Central corporate control revealed by mathematics

If you haven't already heard about this new study on the network of corporate control, do have a look. The idea behind it was to use network analysis of who owns whom in the corporate world (established through stock ownership) to tease out centrality of control. New Scientist magazine offers a nice account, which starts as follows:

AS PROTESTS against financial power sweep the world this week, science may have confirmed the protesters' worst fears. An analysis of the relationships between 43,000 transnational corporations has identified a relatively small group of companies, mainly banks, with disproportionate power over the global economy.

The study's assumptions have attracted some criticism, but complex systems analysts contacted by New Scientist say it is a unique effort to untangle control in the global economy. Pushing the analysis further, they say, could help to identify ways of making global capitalism more stable.

The idea that a few bankers control a large chunk of the global economy might not seem like news to New York's Occupy Wall Street movement and protesters elsewhere (see photo). But the study, by a trio of complex systems theorists at the Swiss Federal Institute of Technology in Zurich, is the first to go beyond ideology to empirically identify such a network of power. It combines the mathematics long used to model natural systems with comprehensive corporate data to map ownership among the world's transnational corporations (TNCs).

But also have a look at the web site of the project behind the study, the European project Forecasting Financial Crises, where the authors have tried to clear up several common misinterpretations of just what the study shows.

Indeed, I know the members of this group quite well. They're great scientists and this is a beautiful piece of work. If you know a little about natural complex networks, then the structures found here actually aren't terrifically surprising. However, they are interesting, and it's very important to have the structure documented in detail. Moreover, just because the structure observed here is very common in real world complex networks doesn't mean its something that is good for society.

Abolish banks? Maybe, maybe not...

I have little time to post this week as I have to meet several writing deadlines, but I wanted to briefly mention  this wonderful and extremely insightful speech by Adair Turner from last year (there's a link to the video of the speech here). Turner offers so many valuable perspectives that the speech is worth reading and re-reading; here are a few short highlights that caught my attention.

First, Turner mentions that the conventional wisdom about the wonderful self-regulating efficiency of markets is really a caricature of the real economic theory of markets, which notes many possible shortcomings (asymmetric information, incomplete markets, etc.). However, he also notes that this conventional wisdom is still what has been most influential in policy circles:

.. why, we might ask, do we need new economic thinking when old economic thinking has been so varied and fertile? ... Well, we need it because the fact remains that while academic economics included many strains, in the translation of ideas into ideology, and ideology into policy and business practice, it was one oversimplified strain which dominated in the pre-crisis years.

What was that "oversimplified strain"? Turner summarizes it as follows:

For over half a century the dominant strain of academic economics has been concerned with exploring, through complex mathematics, how economically rational human beings interact in markets. And the conclusions reached have appeared optimistic, indeed at times panglossian. Kenneth Arrow and Gerard Debreu illustrated that a competitive market economy with a fully complete set of markets was Pareto efficient. New classical macroeconomists such as Robert Lucas illustrated that if human beings are not only rational in their preferences and choices but also in their expectations, then the macro economy will have a strong tendency towards equilibrium, with sustained involuntary unemployment a non-problem. And tests of the efficient market hypothesis appeared to illustrate that liquid financial markets are not driven by the patterns of chartist fantasy, but by the efficient processing of all available information, making the actual price of a security a good estimate of its intrinsic value.

As a result, a set of policy prescriptions appeared to follow:

· Macroeconomic policy – fiscal and monetary – was best left to simple, constant and clearly communicated rules, with no role for discretionary stabilisation.

· Deregulation was in general beneficial because it completed more markets and created better incentives.

· Financial innovation was beneficial because it completed more markets, and speculative trading was beneficial because it ensured efficient price discovery, offsetting any temporary divergences from rational equilibrium values.

· And complex and active financial markets, and increased financial intensity, not only improved efficiency but also system stability, since rationally self-interested agents would disperse risk into the hands of those best placed to absorb and manage it.

In other words, all the nuances of the economic theories showing the many limitations of markets seem to have made little progress in getting into the minds of policy makers, thwarted by ideology and the very simple story espoused by the conventional wisdom. Insidiously, the vision of efficient markets so transfixed people that it was assumed that the correct policy prescriptions must be those which would take the system closer to the theoretical ideal (even if that ideal was quite possibly a theorist's fantasy having little to do with real markets), rather than further away from it:

What the dominant conventional wisdom of policymakers therefore reflected was not a belief that the market economy was actually at an Arrow-Debreu nirvana – but the belief that the only legitimate interventions were those which sought to identify and correct the very specific market imperfections preventing the attainment of that nirvana. Transparency to reduce the costs of information gathering was essential: but recognising that information imperfections might be so deep as to be unfixable, and that some forms of trading activity might be socially useless, however transparent, was beyond the ideology...

Turner goes on to argue that the more nuanced views of markets as very fallible systems didn't have much influence mostly because of ideology and, in short, power interests on the part of Wall St., corporations and others benefiting from deregulation and similar policies. I think it is also fair to say that economists as a whole haven't done a very good job of shouting loudly that markets cannot be trusted to know best or that they will only give good outcomes in a restricted set of circumstances.Why haven't there been 10 or so books by prominent economists with titles like "markets are often over-rated"?

But perhaps the most important point he makes is that we shouldn't expect a "theory of everything" to emerge from efforts to go beyond the old conventional wisdom of market efficiency:

...one of the key messages we need to get across is that while good economics can help address specific problems and avoid specific risks, and can help us think through appropriate responses to continually changing problems, good economics is never going to provide the apparently certain, simple and complete answers which the pre-crisis conventional wisdom appeared to. But that message is itself valuable, because it will guard against the danger that in the future, as in the recent past, we sweep aside common sense worries about emerging risks with assurances that a theory proves that everything is OK.

