiller behind me, I now understand Lars’ goal better. Lars, just like Bob, is setting forth a pure rhetoric, a pure language, a pure methodology for how we should think about finance and do finance. As Bob wants it to look like social psychology or maybe literary criticism, Lars wants it to look like physics. We write down the model, formally, and carefully. We test the model. We do not spend any time on loosely written ideas, either “rational” or “behavioral.” We don’t spend time on “alternative explanations” as is common in empirical finance.  We don’t pretend that empirical work can say anything useful about whole classes of models, like “economic” or “rational” or “psychological.” In Lars’ world, the whole rational-irrational debate is a waste of time. Show us your models, or be quiet. A test can tell you something about this model, period. At best a summary statistic like the Hansen-Jagannathan bound can tell you “this is what discount factors produced by any model must behave,” but that’s it.

This too is how Nobel Prizes are won. And looked at empirically – how many followers he has collected who write in his style – this is a successful language too.

Fama

Which brings me at last to Gene Fama, who came first. Gene gave a straightforward talk on efficient markets, long run forecastability  and empirical finance. The one slight zinger was putting down some equations and citations to remind the world that indeed he started documenting long-run return forecasts in 1975. He apparently had some behavioral finance zingers in reserve, but didn’t get time to give them. The written version will be interesting.

Looked at in this rhetorical light, Gene can afford to be gracious. Gene also invented a language, a methodology, for empirical fiance. And his language and methodology did not just attract a small band of followers, but took over the finance profession, so thoroughly and completely that it’s easy to forget his influence. When Gene runs Fama MacBeth regressions, we run Fama MacBeth regressions – even if GLS might be more efficient, even if time series variation might be informative. When Gene sorts stocks into 10 portfolios, we sort stocks into 10 portfolios – even if 20 or smooth kernels might make sense. When Gene uses monthly returns, we use monthly returns. Gene writes beautiful paragraphs of prose to describe his theories, (no criticism, it’s just comparative advantage) so do we. When Gene defines terms like “efficiency” and “joint hypothesis” the rest of us use those definitions.  When Gene points out differences between empirical finance and empirical economics, perhaps there you can see just how strong the Fama language effect has been.

Mulligan on Obamacare Marginal Tax Rates

Casey Mulligan wrote a nice Wall Street Journal Oped last week, summarizing his recent NBER Working Paper (also here on Casey’s webpage) on marginal tax rates.

What do I mean, tax, you might ask. Obamacare is about giving people stuff, not taxing. Sadly, no. Obamacare gives subsidies that are dependent on income. As you earn more, you receive fewer subsidies for health care, reducing the incentive to earn more. Casey tots this sort of thing up, along with the actual taxes people will pay.

Economists use the word “tax” here and we know what we mean, but it would be better to call it “disincentives” so it’s clearer what the problem is, and just how painful we make it for poor people in this country to rise out of that poverty.

As you can see, the average marginal “tax” rate went up 10 percentage points since 2007, and about 5 percentage points due to Obamacare alone.

Going back to the working paper, I think this is actually an understatement. (Probably the first time Casey or I have ever been accused of that!)


First, not even Casey can add everything up, and it all adds in one direction. State and local taxes, and vast number of state, county, city and other income or asset-based transfers all add. I haven’t read all his papers or the whole book yet, but did Casey get them all? For example, I just got in the mail notice for a little program offered by the state of Illinois to lower your property taxes if you earn less than $100,000 per year. Nice, but one more little incentive not to earn more than $100,000 per year, and not in Casey’s calculation.

In email correspondence, Casey pulled me back from these thoughts in a way that is revealing about the calculation. I wanted to add sales tax. After all, if you earn a dollar, but you have to pay 10% sales tax to do anything with it, that’s another 10% distortion, no? Casey responds no, because he wants to measure the income-compensated distortion to labor, period. If you don’t work, and somehow you also get income, you still have to pay the 10% sales tax. So the sales tax does not distort that pure work-no work decision. Casey’s right, but I think this clearly illuminates the conservative nature of his calculation and what it means. There are a lot more margins, wedges, and distortions out there, and he’s not trying to measure them all. He’s also not trying to measure the wedges and disincentives for employers to hire people, towards non-market activities, and certainly not the effects of the regulatory tangle.

