Paper: Oct 02,2024
q-fin.PM
ID:2410.01864
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.
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Paper Author: Diego Vallarino
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