用图神经网络预测交易成本，再用最短路径找最优调仓方案。
Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency
- 用GNN预测资产间交易成本，作为图中边权重。
- 结合Dijkstra算法找到总成本最低的调仓路径。
- 适合高频交易场景下的量化投资经理使用。
本文提出一种新型组合再平衡优化方法，将图神经网络（GNN）与迪杰斯特拉（Dijkstra）路径搜索算法结合。利用知名科技公司历史股价数据，训练GNN以预测未来交易成本，并将其作为金融资产图中各边的权重。随后，采用迪杰斯特拉算法寻找资本在资产间重新配置的最小成本路径。实证结果表明，该混合方法显著降低了交易成本，为投资经理提供了强有力的工具，尤其适用于高频交易环境。该方法展示了先进机器学习与经典优化算法融合在金融决策中的潜力。未来研究将拓展资产范围并引入强化学习实现持续优化。
原文摘要 · Abstract (English)
This paper introduces a novel approach to optimizing portfolio rebalancing by integrating Graph Neural Networks (GNNs) for predicting transaction costs and Dijkstra's algorithm for identifying cost-efficient rebalancing paths. Using historical stock data from prominent technology firms, the GNN is trained to forecast future transaction costs, which are then applied as edge weights in a financial asset graph. Dijkstra's algorithm is used to find the least costly path for reallocating capital between assets. Empirical results show that this hybrid approach significantly reduces transaction costs, offering a powerful tool for portfolio managers, especially in high-frequency trading environments. This methodology demonstrates the potential of combining advanced machine learning techniques with classical optimization algorithms to improve financial decision-making processes. Future research will explore expanding the asset universe and incorporating reinforcement learning for continuous portfolio optimization.
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