Third (and final) excerpt...

The third (and, you'll all be pleased to hear, final!) excerpt of my book was published in Bloomberg today. The title is "Toward a National Weather Forecaster for Finance" and explores (briefly) the topic of what might be possible in economics and finance in creating national (and international) centers devoted to data intensive risk analysis and forecasting of socioeconomic "weather."

Before anyone thinks I'm crazy, let me make very clear that I'm using the term "forecasting" in it's general sense, i.e. of making useful predictions of potential risks as they emerge in specific areas, rather than predictions such as "the stock market will collapse at noon on Thursday." I think we can all agree that the latter kind of prediction is probably impossible (although Didier Sornette wouldn't agree), and certainly would be self-defeating were it made widely known. Weather forecasters make much less specific predictions all the time, for example, of places and times where conditions will be ripe for powerful thunderstorms and tornadoes. These forecasts of potential risks are still valuable, and I see no reason similar kinds of predictions shouldn't be possible in finance and economics. Of course, people make such predictions all the time about financial events already. I'm merely suggesting that with effort and the devotion of considerable resources for collecting and sharing data, and building computational models, we could develop centers acting for the public good to make much better predictions on a more scientific basis.

As a couple of early examples, I'll point to the recent work on complex networks in finance which I've touched on here and here. These are computationally intensive studies demanding excellent data which make it possible to identify systemically important financial institutions (and links between them) more accurately than we have in the past. Much work remains to make this practically useful.

Another example is this recent and really impressive agent based model of the US housing market, which has been used as a "post mortem" experimental tool to ask all kinds of "what if?" questions about the housing bubble and its causes, helping to tease out better understanding on controversial questions. As the authors note, macroeconomists really didn't see the housing market as a likely source of large-scale macroeconomic trouble. This model has made it possible to ask and explore questions that cannot be explored with conventional economic models:

 Not only were the Macroeconomists looking at the wrong markets, they might have been looking at the wrong variables. John Geanakoplos (2003, 2010a, 2010b) has argued that leverage and collateral, not interest rates, drove the economy in the crisis of 2007-2009, pushing housing prices and mortgage securities prices up in the bubble of 2000-2006, then precipitating the crash of 2007. Geanakoplos has also argued that the best way out of the crisis is to write down principal on housing loans that are underwater (see Geanakoplos-Koniak (2008, 2009) and Geanakoplos (2010b)), on the grounds that the loans will not be repaid anyway, and that taking into account foreclosure costs, lenders could get as much or almost as much money back by forgiving part of the loans, especially if stopping foreclosures were to lead to a rebound in housing prices.

There is, however, no shortage of alternative hypotheses and views. Was the bubble caused by low interest rates, irrational exuberance, low lending standards, too much refinancing, people not imagining something, or too much leverage? Leverage is the main variable that went up and down along with housing prices. But how can one rule out the other explanations, or quantify which is more important? What effect would principal forgiveness have on housing prices? How much would that increase (or decrease) losses for investors? How does one quantify the answer to that question?

Conventional economic analysis attempts to answer these kinds of questions by building equilibrium models with a representative agent, or a very small number of representative agents. Regressions are run on aggregate data, like average interest rates or average leverage. The results so far seem mixed. Edward Glaeser, Joshua Gottlieb, and Joseph Gyourko (2010) argue that leverage did not play an important role in the run-up of housing prices from 2000-2006. John Duca, John Muellbauer, and Anthony Murphy (2011), on the other hand, argue that it did. Andrew Haughwout et al (2011) argue that leverage played a pivotal role.

In our view a definitive answer can only be given by an agent-based model, that is, a model in which we try to simulate the behavior of literally every household in the economy. The household sector consists of hundreds of millions of individuals, with tremendous heterogeneity, and a small number of transactions per month. Conventional models cannot accurately calibrate heterogeneity and the role played by the tail of the distribution. ... only after we know what the wealth and income is of each household, and how they make their housing decisions, can we be confident in answering questions like: How many people could afford one house who previously could afford none? Just how many people bought extra houses because they could leverage more easily? How many people spent more because interest rates became lower? Given transactions costs, what expectations could fuel such a demand? Once we answer questions like these, we can resolve the true cause of the housing boom and bust, and what would happen to housing prices if principal were forgiven.