Another note of conservatisim: “The results account for the fact that many people will not participate in programs for which they are eligible.” This is an important issue, that at least had not sunk in for me until reading a recent CBO report. People don’t sign up for all the benefits to which they are eligible. If they did, marginal tax rates would be astronomical. It also sends a warning: Just how long will it be before people in an increasingly stagnant economy figure out all the programs they are eligible for?

Drawing a single line can also be unduly calming. You might say, “well 50% isn’t so bad. Europeans still work, sort of, paying 50% marginal tax rates.”  But as Casey reminds us, the spread in marginal tax rates across people is enormous. For example, the paper has a nice example (p. 13, table 2) of how a typical earner will come out ahead by choosing to work part time and receive subsidies, rather than work full time. This is a case of a 100% marginal tax rate.

It’s likely that the effect of marginal taxes is nonlinear. Much of the labor decision comes in chunks: work or don’t work; work part time or full time; apply for benefits or don’t, with transactions costs and irreversibilities.  Suppose half the population feels a 100% marginal tax rate and half feels zero. Half the population works, half does not. That is likely a much larger effect than if the whole population felt a 50% marginal tax rate.

In sum, it’s probably worse than even Casey’s graph. But Casey is doing the right thing in putting up a carefully documented graph and paper rather than speculating. Speculation is for blogs, not papers and not for good opeds.

And Casey’s big point remains the additional effect of Obamacare and other changes to Federal programs. Whatever you think the level is, it’s now 10 percentage points more than what it used to be. On average.

Health Insurance and Labor Supply

I just ran across an interesting paper, “Public Health Insurance, Labor Supply, and Employment Lock” by  Craig Garthwaite,  Tal Gross and my Booth colleague Matthew Notowidigdo.

They study an interesting event

… In 2005, Tennessee discontinued its expansion of TennCare, the state’s Medicaid system. … Approximately 170,000 adults (roughly 4 percent of the state’s non-elderly, adult population) abruptly lost public health insurance coverage over a three-month period.

The result was

a large and immediate labor supply increase….we find an immediate increase in job search behavior and a steady rise in both employment and health insurance coverage. 

They call the phenomenon “employment lock.” This is different from “job lock,” people with preexisting conditions who stay with jobs they didn’t want in order to keep health insurance. “Employment lock” is the choice by healthy people to work at all in order to get  insurance, or put in academic prose, “strong work disincentives from public health insurance that are unrelated to strict income-based eligibility limits.”

The converse is a new danger for the ACA

Additionally, our estimates may provide useful guidance regarding the likely labor supply impacts of the ACA…

If such individuals could instead acquire affordable health insurance apart from their employer, many of them would exit the labor force entirely. As a result of employment lock, policies that expand access to health insurance apart from employers (such as the ACA) may have large labor market effects

… Using CPS data, we estimate that between 840,000 and 1.5 million childless adults in the US currently earn less than 200 percent of the poverty line, have employer-provided insurance, and are not eligible for public health insurance.Applying our labor supply estimates directly to this population, we predict a decline in employment of between 530,000 and 940,000 in response to this group of individuals being made newly eligible for free or heavily subsidized health insurance. 

They are quick to point out that this is not necessarily a bad thing.“the effects do not necessarily imply a welfare loss for individuals choosing to leave the labor force after receiving access to non-employer provided health insurance.” If people only work at a job they hate in order to get health insurance, then people may be better off not working. The policy world often just assumes more employment is always a great thing, which isn’t true.

However, less employment is not necessarily a good thing either. These are childless adults. How are they supporting themselves if they don’t work? Can it possibly be optimal for them to just sit around the house? We surely don’t want to compare employer-provided health insurance with highly subsidized individual insurance for the unemployed– that’s a subsidy to leisure and obviously skewing the scales.

