  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.