... the agent-based approach brings a new kind of discipline because it uses so much more data. Aside from passing a basic plausibility test (which is crucial in any model), the agent-based approach allows for many more variables to be fit, like vacancy rates, time on market, number of renters versus owners, ownership rates by age, race, wealth, and income, as well as the average housing prices used in standard models. Most importantly, perhaps, one must be able to check that basically the same behavioral parameters work across dozens of different cities. And then at the end, one can do counterfactual reasoning: what would have happened had the Fed kept interest rates high, what would happen with this behavioral rule instead of that.

The real proof is in the doing. Agent-based models have succeeded before in simulating traffic and herding in the flight patterns of geese. But the most convincing evidence is that Wall Street has used agent-based models for over two decades to forecast prepayment rates for tens of millions of individual mortgages.

This is precisely the kind of work I think can be geared up and extended far beyond the housing market, augmented with real time data, and used to make valuable forecasting analyses. It seems to me actually to be the obvious approach.
 

Second excerpt...

A second excerpt of my forthcoming book Forecast is now online at Bloomberg. It's a greatly condensed text assembled from various parts of the book. One interesting exchange in the comments from yesterday's excerpt:

Food For Thought commented....Before concluding that economic theory does not include analysis of unstable equilibria check out the vast published findings on unstable equilibria in the field of International Economics.  Once again we have someone touching on one tiny part of economic theory and drawing overreaching conclusions. 

I would expect a scientist would seek out more evidence before jumping to conclusions.

to which one Jack Harllee replied...

Sure, economists have studied unstable equilibria. But that's not where the profession's heart is. Krugman summarized rather nicely in 1996, and the situation hasn't changed much since then:
"Personally, I consider myself a proud neoclassicist. By this I clearly don't mean that I believe in perfect competition all the way. What I mean is that I prefer, when I can, to make sense of the world using models in which individuals maximize and the interaction of these individuals can be summarized by some concept of equilibrium. The reason I like that kind of model is not that I believe it to be literally true, but that I am intensely aware of the power of maximization-and-equilibrium to organize one's thinking - and I have seen the propensity of those who try to do economics without those organizing devices to produce sheer nonsense when they imagine they are freeing themselves from some confining orthodoxy. ...That said, there are indeed economists who regard maximization and equilibrium as more than useful fictions. They regard them either as literal truths - which I find a bit hard to understand given the reality of daily experience - or as principles so central to economics that one dare not bend them even a little, no matter how useful it might seem to do so."

This response fairly well captures my own position. I argue in the book that the economics profession has been fixated far too strongly on equilibrium models, and much of the time simply assumes the stability of such equilibria without any justification. I certainly don't claim that economists have never considered unstable equilibria (or examined models with multiple equilibria). But any examination of the stability of an equilibrium demands some analysis of dynamics of the system away from equilibrium, and this has not (to say the least) been a strong focus of economic theory.   

New territory for game theory...

This new paper in PLoS looks fascinating. I haven't had time yet to study it in detail, but it appears to make an important demonstration of how, when thinking about human behavior in strategic games, fixed point or mixed strategy Nash equilibria can be far too restrictive and misleading, ruling out much more complex dynamics, which in reality can occur even for rational people playing simple games: 

Abstract

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems.

...and from the conclusions, ...

Cycles in the belief space of learning agents have been predicted for many years, particularly in games with intransitive dominance relations, like Matching Pennies and Rock-Paper-Scissors, but experimentalists have only recently started looking to these dynamics for experimental predictions. This work should function to caution experimentalists of the dangers of treating dynamics as ephemeral deviations from a static solution concept. Periodic behavior in the Mod Game, which is stable and efficient, challenges the preconception that coordination mechanisms must converge on equilibria or other fixed-point solution concepts to be promising for social applications. This behavior also reveals that iterated reasoning and stable high-dimensional dynamics can coexist, challenging recent models whose implementation of sophisticated reasoning implies convergence to a fixed point [13]. Applied to real complex social systems, this work gives credence to recent predictions of chaos in financial market game dynamics [8]. Applied to game learning, our support for cyclic regimes vindicates the general presence of complex attractors, and should help motivate their adoption into the game theorist’s canon of solution concepts

Book excerpt...