Most of all, low-income single people face extraordinarily high marginal tax rates and other disincentives to work. So, an artificial incentive to work in order to get health insurance may offset some of the otherwise irresistible incentives not to work. (A good calculation for Casey Mulligan!)

And whether the people are in the end better off working or staying home and receiving larger subsidies, the government and taxpayers are clearly worse off, as the people and their employers are not paying taxes any more.

In sum, academic caution aside, inducing a million childless adults to leave legal employment doesn’t look like a good thing to me.  

The evidence is pretty cool. Here are some pictures lifted from the paper.

The banker's new clothes -- review

I wrote a review of Anat Admati and Martin Hellwig’s nice new book, “The banker’s new clothes” for the March 2 2103 Wall Street Journal.

Bottom line: Banks should issue a lot more equity, a lot less debt, especially short term debt, and a heck of a lot less nonsense.

I admire Anat and Martin. The rest of us read the gobbledygook in the newspapers, chuckle at the faculty lunch – “Ha ha, xyz is CEO of a huge bank and has never heard of Modigliani-Miller! Ha Ha – pdq is a senior regulator, and doesn’t know the difference between capital and reserves!” – and then we go about our business. Anat and Martin have admirably taken the bull by the horns. They write opeds, they go to interminable banking policy conferences, they fight it out with bigwig bankers, regulators, and their consultant economists, and endure their scorn. This nice book summarizes their arguments very clearly (without the foaming at the mouth ranting and raving that I would have had a hard time avoiding in their place!)

(Links: This review at the Wall Street Journal (html), in a pdf from my webpage. Admati and Hellwig have a book website with lots of extra material and response to critics.)

Enough preamble. The review: 

Four and a half years ago, the large commercial banks nearly failed, inaugurating our great recession. They were saved by the Troubled Asset Relief Program, Federal Reserve lending and other government support. If you think all that was bad, imagine the ATMs going dark. What has been done to avoid a repetition of these events? Sadly, and despite all the noise you hear about bank regulation, not much.

The central problem, at the core of Anat Admati and Martin Hellwig’s “The Bankers’ New Clothes,” is capital.

In order to make $100 of loans, a typical bank borrows $97—from depositors, from money-market funds, from other banks, or from bondholders—and sells $3 of stock, its “capital.” So if only 4% of the bank’s loans fail, the shareholders are wiped out, and the bank cannot pay its debts. Worse, if there is a rumor that some loans are in trouble, creditors may “run,” each trying to get his money out first, and force a needless bankruptcy. Think of Jimmy Stewart in “It’s a Wonderful Life.”

When banks are on the brink, all sorts of other pathologies emerge. Bankers and their regulators may try to keep zombie loans on the books, hoping things will turn around. Or bankers may bet the farm on very risky loans that either save the bank or impose larger losses on creditors and the government. Ms. Admati and Mr. Hellwig explain all this nicely in their first few chapters.

The solution seems pretty obvious, no? Banks should fund their investments by selling a heck of a lot more stock and borrowing a heck of a lot less, especially in the form of run-prone short-term debt, as most other companies do.

Far more value was lost in the 2000 tech bust, for instance, than in the subprime mortgages that sparked the 2008 crisis, but the tech bust did not cause a financial crisis. Why? Tech companies were funded by stocks, not short-term debt. Worried shareholders can drive down the price of a stock, but they have no right to demand that the company redeem shares at yesterday’s price, so they can’t drive the company to bankruptcy in a run. Depositors and other short-term creditors have a fixed-value, first-come-first-serve promise from a bank—they can run.

More capital and less debt would stabilize the financial system in many ways. If a bank wants to rebuild its ratio of capital to assets from 1% to 2% by selling assets, it has to sell half of its assets. Doing so can spark a fire sale, especially if all the other banks are doing the same thing. If the same bank wants to rebuild capital from 49% to 50% of assets, it only has to sell 2% of its assets. That bank will also have a far easier time issuing more stock, rather than selling assets, which is a better way to build equity in the first place.

