So quickly we forget...

One of the lessons we'd supposedly learned from the financial crisis is the need to look seriously at systemic risks -- risks not tied to the well-being of just any one institution, but which emerge out of their interconnections and interdependence. Systemic risk is the new buzz phrase for research on banking network stability. The Dodd-Frank financial reforms still under negotiation aim to improve systemic stability (in some small way, at least), in part by raising capital requirements on individual banks.

It's doubtful this will be enough. As a number of studies (notably this one) have shown (see further comment below), the propagation of distress through a banking network depends on more than the situation facing each individual bank -- the density of links between banks matters too. More links of financial dependence between banks can share risks and make a network more stable, in some cases, but they can also make the system more fragile. So raising capital requirements isn't enough. Still, it's at least a step in the right direction.

After all, if there's one thing we've learned about the risks linked to rare fat-tail-associated crises is that years of apparent profitability can be wiped out in a few tumultuous weeks. Two years ago financial writers couldn't repeat often enough what they'd learned from Nassim Taleb's Black Swan -- that there's real danger in pushing up leverage and ignoring those tail risks in pursuit of short term profits, and that "profitability," sensibly considered, has to refer to the long term. Now they have apparently forgotten, as this article in today's New York Times makes clear. Bank stocks are down. They're worried about all the new regulations eating into their profits. Especially those pesky capital requirements:

... new international rules now being developed to require major institutions to hold more capital as a buffer against future financial crises will also erode profitability. That is because money set aside as ballast is cash that will not be available to lend out or pay dividends or buy back stock.

This is only true if you take "profitability" in a special, narrow, short term sense. That money set aside as ballast might just enable the bank to survive tough times in the future, and might also make the entire banking network itself more stable, as well as the broader economy. The kind of "profitability" this paragraph is talking about isn't what we need.

Coincidentally, the paper I mentioned above -- which studies a model network of interdependent banks -- explores a scenario that economist Mark Thoma describes quite beautifully (without apparently having it in mind, though perhaps he did). The point is that there's an interesting trade-off we don't understand very well between the increasing safety that comes from diversification as the network grows more dense, and the simultaneously increasing danger of cascading distress which dense ties make more likely. As Thoma puts it,

When I think of an interconnected network that can spread problems from institution to institution, there are two possible scenarios. First, think of a drop of poison in the ocean. The ocean is so big that even a powerful poison can be neutralized as it spreads.... This is much like traditional financial risk sharing where large individual losses are spread throughout the system so that the losses to any one person are tiny.

But now think of a poison that acts more like an infection. As it spreads it does not become less toxic, it continues to be lethal to anyone who comes in contact with it. In this case, you want to break the network connections -- i.e. compartmentalize -- as soon as possible to prevent the spread of the lethal infection. You may even want to have the compartments set in advance if you cannot sever the ties fast enough.

Man Bites Dog and Couple "Forecloses" on Bank

Here's a classic role reversal story - a couple foreclosed on a bank! It started about five months ago when Bank of America attempted to foreclose on a Florida couple for non-payment of their mortgage.

The problem was, they'd paid cash for the house. So they went to court, and eventually won (they showed that they'd never had a mortgage with BOA).

The judge awarded them legal fees, but after five months, BOA somehow never got around to paying the judgement.

So, the couple's attorney got the sheriff, seized their assets and padlocked the bank branch building. The attorney gave instructions to remove assets like computers, desks, copiers, and any cash in the tellers' drawers. After about an hour of being locked out of the bank, the bank manager handed the attorney a check for the legal fees.

Talk about turnaround.

Read the article here.

update: It wasn't technically a "foreclosure" on the bank - it was actually a default judgment for unpaid legal fees and court costs. But close enough for the irony.

The trouble with today's macroeconomics...

This lengthy post by Willem Buiter -- currently the Chief Economist for Citibank -- is now a couple years old, but it touches on many shortcomings of current academic economics modeling based on the notion of equilibrium, excessive assumptions about rationality, inadequate attention to non-linearities and so forth. I list a few choice excerpts below, but the whole thing is very much worth a read.

On the value of the rational expectations revolution which has dominated mainstream macroeconomic modeling for the past 30 years:

... the typical graduate macroeconomics and monetary economics training received at Anglo-American universities during the past 30 years or so, may have set back by decades serious investigations of aggregate economic behaviour and economic policy-relevant understanding. It was a privately and socially costly waste of time and other resources.

Most mainstream macroeconomic theoretical innovations since the 1970s (the New Classical rational expectations revolution associated with such names as Robert E. Lucas Jr., Edward Prescott, Thomas Sargent, Robert Barro etc, and the New Keynesian theorizing of Michael Woodford and many others) have turned out to be self-referential, inward-looking distractions at best.  Research tended to be motivated by the internal logic, intellectual sunk capital and esthetic puzzles of established research programmes  rather than by a powerful desire to understand how the economy works...  