Bloomberg is publishing a series of excerpts from my forthcoming book, Forecast, which is now due out in only a few days. The first one was published today.

Secrets of Cyprus...

Just something to think about when scratching your head over the astonishing developments in Cyprus, which seem to be more or less intentionally designed to touch off bank runs in several European nations. Why? Courtesy of Zero Hedge:

...news is now coming out that the Cyprus parliament has postponed the decision and may in fact not be able to reach agreement. They may tinker with the percentages, to penalize smaller savers less (and larger savers more). However, the damage is already done. They have hit their savers with a grievous blow, and this will do irreparable harm to trust and confidence.

As well it should! In more civilized times, there was a long established precedent regarding the capital structure of a bank. Equity holders incur the first losses as they own the upside profits and capital gains. Next come unsecured creditors who are paid a higher interest rate, followed by secured bondholders who are paid a lower interest rate. Depositors are paid the lowest interest rate of all, but are assured to be made whole, even if it means every other class in the capital structure is utterly wiped out.

As caveat to the following paragraph, I acknowledge that I have not read anything definitive yet regarding bondholders. I present my assumptions (which I think are likely correct).

As with the bankruptcy of General Motors in the US, it looks like the rule of law and common sense has been recklessly set aside. The fruit from planting these bitter seeds will be harvested for many years hence. As with GM, political expediency drives pragmatic and ill-considered actions. In Cyprus, bondholders include politically connected banks and sovereign governments.  Bureaucrats decided it would be acceptable to use depositors like sacrificial lambs. The only debate at the moment seems to be how to apportion the damage amongst “rich” and “non-rich” depositors.

Also, much more on the matter here, mostly expressing similar sentiments. And do read The War On Common Sense by Tim Duy:

This weekend, European policymakers opened up a new front in their ongoing war on common sense.  The details of the Cyprus bailout included a bail-in of bank depositors, small and large alike.  As should have been expected, chaos ensued as Cypriots rushed to ATMs in a desperate attempt to withdraw their savings, the initial stages of what is likely to become a run on the nation's banks.  Shocking, I know.  Who could have predicted that the populous would react poorly to an assault on depositors?

Everyone.  Everyone would have predicted this.  Everyone except, apparently, European policymakers....
 

Beginning of the end for big banks?

If the biggest banks are too big to fail, too connected to fail, too important to prosecute, and also too complex to manage, it would seem sensible to scale them down in size, and to reduce their centrality and the complexity of their positions. Simon Johnson has an encouraging article suggesting that at least some of this may actually be about to happen: 

The largest banks in the United States face a serious political problem. There has been an outbreak of clear thinking among officials and politicians who increasingly agree that too-big-to-fail is not a good arrangement for the financial sector.

Six banks face the prospect of meaningful constraints on their size: JPMorgan Chase, Bank of America, Citigroup, Wells Fargo, Goldman Sachs and Morgan Stanley. They are fighting back with lobbying dollars in the usual fashion – but in the last electoral cycle they went heavily for Mitt Romney (not elected) and against Elizabeth Warren and Sherrod Brown for the Senate (both elected), so this element of their strategy is hardly prospering.

What the megabanks really need are some arguments that make sense. There are three positions that attract them: the Old Wall Street View, the New View and the New New View. But none of these holds water; the intellectual case for global megabanks at their current scale is crumbling.

Most encouraging is the emergence of a real discussion over the implicit taxpayer subsidy given to the largest banks. See also this editorial in Bloomberg from a few weeks ago:

On television, in interviews and in meetings with investors, executives of the biggest U.S. banks -- notably JPMorgan Chase & Co. Chief Executive Jamie Dimon -- make the case that size is a competitive advantage. It helps them lower costs and vie for customers on an international scale. Limiting it, they warn, would impair profitability and weaken the country’s position in global finance.

So what if we told you that, by our calculations, the largest U.S. banks aren’t really profitable at all? What if the billions of dollars they allegedly earn for their shareholders were almost entirely a gift from U.S. taxpayers?

