WIT Press

Modeling Financial Data Using Clustering And Tree-based Approaches

Price

Free (open access)

Paper DOI

10.2495/DATA980041

Volume

22

Pages

17

Published

1998

Size

1,251 kb

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