The U.S. government has instead addressed the risks of banking crises by guaranteeing bank debt. Guaranteeing debts creates perverse incentives, so our government tries to regulate the banks from taking excessive risks: “OK, cousin Louie, I’ll cosign the loan for your Las Vegas trip, but no poker this time, and be in bed by 10.”

Ms. Admati and Mr. Hellwig show how this approach has failed, repeatedly, over the course of many years—in the 1984 Continental Illinois rescue; in the Latin American debt crisis and savings-and-loan crisis in the 1980s; in the Asian-currency crisis and the collapse of Long-Term Capital Management in the 1990s; and in the recent financial crisis. Each time, our government bailed out more and more creditors in a wider array of institutions. Each time, our government wrote reams of new rules that banks quickly got around.

Now pretty much all of the big banks’ debt is guaranteed, explicitly or implicitly through the widely held expectation that a big bank’s creditors will be bailed out. But our regulators promise that next time, trust them, they really will spot trouble ahead and do something to stop it—even though our massive bank-regulation machinery failed to notice that subprime mortgages might be a bit risky in 2006 and even though, as Ms. Admati and Mr. Hellwig note, Europe’s regulators still consider Greek government bonds to be risk-free assets.

Most basically, Ms. Admati and Mr. Hellwig point out that current regulation is focused on a bank’s assets: the loans, securities and other investments that bring money in (and sometimes don’t). They want us to focus instead on the bank’s liabilities: the ways banks get money and the promises banks make to depositors and investors. Bank assets are not particularly risky or illiquid. Apple’s profits from selling iPhones or a mutual fund’s portfolio of stocks are far riskier than any bank’s portfolio of loans and mortgage-backed securities, or even their much-disparaged trading books. Bank liabilities—too much debt and too much short-term debt—are the central problem that causes financial crises.

What about those “tough” new capital regulations that you keep reading about? They are not nearly as tough as you think. At best, the new Basel III international bank regulation agreement calls for a 7% ratio of capital to assets by a leisurely 2019 deadline. But that is the ratio of capital to “risk-weighted” assets. Risk-weighting is a complex system in which some assets count less against capital requirements than others. A dollar of mortgage assets might count as 50 cents, but it might count as 10 cents or less if it is a complex mortgage-backed security, and zero if it is government debt. When Ms. Admati and Mr. Hellwig unravel those “risk weights,” we’re still talking about 2% to 3% actual capital.

Foreseeing the usual risk-weighting games, Basel III requires a backstop 3% ratio of equity to all assets. “If this number looks outrageously low,” Ms. Admati and Mr. Hellwig write, “it is because the number is outrageously low.” Indeed.

This simple truth has been met by howls of protest and layers of obfuscation and derision by bankers, their consultants and many of their regulators. “Oh, you just don’t understand the complexities of banking” is the basic attitude. “Go away and let the experts fix this.” Well, Ms. Admati and Mr. Hellwig, top-notch academic financial economists, do understand the complexities of banking, and they helpfully slice through the bankers’ self-serving nonsense. Demolishing these fallacies is the central point of “The Bankers’ New Clothes.”

No, they write, it was not always thus. In the 19th century, banks funded themselves with 40% to 50% capital. Depositors wouldn’t lend to banks unless the banks had a lot of skin in the game. Without a government debt guarantee—and, early on, without limited liability—shareholders wanted less risk as well.

“Capital” is not “reserves,” and requiring more capital does not reduce funds available for lending. Capital is a source of money, not a use of money. When, as Ms. Admati and Mr. Hellwig gleefully note, the British Bankers’ Association complained in 2010 about regulations that would require banks to “hold"—the wrong verb—"an extra $600 billion of capital that might otherwise have been deployed as loans to businesses or households,” it made an argument both “nonsensical and false,” contradicting basic facts of a bank balance sheet. Requiring more capital does not require banks to raise one cent more money in order to make a loan. For every extra dollar of stock the bank must issue, it need borrow one dollar less.

Capital is not an inherently more expensive source of funds than debt. Banks have to promise stockholders high returns only because bank stock is risky. If banks issued much more stock, the authors patiently explain, banks’ stock would be much less risky and their cost of capital lower. “Stocks” with bond-like risk need pay only bond-like returns. Investors who desire higher risk and returns can do their own leveraging—without government guarantees, thank you very much—to buy such stocks.

