Author(s)

Fei CHEN, Stephen FIGLEWSKI, Andreas S. WEIGEND

Abstract

This paper compares tree-based approaches to clustering. We model a set of 3-million transactional T-bond futures data using these two techniques and compare their predictive performance on trade profit. We illustrate their respective strengths and weaknesses. 1 Problem Financial data are usually modeled with supervised methods, where functional dependencies are estimated with explicit targets (such as profit). Unsupervised methods, in contrast, apply in cases where hid- den structures in the data need to be discovered without knowledge of such pre-specified targets. This paper seeks to investigate these two approach

Keywords