Stock return prediction is fundamental to financial decision-making, yet traditional time series models fail to capture the complex interdependencies between companies in modern markets. We propose the Full-State Graph Convolutional LSTM (FS-GCLSTM), a novel temporal graph neural network that incorporates value-chain relationships to enhance stock return forecasting. Our approach features two key innovations: First, we represent inter-firm dependencies through value-chain networks, where nodes correspond to companies and edges capture supplier-customer relationships, enabling the model to leverage information beyond historical price data. Second, FS-GCLSTM applies graph convolutions to all LSTM components - current input features, previous hidden states, and cell states - ensuring that spatial information from the value-chain network influences every aspect of the temporal update mechanism. We evaluate FS-GCLSTM on Eurostoxx 600 and S&P 500 datasets using LSEG value-chain data. While not achieving the lowest traditional prediction errors, FS-GCLSTM consistently delivers superior portfolio performance, attaining the highest annualized returns, Sharpe ratios, and Sortino ratios across both markets. Performance gains are more pronounced in the denser Eurostoxx 600 network, and robustness tests confirm stability across different input sequence lengths, demonstrating the practical value of integrating value-chain data with temporal graph neural networks.

翻译：股票收益预测是金融决策的基础，然而传统的时间序列模型未能捕捉现代市场中公司之间复杂的相互依赖性。我们提出了一种新颖的时序图神经网络——全状态图卷积LSTM（FS-GCLSTM），它通过整合价值链关系来增强股票收益预测。我们的方法具有两个关键创新点：首先，我们通过价值链网络表示公司间的依赖关系，其中节点对应公司，边捕捉供应商-客户关系，使模型能够利用超出历史价格数据的信息。其次，FS-GCLSTM将图卷积应用于所有LSTM组件——当前输入特征、先前隐藏状态和细胞状态——确保来自价值链网络的空间信息影响时序更新机制的每个方面。我们使用LSEG价值链数据在Eurostoxx 600和S&P 500数据集上评估FS-GCLSTM。尽管未达到最低的传统预测误差，但FS-GCLSTM在两个市场中始终提供更优的投资组合表现，实现了最高的年化收益、夏普比率和索提诺比率。在更密集的Eurostoxx 600网络中，性能提升更为显著，且稳健性测试证实了其在不同输入序列长度下的稳定性，这证明了将价值链数据与时序图神经网络相结合的实际价值。