Agent-based modeling and simulation in US equity markets – Part 1

This is the first of an occasional series of posts on the application of agent-based modeling to US equity markets. I chose the US equity markets because it serves as the motivating paradigm for the vast majority of historical academic work on agent-based modeling of equity markets. Despite the appearance of such a narrow focus, this particular perspective is relevant because the history of US equity markets reflects and occasionally foretells the variety of market structures observed around the globe today. For example, prior to Regulation National Market System (Reg NMS), US equity markets were a combination of fragmented dealer-intermediated continuous two-sided auctions, call auctions, and over-the-counter networks. Nowadays, they are a combination of fragmented order-driven two-sided continuous auctions, electronically-intermediated call auctions, and a variety of dark pools.

There are three primary sources of agent-based models and/or simulation-based models of equity markets: academics (including academics in government), for-profit companies and not-for-profit government contractors. My discussion and examples will be focused on academic work because academics are the ones most likely to publish their results. It would come as no surprise to learn that hedge funds and other market participants employ simulations as a part of their R&D apparatus. For example, backtesting trading strategies with historical data requires a technical infrastructure that would also support some forms of ABM or simulation. However, for-profit trading firms are not very likely to publish their findings. Even not-for-profit government contractors (and academics who aspire to be) have an incentive to withhold critical model details, thereby making validation and replication difficult or impossible.

In the introduction to this website, I set out my true aims. One of these is increasing awareness of the utility of applying agent-based modeling to financial markets. Toward that end, I will begin with some definitions and provide some examples of how I think we can do better at communicating with market practitioners, regulators, and other professionals who are not susceptible to deep and lengthy discussions of the scientific method. I will source many of the definitions from Wikipedia; not because I think they are the final arbiter of scientific rigor, but because I don’t necessarily want to argue about definitions. I just want a common source everyone can agree is the source – even if they don’t agree with the precision (or lack thereof) contained within.

A model

Wikipedia defines a conceptual model as a representation of a system using general rules and concepts and a scientific model as a simplified and idealized understanding of physical systems. A model is a simplification of reality. Economists use a variety of models to simplify our true complex economy. Theoretical models are mathematical frameworks. Simplifications are introduced to facilitate analytical tractability. In other words, the framework (i.e., the construction of the model) is simplified in order to get answers, right or wrong. Econometric models are empirical statistical models, typically applied to real economic data. Simplifications involve specification (choosing a type of model that can be estimated or that suits the available data), variable selection (choosing variables that are consistent with the available data), and imposing (or ignoring) untested assumptions regarding the unbiasedness or consistency of parameter estimates. Yet economists continue to use these models because they can be useful despite their shortcomings. George Box, a statistician, summarized the practical use of statistical models in a paper with a section entitled “All models are wrong but some are useful.”

Agent-based models are simplifications of reality, too. To maintain usefulness, the agent-based modeler abstracts away features of the real economy that are unnecessary and accentuates features that are necessary for generating insights into the proposed problem. If the agent-based modeler is successful, the model is useful. But the model is still a simplification, and, if Box is right, it is wrong, too!

How would you explain economic modeling to an accountant, attorney or MBA?

In the next post, I will introduce simulation and agent-based modeling and discuss the relationship between them.

Published by

Chuck Collver

Quant, Programmer, Data Scientist, Developer

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