This book presents a high-level overview to the application of complexity theory to finance, first presenting the more theoretical side to complexity finance before going on to illustrate more practical examples of nonlinear dynamical modeling applied to capital markets.
In a recent interview George Soros captured much of the predicament that finance as a science finds itself in today when he said “The efficient markets hypothesis has failed and it is recognized that it has failed and therefore economist need to find a new understanding of financial markets…. this is what science is, it is a trial and error. Unfortunately, we don’t have a properly developed alternative and that is what we are looking for.” He goes on to say that the approach to finance that we developed under the efficient markets paradigm is not applicable to the real world and that he, in fact, made his money betting against the efficient markets hypothesis.
Since the financial crisis, much of economic and financial theory has been called into question. We are increasingly recognizing the limitations of the many kinds of financial models that are dependent upon assumptions of linearity and equilibrium; that agents are rational and independent and that the future will resemble the past. We come to increasingly recognize that linear development is but one kind of change, nonlinearity is another and of equal importance, if we are to build a more comprehensive understanding of financial systems. When systems involve synergies and feedback then they become nonlinear. You can get cascading effects that take the system out of equilibrium and into phase transitions and that these periods of what seems to be chaos, in fact, have their own kind of dynamics. By understanding the science of nonlinear dynamics we stand a much better chance of seeing and dealing with these periods of exponential and fundamental transformation.
This is indeed an exciting time for economics and finance as after almost two centuries of studying equilibrium solutions economists are beginning to study the emergence of non-equilibria and the general evolution of patterns in the economy. That is, we are starting to study the economy out of equilibrium and increasingly doing this through a computer-based algorithmic approach. This new complexity approach is certainly a paradigm shift, one of its creators W. Brian Arthur describes the essence of this change in perception when he notes “really it is a shift from looking at the world in reductionist terms, from the top down and imagine everything holding everything else in equilibrium where not much is changing at all, to looking at the world as alive everything is affecting everything.”
A key tool in this new approach is agent-based modeling (ABM) that gives us an inherently dynamic vision of markets, as patterns are continually being created and recreated through endless computations across complex networks of interaction; just as we see in the real world. When seen in this way financial markets show themselves not as mechanical, deterministic systems always moving towards stability and equilibrium but instead more like an ecosystem continuously evolving and creating new structures and patterns.