Classifying and Clustering Trading Agents

TL;DR

This paper explores classifying and clustering trading agents using synthetic data generated by an agent-based model, highlighting challenges in unsupervised clustering and demonstrating the effectiveness of supervised methods.

Contribution

It introduces a novel approach using agent-based models to generate ground truth data for evaluating classification and clustering of trading agents.

Findings

Supervised classification effectively distinguishes trading behaviors.

Unsupervised clustering can produce misleading results.

Synthetic data enables validation of machine learning methods.

Abstract

The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is finally available. The problem, however, is that we do not have access to ground truth when working with real-world data. This, together with often limited interpretability of modern machine learning methods, makes it difficult to fully utilize the available research potential. In order to deal with this challenge we propose to use a realistic agent-based model as a way to generate synthetic data. This way one has access to ground truth, large replicable data, and limitless research scenarios. Using this approach we show how, even when classifying trading agents in a supervised manner is relatively easy, a more realistic task of unsupervised clustering may give incorrect or even misleading results. We complete the results with investigating the details of how supervised techniques were able to successfully distinguish between different trading behaviors.