That is indeed a very important message.

The speech goes on to touch on many other topics, all with a fresh and imaginative perspective. Abolish banks? That sounds fairly radical, but it's important to realise that things we take for granted aren't fixed in stone, and may well be the source of problems. And abolishing banks as we know them has been suggested before by prominent people:

Larry Kotlikoff indeed, echoing Irving Fisher, believes that a system of leveraged fractional reserve banks is so inherently unstable that we should abolish banks and instead extend credit to the economy via mutual loan funds, which are essentially banks with 100% equity capital requirements.8 For reasons I have set out elsewhere, I’m not convinced by that extremity of radicalism.9 ... But we do need to ensure that debates on capital and liquidity requirements address the fundamental issues rather than simply choices at the margin. And that requires
economic thinking which goes back to basics and which recognises the importance of specific evolved institutional structures (such as fractional reserve banking), rather than treating existing institutional structures either as neutral pass-throughs in economic models or as facts of life which cannot be changed.

 Amen.

Leverage Control -- A Subtle Story

I mentioned recently some work (in progress) by Stefan Thurner and colleagues exploring how leverage influences stability (price volatility) in a competitive, speculative market. Thurner spoke about this at a meeting on Tipping Points in Durham, UK. What I find most appealing about this work is that is explores this question with a model that is rich enough to exhibit many of the basic features we see in speculative markets -- competition between hedge funds and other investment firms to attract investors' funds, the use of leverage to amplify potential gains, the monitoring of leverage by banks who lend to the investment firms, occasional abrupt crashes and bankruptcies, etc.

Is it a perfect model? Of course not, there is no such thing; models are tools for thinking. But it is arguably better than anything else we currently have for running "policy experiments" to test what might happen in such a market if regulators take this or that step -- establishing tight limits to allowed leverage, for example. 

Stefan kindly sent me the slides from his talk, a few of which I'd like to mention here. As I said, this is work in progress, so these are preliminary results. They're interesting because they suggest that avoiding dangerous market instability through leverage limits comes with costs, and that our intuition isn't at all a reliable guide -- we need these kinds of models in which we can discover surprising outcomes (before we discover them in reality).

I won't give a detailed description of the model; it can be found in an early draft of the paper available here. Thurner and colleagues have been working to improve the model over several years, and it now reproduces a number of realistic market behaviors quite naturally. Thurner summarized these as follows:

 

In other words, the hedge funds act to eliminate mis-pricings (taking volatility out of the market), and profit by doing so. Funds have to be aggressive to survive in the face of stuff competition, but suffer if they get too large. Risks shorten the lifetime of a fund. Overall, the models also reproduces the right statistical fluctuations in the market.

As I discussed in my earlier post in this work, competition between hedge funds leads naturally to increasing leverage and drives the market to have a fat-tailed distribution of returns; it becomes subject (like real market) to large price fluctuations as a matter of course driven by its own internal dynamics (no external impacts required). In this condition, the market is highly prone to catastrophic crashes triggered by nothing by small price fluctuations linked to noise traders (unsophisticated investors buying and selling more or less at random). The figure below shows a typical example, plotting the wealth of various funds versus time, with a dramatic crash that affects all funds at once (different colors for different funds):

Now, a natural question is -- could these kinds of events be avoided with proper regulations? One idea would be to restrict the amount of leverage allowed with the aim of keeping the market returns in a mode Gaussian regime, i.e. eliminating fat tails. People could probably argue for decades about whether this would work or not without coming to an answer; this model makes it possible to do an experiment to find out, which is what Thurner and colleagues have done.

Two figures (below) show some of the results, and require some explanation. The different colors correspond to different possible regulatory regimes, and show how behavior changes with maximum allowed hedge fund leverage : BLUE (no other regulations), PALE GREEN (regulations akin to Basel I and II, in which banks loaning to hedge funds are restricted by capital requirements) and RED (a situation in which banks monitor hedge funds and reduce a hedge fund's allowed leverage below the maximum when the volatility in its assets grows; a kind of adaptive leverage control). 

First, consider a figure showing how how the action of hedge funds, and their use of volatility, actually benefits the market -- making it more efficient (in one sense). 

The figure shows the mean square price volatility versus allowed leverage. Increasing leverage lets the hedge funds pounce on opportunities more aggressively and wipe out mis-pricings more effectively. Die hard free market people should love this as it shows that the effect is strongest in the absence of any regulation. The regulated markets require higher leverage to get the same reduction in volatility.

But this isn't the whole story. Now consider another figure for the probability (per unit time) of a failure of one of the hedge funds:

Here the pure free market solution isn't so good, as this probability rises rapidly with increasing leverage. There is a relatively low value of leverage (around 5 in the model's units) where the market benefits of leverage have already been realized, and more leverage only leads to more failures (because it takes the market into the regime of fat-tailed returns; this can happen even if the mean square volatility remains small).

The regulated markets in this case perform marginally better -- the regulations reduce the number of failures, and the cost for this is marginally increased volatility.

A surprising outcome is that these same regulations, in the regime of very high leverage, actually do worse than no regulations at all -- they lead to higher market volatility AND more failures as well, a truly perverse regime.

All in all, then, this model offers a sobering perspective on how regulators might go about trying to avoid crashes linked to fat tails by limiting leverage. Some limitation clearly seems to be good. But too much can be bad, especially when coupled with other market regulations. You can't test out one idea in isolation, because they interact in surprising ways.

I'll probably have some further comments on this in the near future. It's a work in progress, as is my understanding of it -- and of what it means for the bigger picture.