Nothing inherent in banking requires banks to borrow money rather than issue equity. Banks could also raise capital by retaining earnings and forgoing dividends, just as Microsoft MSFT +0.54% did for years. Every dividend drains capital from banks and removes a layer of protection between us taxpayers and the next bailout. Ms. Admati and Mr. Hellwig are at their best in decrying U.S. regulators’ decision to let banks pay dividends in 2007-08—amounting to half the TARP bailouts—and to let big banks begin paying out dividends again in 2011.

Why do banks and protective regulators howl so loudly at these simple suggestions? As Ms. Admati and Mr. Hellwig detail in their chapter “Sweet Subsidies,” it’s because bank debt is highly subsidized, and leverage increases the value of the subsidies to management and shareholders. To borrow without the government guarantees and expected bailouts, a bank with 3% capital would have to offer very high interest rates—rates that would make equity look cheap. Equity is expensive to banks only because it dilutes the subsidies they get from the government. That’s exactly why increasing bank equity would be cheap for taxpayers and the economy, to say nothing of removing the costs of occasional crises.

And, in an all-too-short chapter on “The Politics of Banking,” they show us how politicians and regulators like the cozy cronyism of the current system. Banks are, of course, “where the money is,” and governments around the world use regulation to direct funds to politically favored businesses, to preferred industries, to homeowners and to the government itself. Politicians want to subsidize and protect their piggy bank. Regulators commonly become sympathetic to the interests of the industry they regulate, which advances their careers in government or back in industry. Last week’s news coverage of Treasury Secretary Jack Lew’s interesting career is only the most recent reminder.

Part of me wishes that Ms. Admati and Mr. Hellwig had been more specific in their criticisms: naming more names and quoting more nonsense, writing a gripping exposé dripping with their justified outrage. But their restraint is wise: Too much exposé would detract from the clarity of their ideas. So readers will have to recognize the arguments and add their own outrage.

Ms. Admati and Mr. Hellwig do not offer a detailed regulatory plan. They don’t even advocate a precise number for bank capital, beyond a parenthetical suggestion that banks could get to 20% or 30% quickly by cutting dividend payments. (I would go further: Their ideas justify 50% or even 100%: When you swipe your ATM card, you could just sell $50 of bank stock.)

But this apparent omission, too, is a strength. A long, detailed regulatory proposal would simply distract us from the clear, central argument of “The Bankers’ New Clothes”: More capital and less debt, especially short-term debt, equals fewer crises, and common contrary arguments are nonsense. More capital would be far more effective at preventing crises than the tens of thousands of pages of Dodd-Frank regulations and its army of regulators, burrowed deep in the financial system, on a hopeless quest to keep highly leveraged and subsidized too-big-to-fail banks from taking too much risk. Once the rest of us accept this central idea, the details fill in naturally.

 How much capital should banks issue? Enough so that it doesn’t matter! Enough so that we never, ever hear again the cry that “banks need to be recapitalized” (at taxpayer expense)!


(Update in response to a lot of comments. C'mon, this is a review of a book about banks. It’s not my place here to expand the discussion to GSEs’ CRA, the run on repo and broker dealers, money market funds etc. On the ATM card that sells bank stock: That card can also sell a share of your S&P500 index. And if you want stable value accounts, money market funds that hold only short term treasuries can provide all the fixed-value assets we could possibly want.)

More new-Keynesian paradoxes

Last week I saw Johannes Wieland’s paper “Are negative supply shocks expansionary at the zero lower bound?"  A side benefit of the job market season is that we see interesting new papers like this one, and it contributed to my project of trying to better understand new-Keynesian models.