... The top five banks -- JPMorgan, Bank of America Corp., Citigroup Inc., Wells Fargo & Co. and Goldman Sachs Group Inc. - - account for $64 billion of the total subsidy, an amount roughly equal to their typical annual profits (see tables for data on individual banks). In other words, the banks occupying the commanding heights of the U.S. financial industry -- with almost $9 trillion in assets, more than half the size of the U.S. economy -- would just about break even in the absence of corporate welfare. In large part, the profits they report are essentially transfers from taxpayers to their shareholders.

So much for the theory that the big banks need to pay big bonuses so they can attract that top financial talent on which their success depends. Their success seems to depend on a much simpler recipe.

This paper also offers some interesting analysis on different practical steps that might be taken to end this ridiculous situation.

Megabanks: too complex to manage

Having come across Chris Arnade, I'm currently reading everything I can find by him. On this blog I've touched on the matter of financial complexity many times, but mostly in the context of the network of linked institutions. I've never considered the possibility that the biggest financial institutions are themselves now too complex to be managed in any effective way. In this great article at Scientific American, Arnade (who has 20 years experience working in Wall St.) makes a convincing case that the largest banks are now invested in so many diverse products of such immense complexity that they cannot possibly manage their risks:

This is far more common on Wall Street than most realize. Just last year JP Morgan revealed a $6 billion loss from a convoluted investment in credit derivatives. The post mortem revealed that few, including the actual trader, understood the assets or the trade. It was even found that an error in a spreadsheet was partly responsible.

Since the peso crisis, banks have become massive, bloated with new complex financial products unleashed by deregulation. The assets at US commercial banks have increased five times to $13 trillion, with the bulk clustered at a few major institutions. JP Morgan, the largest, has $2.5 trillion in assets.

Much has been written about banks being “too big to fail.” The equally important question is are they “too big to succeed?” Can anyone honestly risk manage $2 trillion in complex investments?

To answer that question it’s helpful to remember how banks traditionally make money: They take deposits from the public, which they lend out longer term to companies and individuals, capturing the spread between the two.

Managing this type of bank is straightforward and can be done on spreadsheets. The assets are assigned a possible loss, with the total kept well beneath the capital of the bank. This form of banking dominated for most of the last century, until the recent move towards deregulation.

Regulations of banks have ebbed and flowed over the years, played out as a fight between the banks’ desire to buy a larger array of assets and the government’s desire to ensure banks’ solvency.

Starting in the early 1980s the banks started to win these battles resulting in an explosion of financial products. It also resulted in mergers. My old firm, Salomon Brothers, was bought by Smith Barney, which was bought by Citibank.

Now banks no longer just borrow to lend to small businesses and home owners, they borrow to trade credit swaps with other banks and hedge funds, to buy real estate in Argentina, super senior synthetic CDOs, mezzanine tranches of bonds backed by the revenues of pop singers, and yes, investments in Mexico pesos. Everything and anything you can imagine.

Managing these banks is no longer simple. Most assets now owned have risks that can no longer be defined by one or two simple numbers. They often require whole spreadsheets. Mathematically they are vectors or matrices rather than scalars.

Before the advent of these financial products, the banks’ profits were proportional to the total size of their assets. The business model scaled up linearly. There were even cost savings associated with a larger business.

This is no longer true. The challenge of risk managing these new assets has broken that old model.

Not only are the assets themselves far harder to understand, but the interplay between the different assets creates another layer of complexity.

In addition, markets are prone to feedback loops. A bank owning enough of an asset can itself change the nature of the asset. JP Morgan’s $6 billion loss was partly due to this effect. Once they had began to dismantle the trade the markets moved against them. Put another way, other traders knew JP Morgan were in pain and proceeded to ‘shove it in their faces’.

Bureaucracy creates another layer, as does the much faster pace of trading brought about by computer programs. Many risk managers will privately tell you that knowing what they own is as much a problem as knowing the risk of what is owned.

Put mathematically, the complexity now grows non-linearly. This means, as banks get larger, the ability to risk-manage the assets grows much smaller and more uncertain, ultimately endangering the viability of the business.