Industry Classification Using a Novel Financial Time-Series Case Representation
This work addresses a domain-specific problem in finance where even modest improvements can yield significant value, though it appears incremental in applying a new representation to an existing task.
The paper tackled industry sector classification using historical stock returns time-series data by proposing a novel representation based on stock returns embeddings for case-based reasoning, demonstrating substantial performance improvements over conventional baselines.
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.