SimStock: Learning Stock Similarities from Financial Time Series

Summary

This study presents SimStock, a framework for identifying similar stocks from time-series behavior rather than relying only on conventional sector or regional labels. It combines self-supervised learning with temporal domain generalization to form representations intended to remain useful as market conditions change. The motivation is that stock relationships can be difficult to classify when markets are non-stationary and standard categories fail to capture their dynamics.

The authors evaluate the approach on four real-world datasets covering thousands of stocks and report that it outperforms existing similarity methods. They also apply the learned similarities to pairs trading, index tracking, and portfolio optimization, reporting better performance than conventional approaches. The summary does not specify dataset composition, benchmark details, evaluation periods, or robustness tests, so the reported results should be interpreted within the scope of those experiments rather than as a guarantee of future investment performance.

Key ideas

• SimStock learns stock representations from temporal data instead of relying only on sector and regional labels.
• The framework combines self-supervised learning with temporal domain generalization to address changing market behavior.
• The study tests the method on four datasets containing thousands of stocks and reports stronger similarity results than existing methods.
• Applications include pairs trading, index tracking, and portfolio optimization.
• The description omits benchmark and robustness details, limiting assessment of generalization to other settings.

Tags

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


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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