Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management

TL;DR

This paper introduces SimStock, a novel temporal self-supervised learning framework that effectively captures stock similarities from financial time series data, improving investment strategies amid market non-stationarity.

Contribution

The study presents a new SSL-based method for learning robust stock representations that outperform existing approaches in identifying similarities and enhancing investment applications.

Findings

SimStock outperforms existing methods in stock similarity detection.

Application of SimStock improves performance in pairs trading, index tracking, and portfolio optimization.

The approach demonstrates robustness across multiple real-world financial datasets.

Abstract

In the era of rapid globalization and digitalization, accurate identification of similar stocks has become increasingly challenging due to the non-stationary nature of financial markets and the ambiguity in conventional regional and sector classifications. To address these challenges, we examine SimStock, a novel temporal self-supervised learning framework that combines techniques from self-supervised learning (SSL) and temporal domain generalization to learn robust and informative representations of financial time series data. The primary focus of our study is to understand the similarities between stocks from a broader perspective, considering the complex dynamics of the global financial landscape. We conduct extensive experiments on four real-world datasets with thousands of stocks and demonstrate the effectiveness of SimStock in finding similar stocks, outperforming existing methods. The practical utility of SimStock is showcased through its application to various investment strategies, such as pairs trading, index tracking, and portfolio optimization, where it leads to superior performance compared to conventional methods. Our findings empirically examine the potential of data-driven approach to enhance investment decision-making and risk management practices by leveraging the power of temporal self-supervised learning in the face of the ever-changing global financial landscape.