Though starting academic papers with blog quotations is usually a bad idea, Johannes starts with a great and very appropriate one,

As some of us keep trying to point out, the United States is in a liquidity trap: […] This puts us in a world of topsy-turvy, in which many of the usual rules of economics cease to hold. Thrift leads to lower investment; wage cuts reduce employment; even higher productivity can be a bad thing. And the broken windows fallacy ceases to be a fallacy: something that forces firms to replace capital, even if that something seemingly makes them poorer, can stimulate spending and raise employment.” -Paul Krugman

I endorse this quote, because it is an accurate and pithy description of the properties of many careful new-Keynesian analyses in the academic literature.

 Johannes explains

Does destroying productive capacity raise output when the zero lower bound (ZLB) binds? [ZLB: When interest rates are zero, the Fed can’t lower them any more in response to shocks -JC] While this question may seem absurd, in fact it is a common prediction of many macroeconomic models: In these models, temporary negative supply shocks raise inflation expectations and lower expected real interest rates at the ZLB, which stimulates consumption and output. While some prominent economists have subscribed to this view and its policy implications (e.g., Eggertsson and Woodford [2003], Eggertsson and Krugman [2011], Eggertsson [2012]), there is wide disagreement over such a radical and unintuitive proposition.

Indeed there is.

These are just the beginning of the strange predictions new-Keynesian models (or modelers) make.

"Fiscal stimulus” is the prediction that even completely wasted government spending is good for the economy. Paul Krugman recommended, with refreshing clarity, that the US government fake an alien invasion so we could spend trillions of dollars building useless defenses. (I’m not exactly sure why he does not call for real defense spending. After all, if building aircraft carriers saved the economy in 1941, and defenses against imaginary aliens would save the economy in 2013, it’s not clear why real aircraft carriers have the opposite effect. But I’m still working on the nuances of new-Keynesianism, so I’ll let him explain the difference. I’m not a big fan of huge defense spending anyway.)

Furthermore, all the new-Keynesian models are “Ricardian.” They predict the same stimulus whether spending is financed by borrowing or by lump-sum taxes  today. Good, we don’t need to argue about “Ricardian equivalence,” but to believe their predictions for spending borrowed money, you have to believe that taxing you and me a trillion dollars and spending it on a trillion dollars of alien defenses will raise overall output by 2, 3, or 4 (you can get really big multipliers in these models) trillion dollars.

Actually, stimulus financed by temporary payroll taxes can be even better than from borrowing money. These are a negative supply shock, which causes inflation and lowers the real interest rate. Sand in the gears is good. Stimulus financed by temporary consumption taxes is worse, because that encourages saving. Promises of higher future consumption taxes, anathema in the standard view of the world, are good, as they get people to consume today.

Super-weirdly, many new-Keyensian paradox predictions get worse as the central friction, price stickiness, gets better.

Johannes again on the new-Keynesian paradoxes:

First, according to the “Paradox of Thrift,” a rise in the desire to save is self-defeating at the ZLB, because it reduces output so much that aggregate savings fall (Keynes [1936], Krugman [1998], Eggertsson and Woodford [2003], and Christiano [2004]). Second, according to the “Paradox of Flexibility,” output volatility may rise at the ZLB when prices and wages are more flexible (e.g., Werning [2011], Eggertsson and Krugman [2011]).

My empirical results concern primarily the “Paradox of Toil” (Eggertsson [2010]), whereby a temporary increase in desired labor supply at the ZLB reduces the equilibrium employment level in standard models. …. Following this logic, payroll tax cuts are contractionary at the ZLB because they lower expected inflation (Eggertsson [2011]), and allowing collusion among firms is expansionary because it raises expected inflation (Eggertsson [2012]).

A pause in praise of economic models: They tie ideas together. You can’t pick and choose. If you like stimulus with borrowed money, but suspect that tax-financed stimulus might not work so well, you can’t just waive your hands and refer to new-Keynesian models to defend you. These models predict the two policies have the same effect. If you like your stimulus, but think that maybe hurricanes wiping out a bunch of the capital stock isn’t great, sorry, you can’t refer to new-Keynesian models to defend you. If you don’t buy one of Krugman’s assertions, you don’t buy any of them. (At a minimum, you have to build a new variant of model – you can’t refer to existing new-Keynesian models to defend you.) To taste fish, you have to swallow the whole whale, hook, line, and sinker.