 On the matter of mathematical assumptions turning up in dynamic programming problems, and their unjustified inclusion as assumptions about the behaviour of real people in real markets:

The common practice of solving a dynamic general equilibrium model of a(n) (often competitive) market economy by solving an associated programming problem, that is, an optimisation problem, is evidence of the fatal confusion in the minds of much of the economics profession between shadow prices and market prices and between transversality conditions that are an integral part of the solution to an optimisation problem and the long-term expectations that characterise the behaviour of decentralised asset markets.  The efficient markets hypothesis assumes that there is a friendly auctioneer at the end of time – a God-like father figure – who makes sure that nothing untoward happens with long-term price expectations or (in a complete markets model) with the present discounted value of terminal asset stocks or financial wealth. 

... The future surely belongs to behavioural approaches relying on empirical studies on how market participants learn, form views about the future and change these views in response to changes in their environment, peer group effects etc.  Confusing the equilibrium of a decentralised market economy, competitive or otherwise, with the outcome of a mathematical programming exercise should no longer be acceptable.

 On the systematic obliteration of non-linearities and positive feed backs in today's macro models, even in the face of obvious empirical evidence for their importance:

If one were to hold one’s nose and agree to play with the New Classical or New Keynesian complete markets toolkit, it would soon become clear that any potentially policy-relevant model would be highly non-linear, and that the interaction of these non-linearities and uncertainty makes for deep conceptual and technical problems. Macroeconomists are brave, but not that brave.  So they took these non-linear stochastic dynamic general equilibrium models into the basement and beat them with a rubber hose until they behaved.  This was achieved by completely stripping the model of its non-linearities and by achieving the transsubstantiation of complex convolutions of random variables and non-linear mappings into well-behaved additive stochastic disturbances.

Those of us who have marvelled at the non-linear feedback loops between asset prices in illiquid markets and the funding illiquidity of financial institutions exposed to these asset prices through mark-to-market accounting, margin requirements, calls for additional collateral etc.  will appreciate what is lost by this castration of the macroeconomic models.  Threshold effects, critical mass, tipping points, non-linear accelerators – they are all out of the window. ...The practice of removing all non-linearities and most of the interesting aspects of uncertainty from the models that were then let loose on actual numerical policy analysis, was a major step backwards.  I trust it has been relegated to the dustbin of history by now in those central banks that matter.

 Finally, on the usefulness of so-called Dynamic Stochastic General Equilibrium models, the current state of the art of the macroeconomics community:

Charles Goodhart, who was fortunate enough not to encounter complete markets macroeconomics and monetary economics during his impressionable, formative years, but only after he had acquired some intellectual immunity, once said of the Dynamic Stochastic General Equilibrium approach which for a while was the staple of central banks’ internal modelling: “It excludes everything I am interested in”. He was right.  It excludes everything relevant to the pursuit of financial stability.

Universal switching patterns in market trends

One of the more intriguing simple facts about market fluctuations is their self-similarity in time. The statistics of market fluctuations over a short time, if stretched out in time and rescaled in value, look identical to fluctuations over longer times. What's happening over seconds is somehow intimately linked up with what's happening over hours, days or months.

This geometric order follows from no end of modern studies of the statistics of market returns, which find power laws all over the place. Mandelbrot himself gave a nice exploration of self-similarity in market movements more than a decade ago in the wake of the meltdown of Long Term Capital Management, which was caused in part by an inadequate appreciation of the likelihood of sudden large fluctuations in any system with fluctuations of this sort. Now everyone knows about Black Swans and fat tails, but there may be much deeper secrets lurking in the delicate structure of market ups and downs.

That's the contention at least of Tobias Preis and colleagues in a recent paper in the Proceedings of the National Academy of Sciences. It is not by any means an easy study to digest and understand, but it seems quite important as it hints at some deep regularities in the way markets switch between upward and downward trends over a huge range of timescales from seconds up to years. Indeed, it suggests that some very fundamental collective pattern of human decision making -- as individuals sense the shifting views of others and become more likely to act in the same way -- lie behind many market rallies and crashes, regardless of the timescale on which they play out.

