Industry Classification Using a Novel Financial Time-Series Case Representation

TL;DR

This paper introduces a new stock returns embedding representation for industry classification that enhances case-based reasoning performance on financial time-series data, demonstrating significant improvements over traditional methods.

Contribution

The paper proposes a novel stock returns embedding representation tailored for case-based reasoning in financial time-series classification tasks.

Findings

Substantial performance improvements over baseline methods.

Effective representation of stock returns for industry classification.

Demonstrated on large-scale public dataset.

Abstract

The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Researchers can access an abundance of time-series data and even modest performance improvements can be translated into significant additional value. In this work, we consider the use of case-based reasoning for an important task in this domain, by using historical stock returns time-series data for industry sector classification. We discuss why time-series data can present some significant representational challenges for conventional case-based reasoning approaches, and in response, we propose a novel representation based on stock returns embeddings, which can be readily calculated from raw stock returns data. We argue that this representation is well suited to case-based reasoning and evaluate our approach using a large-scale public dataset for the industry sector classification task, demonstrating substantial performance improvements over several baselines using more conventional representations.