Scott Reamer: Economies as Networks, Investors as Nodes
The "markets" are just a networked collection of traders competing and cooperating in both general and specific ways.
One of the keystones in my firm's hypothesis that negotiated financial markets are complex social systems, is the idea that markets are simply networks and investors are the nodes in those networks, interacting in complex ways through feedback mechanisms subject to non-linear dynamics. Though the importance of economic, institutional, legal, operational, and regulatory connections in this network cannot be underestimated, the "markets" are just a networked collection of traders competing and cooperating in both general and specific ways.
This is such an axiomatic statement that it begs the question, so what? There is precisely nothing novel in the idea that markets are a network of connected investors. Charles Mackay wrote the now-classic book Extraordinary Popular Delusions and the Madness of Crowds 172 years ago in which he laid bare some of the basics of this idea: social psychology as a "network effect." More than a convenient analogy, however, we believe that viewing financial markets as networks allows us to employ some interesting theoretical and practical network-theory research with the explicit goal of trying to understand the network dynamics of financial markets. What gives rise to large-scale volatility events? How and why do markets enter different regimes of behavior? What role does interconnectedness play in market stability?
The last decade or so has seen an important formalism brought to bear on network theory: Data has been teased from both real-world and theoretical-network models that strongly suggest some grossly universal behaviors may exist across different types of networks. That is, networks of any stripe – bees, molecules, viruses, electrical systems, people – have generally similar behavior in their growth, stasis, and, most pertinent to fiduciaries, their failures.
There exists a large body of academic research that has formalized some of the concepts of network theory. For readers interested in further study, the yellow brick road of this discipline could start at any number of places, but we think Networks: An Introduction, by M. E. J. Newman from Oxford University is a good start.
For our part, network theory has helped us to identify similarities and differences in, say, earthquake propagation and market volatility, by understanding their underlying network-based causes. It has enabled us to explore the application of certain classes of mathematics that have been used to model other networks of connected agents. It has allowed us to explore concepts like information cascades and instabilities.
More generally, network theorists have proposed that highly interdependent networks are simultaneously fragile and robust: Fragile in the sense that a single perturbation at one node could theoretically affect large-scale changes in the rest of the network. Robust in the sense that such networks are capable of evolving – responding to exogenous or endogenous catalysts – rapidly. Examples include the following: the power-grid failure that results from a lightning strike to a transformer; the swine flu pandemic that results from one interaction between a farmer and a pig; and more germane, the "flash crash" that results from one relatively small sell order.
These are but the most obvious examples of a common type of network process, derived from a sort of ‘robust weakness’ (to coin a phrase) that is fundamental to the network itself. Understanding that highly interconnected networks are subject to these sorts of robust weaknesses implies something important: one should not expend energy trying to determine what single straw might be the proverbial one that breaks the camel’s back. Rather, we should attempt to determine if the camel’s back is weak enough to be broken by a single straw. Or in the lexicon of network theorists, is the system particularly prone to propagating self-reinforcing perturbations? Of course, how one might ask financial time series data that question is the heart of the matter. And, we can assure you, it is no small challenge. But it does have utility in the sense of distilling our focus to a smaller set of mathematical tools to query the data.
There are important and broad implications here for regulators, portfolio managers, risk managers, and traders; indeed, all fiduciaries. In a negotiated financial market, there are myriad forces that act as the connective tissue keeping investors "networked" to one another. Money itself is one of these connections, of course, to say nothing about the physical infrastructure of trading in the form of the communication networks and backoffice operations. Regulations, contract law, social norms (e.g. a 9:30 a.m. to 4:00 p.m. trading day), CNBC, credit availability, individual risk preferences -- they all have impacts on the stability of the network itself. No matter what historians say "caused" the credit-bust process that started in the summer of 2007, the "network-centric" reality is that the system – the economy and the markets – grew increasingly unstable from the positive feedback of risk preference, credit availability, and regulatory-capture dynamics that existed upstream.
How regulators might approach their craft if they understood the economy and markets in this way is, of course, impossible to predict. But one need not be a libertarian to argue that the nature of the regulatory response to the 2008 credit bust could have actually made the system less robust to future exogenous or endogenous shocks. In our view, it is not a coincidence that Western markets seem to hang on every statement, utterance, or leak from political or regulatory leaders. Market participants seem to have learned a whole new factor attribution: The currency most in trade now is political and monetary policy analysis rather than microeconomics.
While the prima facie evidence of low volatility and low vol-of-vol suggests that central bankers’ volatility squashing goals that were undertaken in 2008 have been met, there are both theoretical and practical reasons to believe the network (of the economy and markets) has become more, not less, prone to the feedback effects of "surprises." There is greater interdependence and interconnectedness than ever before thanks to the monetization of sovereign debts. There is greater symmetry among and between various countries’ monetary and fiscal policies: All bankers are monetizing debt, and (more or less) all legislative bodies are pursuing fiscal stimulus. Like in the summer of 2007 when every trade was the same trade, the worry now is that some sort of critical threshold of homogeneity has been crossed by monetary and fiscal policy authorities. They are all doing the same thing. And for networks of any type, that doesn’t build robustness. From our perch, financial markets don’t appear as quiescent as they seem.
Snowfields, power grids, economies -- they all contain risks that emerge from the heterogeneous interactions of individual "nodes" in those networks of snowflakes, electrons, and traders. When every node is following its own dynamics – when there are no dominant "local attractors" (like the Federal Reserve) – generally speaking, networks are robust and subject to fewer large-scale phase transitions (network-theory lexicon for "bad stuff"). Some semblance of order can be achieved and network evolution can be assured.
But no model – mental or otherwise – can capture the diversity and complexity of the world in which we live: Analogs have a necessarily truncated utility. Viewing investors as nodes in a network can, however, illuminate new insights into the unusual dynamics of market economics: kurtosis, skewness, and fat tails. To the degree that network analogies can help us test mathematical methods to understand the propagation dynamics of that volatility, then its usefulness may well stand the test of time. We’ll see about the Federal Reserve.
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