Briefly, what Preis and colleagues have done is to analyze several different data sets for price movements in markets on times ranging from 10 milliseconds (high frequency trading in futures markets) up to around 10 billion milliseconds, or several decades (the S&P Stock Market Index over a 40 year period). Their analysis focuses on what they refer to as "switching events" -- an upward trend which breaks off into a negative one, or vice versa. These can be identified (or defined) in a mathematically unambiguous way. Focus your microscope in on a timescale of duration Δt. The price is bouncing up and down, with p(t) giving the value versus time. The researchers then define p(t) to be ...

a local maximum of order Δt if there is no higher transaction price in the interval t − Δt ≤ t ≤ t + Δt, and is defined to be a local minimum of order Δt if there is no lower transaction price in this
interval.

In other words, every interval must have both a maximum and a minimum, and these are taken as potential switching points - moments at which the market moved from a trend in one direction to another. They go on to study the volume of individual trades in the market record as you move closer to or further away from these switching points. The payoff is that they find essentially the same mathematical pattern across all time scales, even when moving between the futures market and the stock market -- in effect, a signature of switching points that shows up in the volume of trades and predicts approaching changes.

The basic pattern can be expressed in simple formulae, but the idea comes across even better in the diagram below. It shows how the volume of individual trades increases as the moment of a switching point approaches. Remember, this isn't just at one timescale, but is averaged over many timescales (this figure is for data at shorter times taken from a futures market):

I apologize for the resolution, but you can see the basic point -- the middle of the diagram is the moment of a switching point, and the volume per trade as one approaches this point becomes very large. That is, people start trading in larger and larger amounts as you approach these points. They do so before the switch actually happens. So their behaviour is, in effect, an early warning sign of an impending switch in the trend.

This is a highly provocative finding. The paper also explores some models inspired from physics which might explain how such a universal pattern can occur. In essence, the kinds of models that work reflect the avalanche behaviour as the actions of some people feed into and influence the actions of others. On any timescale, the same kind of dynamic may be at work -- many people follow a trend for a time, then, a few become wary of a reversal and act accordingly. Others begin sensing those peoples' actions and follow suit. A growing avalanche of behaviour reverses the trend -- and simultaneously launches one off in the opposite direction.

As Preis and colleagues conclude,

In summary, we have seen that each trend—microtrend and macrotrend—in a financial market starts and ends with a unique switching process, and each extremum shares properties of macroscopic cooperative behavior. We have seen that the switching mechanism has no scale, for time scales varying over nine orders of magnitude down to the smallest possible time scale —the scale of single transactions measured in units of 10 ms. Thus, the well known catastrophic bubbles occurring on large time scales—such as the most recent financial crisis—may not be outliers but in fact single dramatic events caused by the inherent, scale-free behavior related to the formation of increasing and decreasing trends on time scales from the very large down to the very small.

We might call this Self-Similarity 2.0 for market fluctuations, certainly inspired by Mandelbrot's discoveries of 50 years ago, but also going well beyond.

How Much IS A College Degree Worth?

Here's an interesting chart from Lifehacker. It shows the median salaries (along with the 25th and 75th percentiles for various majors. It's taken from a report created by the Georgetown University Center on Education and the Workforce called "What’s it Worth? The Economic Value of College Majors"

Here's an interesting part from the press release Georgetown put out:

The top 10 majors with the highest median earnings are:
Petroleum Engineer ($120,000); Pharmacy/pharmaceutical Sciences and Administration ($105,000); Mathematics and Computer Sciences ($98,000); Aerospace Engineering ($87,000); Chemical Engineering ($86,000); Electrical Engineering ($85,000); Naval Architecture and Marine Engineering ($82,000); Mechanical Engineering, Metallurgical Engineering and Mining and Mineral Engineering (each with median earnings of $80,000).

The 10 majors with the lowest median earnings are:
Counseling/Psychology ($29,000); Early Childhood Education ($36,000); Theology and Religious Vocations ($38,000); Human Services and Community Organizations ($38,000); Social Work ($39,000); Drama and Theater Arts, Studio Arts, Communication Disorders Sciences and Services, Visual and Performing Arts, and Health and Medical Preparatory Programs (each at $40,000).

This isn;t surprising - with the exception of Pharmacy (which is also pretty rigorous and exacting), they're all fields that require a lot of math. To quote classic Barbie, "Math is Hard!". So there's a smaller supply of grads (and there's a pretty good demand for these grads, too).

How has the crisis changed the teaching of economics?

The Economist has organized a discussion on how the teaching of economics may change in the wake of the recent crisis. Around 15 prominent economists have weighed in, with few suggesting anything radical. Harvard's Alberto Alesina suggests that indeed nothing so far has changed:

As for the methods of teaching and research nothing has changed. We kept all that is good about methods in economics: theoretical and empirical rigor. But one may say we kept also what is bad: a tendency to be too fond of technical elegance and empirical perfection at the expense of enlarging the scope of analysis and its realism. Those who found our methodology good should not worry about changes. Those who did not like it should not hold their breath for any sudden change due to the crisis.

