Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency
<|reference_start|>Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency: 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.<|reference_end|>
@article{vallarino2024dynamic,
    title={Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and
  Pathfinding for Cost Efficiency},
    author={Diego Vallarino},
    journal={arXiv preprint arXiv:2410.01864},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01864},
    primaryClass={q-fin.PM cs.LG}
}