Neural Embeddings of Financial Time-Series Data

Abstract

The dominant approaches for financial portfolio construction rely on estimating sample covariance and correlations matrixes, which serve as an input into a number of classical portfolio construction techniques. These classical approaches are not forward looking, are constrained by the ability to estimate covariance and correlation matrixes, and are inflexible to incorporating additional information. The authors propose a new approach of using learned representations from deep learning networks to augment such classical techniques. This approach can incorporate learned estimates of future performance and can be customized to create tailored representations best suited to meeting varying financial objectives. This article showcases one example of such an embedding, compares and contrasts it with classical approaches to portfolio construction, and discusses additional possibilities for applying representation learning in quantitative finance.

TOPICS: Big data/machine learning, performance measurement, portfolio construction, simulations, statistical methods

Key Findings

• • The authors propose a general methodology for creating tailored embeddings of financial time-series data.

• • The authors demonstrate one approach for creating such embeddings, analyze the induced similarity measure, and compare and contrast it with the traditional approach of using covariance and correlation as a similarity measure for securities.

• • The authors discuss other applications and advantages of this approach, including accounting for probabilistic estimates of future behavior and flexibility in incorporating varying business objectives.