What question did this study set out to answer?

To enhance stock prediction effectiveness by incorporating inter-stock relationships and temporal constraints.

March 26, 2026

Temporal similarity-constraint graph networks for stock prediction with stock relations

Key Points

To enhance stock prediction effectiveness by incorporating inter-stock relationships and temporal constraints.
Introduction of temporal similarity-constraint graph networks (TSCGNs) for stock ranking.
Evaluation on NASDAQ and NYSE datasets.
Comparison of TSCGN performance against baseline models including rank-LSTM and TGC.
TSCGN achieves a 34.1% improvement in cumulative investment return ratio (IRR) compared to rank-LSTM.
TSCGN yields a 29.4% improvement in IRR over TGC.
TSCGN results in a 4.8% increase in mean reciprocal rank (MRR).
TSCGN maintains a comparable mean squared error (MSE) during evaluation.

Abstract

Stock prediction plays a crucial role in assisting investors with decision-making by leveraging predictive models to forecast future trends. Traditional stock prediction models primarily focus on price regression, treating stocks as independent entities and neglecting inter-stock relationships, which limits their applicability in investment decision-making. To address this limitation, we introduce temporal similarity-constraint graph networks (TSCGNs), a novel approach that integrates stock relations and ranks stocks based on expected returns. Experimental results on NASDAQ and NYSE datasets demonstrate that TSCGN significantly improves stock selection performance over baseline models. Specifically, TSCGN achieves a 34.1% and 29.4% improvement in cumulative investment return ratio (IRR) compared to rank-LSTM and TGC, respectively. Additionally, TSCGN yields a 4.8% increase in mean reciprocal rank (MRR) while maintaining a comparable mean squared error (MSE), demonstrating its effectiveness in stock selection. These findings highlight the importance of incorporating inter-stock relations and temporal similarity constraints in financial modeling to enhance investment decision-making.

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