So, back to Johannes. He notes that the models predict quite different behavior away from the bound than at the bound, so conventional estimates don’t really tell us that much about whether these predictions are true. But we have enough experience with economies at the lower bound now, that we can begin to test some of these astonishing predictions.

(Minor suggestion for PhD students. The key requirement for these predictions is that the Fed does not change the nominal interest rate in response to shocks. There have actually been other periods of time when central banks have fixed nominal interest rates, for example between 1945 and 1952 in the US. More generally, the general new-Keynesian view is that interest rates did not respond enough to shocks before 1980. So in fact, versions of the paradoxes should be visible in data away from the zero bound.)

Johannes looks at the earthquake in Japan, and oil price shocks. Surprise, surprise, earthquakes are bad for output. More subtly, the new-Keynesian prediction flows through inflation: “Supply shocks” should raise expected inflation, which lowers real interest rates, and lower real interest rates should raise consumption and output. (As I explained last week, new-Keynesian models anchor expected consumption in the far off future. Then real interest rates determine the growth rate of consumption, and higher growth means a lower level today. In the models consumption=output. See Johannes’ equation 2 page 7.) Johannes finds that the supply shocks led to higher expected inflation, and hence a lower real rate. But the lower real rate just didn’t have the predicted effect on output. In fact, he finds that oil shocks have worse negative effects on employment at the zero bound than in normal times!

Like all provocative empirical work, I’m sure this one will be picked over. The Booth Macroeconomics workshop did its usual good job of exploring nooks and crannies. But let us also pause in praise of serious empirical work. Rather than blurt “this is ridicuous!” let us go see if indeed earthquakes, hurricanes, labor market restrictions, oligopolization and other normally adverse “supply” shocks actually help the economy. The sun might just come up in the West at the zero bound.

Where to go from here? If I had this great introduction, and results that rather decisively reject a central night-is-day new-Keynesian proposition, clearly linked to all the others, I would obviously have been tempted to write it up as “this model is wrong,” and dig deep into which key assumptions of the model drive its basic mistakes. Johannes takes another tack, and adds credit constraints to the model. Whether this is a successful repair or a clever epicycle I will leave for another time – and frankly I haven’t studied it closely enough to opine yet.  How many of the paradoxes it overturns is another good question. It seems to overturn quite a few. But the paradoxes are also the sexy policy implications.  It may save new-Keynesian models from their prediction that hurricanes are good, by destroying the new-Keynesian multiplier.

NBER Asset Pricing conference

I spent Friday at the NBER Asset Pricing conference in Palo Alto. All the papers were really good, and the discussions were especially thoughtful. Here are a few highlights that blog readers might like.

There’s no better way to wake up than with a good puzzle. Emanuel Moench presented his paper with David Lucca,The Pre-FOMC Announcement Drift.(If these links don’t work for you, most papers can be found with google.)

Here are average cumulative returns on the S&P 500 in the day preceding scheduled FOMC announcements (when the Fed says what it will do with interest rates). The grey shaded areas are 2 standard error confidence intervals. The S&P500 drifts up half a percent in the day before FOMC announcements!  In fact, 80% of the total return on the S&P500 over this period was  earned on these days.

So what the heck is this? Obviously, there was some disssection that it is spurious. Stocks are so volatile that it’s easy to find 3 days that account for 80% of its total return, let alone a few hundred. But they did not fish.

I am still a little worried – there are two big positive outliers in the distribution of returns (Figure 2) and a missing left tail. If we had two more such outliers on the negative side, would those confidence intervals get bigger? Did options markets know that the left tail is missing?  The pattern is there for international stocks before US announcements, but not in bond markets. But Annette Vissing-Jorgenson, setting the style for the day, dissected it every which way and didn’t get rid of it, so discussion moved on.

It’s not volatility (higher volatility might generate a higher mean return) – realized volatility is lower in these periods than other periods. And volume is lower too – see at left.

