  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.