But some of the others suggest that things are changing, and that the crisis has at least stimulated a renewed interest in economic history. Indeed, for all the consternation that economists didn't see this crisis coming, and didn't predict it, this isn't really the most surprising thing about the crisis. More surprising is how many economists seemed convinced that an event of this magnitude simply couldn't happen, and believed this despite centuries of history showing a never-ending string of episodic crises in countries around the world. Some economists did foresee trouble brewing, precisely because they took the past seriously as a guide to what could happen in the future, rather than mathematical theory. As Michael Pettis writes,

ONE of the stranger myths about the recent financial crisis is that no one saw it coming. In fact quite a lot of economists saw it coming, and for years had been writing with dread about the growing global imbalances and the necessary financial adjustments. In 2002 for example, Financial Policy published my article, “Will Globalization Go Bankrupt?” in which I compared the previous decade to earlier globalisation cycles during the past two hundred years and argued that we were about to see a major financial crisis that would result in a sharp economic contraction, bankruptcies of seemingly unassailable financial institutions, rising international trade tensions, and the reassertion of politics over finance. I even predicted that at least one financial superstar would go to jail.

How did I know? It didn’t require a very sophisticated understanding of economics, just some knowledge of history. Every previous globalisation cycle except one (the one cut short in 1914) ended that way, and nothing in the current cycle seemed fundamentally different from what had happened before. ... So how should the teaching of economics change? That’s easy. While mathematical fluency is very useful, it should not be at the heart of economics instruction. That place should be reserved for economic history.

That seems like an eminently sensible attitude. Moreover, modelling in economics ought to be much more strongly focused on understanding how past events and crises have emerged and why they appear to be inherent in the nature of economics systems -- just as natural as thunderstorms or hurricanes are in the Earth's atmosphere. This means, it would seem clear, moving outside the context of the profession's beloved general equilibrium models to study natural instabilities in a serious way.

In any event, I'm not convinced this discussion reflects adequately the deep dissatisfaction -- perhaps even embarrassment -- some economists feel over the state of their field. A good gauge of the stronger views held by a fraction of academic economists is the report written by those at the 2008 Dahlem Workshop in economics. The opening paragraph sets the tone:

The global financial crisis has revealed the need to rethink fundamentally how financial systems are regulated. It has also made clear a systemic failure of the economics profession. Over the past three decades, economists have largely developed and come to rely on models that disregard key factors—including heterogeneity of decision rules, revisions of forecasting strategies, and changes in the social context—that drive outcomes in asset and other markets. It is obvious, even to the casual observer that these models fail to account for the actual evolution of the real-world economy. Moreover, the current academic agenda has largely crowded out research on the inherent causes of financial crises. There has also been little exploration of early indicators of system crisis and potential ways to prevent this malady from developing. In fact, if one browses through the academic macroeconomics and finance literature, “systemic crisis” appears like an otherworldly event that is absent from economic models. Most models, by design, offer no immediate handle on how to think about or deal with this recurring phenomenon. In our hour of greatest need, societies around the world are left to grope in the dark without a theory. That, to us, is a systemic failure of the economics profession.

How many "lost decades"?

It is perhaps a reflection of the perceived self-importance of the financial industry -- it is the axis about which the world revolves -- that the term "lost decade" refers to any ten year period in which the stock market actually declines in value. An entire decade of history is deemed to have been lost. Fortunately, such events are exceedingly rare -- or so most people think.

Not quite. Economist Blake LeBaron finds that the likelihood of a lost decade -- as assessed by the historical data for U.S. markets -- is actually around 7%. That's the historical chance for the numerical or nominal value of a diversified portfolio of U.S. stocks to fall over a decade. Calculated in real terms -- adjusting for inflation -- makes the probability significantly higher, probably over 10%, not really an extremely unlikely event at all. The figure below (Figure 1 in LeBaron's paper) shows the calculated return over ten year windows over the past 200 years or so, and shows maybe six or so episodes in which the real return descends into negative figures.

Not an earth shaking result, perhaps, but a useful corrective to widespread belief that long term drops in the market are truly exceptional events. As LeBaron comments,

Lost decades are often treated as a kind of black swan event that is almost impossible. Results in this note show that while they are a tail event, they may not be as far out in the tail as the popular press would have us think.... A life long investor facing 6 decades of investments should consider a probability 0.35 of seeing at least one lost decade in their lifetime.

UPDATE


Blake pointed out to me in an email that the term "lost decade" of course has much wider meanings that what I've discussed here, arising for example in discussions of Japan from 1990 to 2000 (and maybe longer).