This observation generated what I’ll call the consensus of the room: Lots of equity traders sit out or hedge their positions in advance of this day. (Confirmed by people in the room who talk to such traders.) Anyone trading on the morning before an FOMC announcement is suspected of being “informed,” which makes markets less liquid. So the risk is concentrated and held by a narrower group.

Emanuel answered that they did regressions including these and all sorts of liquidity measures, which didn’t get rid of the puzzle. But when you do that, you assess how much expected return premium corresponds to illiquidity by the correlation of returns with liquidity on other days, and there are all sorts of reasons to think this measurement underestimates the effect. Anyway, as a fan of facts linking trading to pricing, it’s a great paper. (And a good hint to PhD students: make sexy graphs like these.)

Annette also brought up the issue, should journals publish papers that just pose well-documented puzzles, without offering (usually lame – my view) theory or explanation? I think this paper makes a hearty case for “yes!”

Xiaoji Lin presented his paper with Jack Favilukis Wage Rigidity: A Solution to Several Asset Pricing Puzzles. How can I make a general equilibrium model with adjustment costs and wage rigidity sexy for a blog?

Well, this one is. “Standard” real-business cycle models drive the economy with productivity shocks. When there is a good such shock, investment and output go up, and people work harder. But, the marginal product of labor goes up (that’s why they work harder), so wages go up. Since wages go up, profits don’t go up that much, and equity isn’t that risky. By putting sticky wages in the model, now wages are like a bond payment, so the firms profits are leveraged, making them more risky. This helps to fit a broad range of asset pricing facts. I’m especially impressed that the model generates a spread of value vs. growth firms (hard to do) and a value premium.

The impulse-responses at left show the basic idea. You’re looking at responses to technology shocks (growth in technology follows an AR(1), so there is some technology momentum.) You see wages rising quickly in the “standard” model to match the higher productivity. You see profits much more affected in the middle when wages can’t adjust.

Lots of discussion here. Of course “stickiness” is an abstraction for all the interesting things that labor/macro people put in their models. One good comment, wages are not “smoothed,” they’re “screwed” – workers don’t get the present value of the  marginal product increase (or feel it if a decrease) as they might under an intertemporal smoothing contract. That likely has a big effect on the value of stocks.

The last one I’ll mention (they were all great, just running out of steam here) Erkko Etula and Tyler Muir presented their paper with Tobias Adrian on “Financial Intermediaries and the Cross Section of Asset Returns”

They construct shocks to broker-dealer leverage from the flow of funds, and then construct a single-factor asset pricing model, expected excess return = beta on broker-dealer leverage shocks times lambda. Here it prices the  size and value portfolios, momentum portfolios, and bond portfolios! All with a single, economically motivated factor!

 Much discussion (of course). One possibility, which I called the “AQR theory of asset pricing.” Suppose you look at the portfolio of one trader, who is invested in value, momentum, small, and term risk. The the wealth, and (if borrowing is pretty constant) leverage of that agent will be a good pricing factor for those anomalies.  So just because leverage works well does not necessarily prove the usual causal story, that these broker-dealers are “marginal,” they get in to trouble sometimes and then start selling securities in “fire sales,” etc.  If they sell, after all, someone else must buy, so they’re “marginal” too. Much good discussion on the facts too, with great graphs by Bryan Kelly showing that it is a bit unstable over different samples.

Lubos Pastor presented his paper with Pietro Veronesi “Political Uncertainty and Risk Premia” with a great discussion by Nick Bloom showing us the latest of his uncertainty index. Welcome to the Krugman-thinks-you’re-a-moron club, Nick.

Snehal Banerjee presented his paper with Jeremy Graveline, “Trading in Derivatives When the Underlying is Scarce”, really interesting (especially to me, given writing on the 3 com / palm issue) with Nicolae Garleanu discussing.

 Chris Polk, presented his paper Dong Lou “Comomentum: Inferring Arbitrage Capital from Return Correlations” with a great discussion by Robert Novy-Marx. They find that momentum works when the pairwise correlations of momentum stocks are low, indicating the trade is “less crowded,” and conversely.

All cool stuff, but but the plane is landing. (Thanks to glamorous Southwest airlines for onboard wifi.)