标题： 用于金融市场跨市场投资组合优化的脉冲神经网络：一种类脑计算方法 显示英文标题

标题： Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach

摘要： 跨市场投资组合优化随着金融市场全球化和高频、多维数据集的增长变得越来越复杂。 传统的人工神经网络在某些投资组合管理任务中虽然有效，但通常会产生大量的计算开销，并且缺乏处理大规模多市场数据所需的时间处理能力。 本研究探讨了脉冲神经网络（SNNs）在跨市场投资组合优化中的应用，利用神经形态计算原理来处理来自印度（Nifty 500）和美国（S&P 500）市场的股票数据。 通过Yahoo Finance API系统地收集了一个包含大约1,250个交易日的五年数据集。 所提出的框架结合了泄漏积分-发放神经元动力学与自适应阈值、尖峰时间依赖可塑性以及侧抑制，以实现对金融时间序列的事件驱动处理。 通过层次聚类实现降维，而基于种群的尖峰编码和多种解码策略在现实交易约束下支持稳健的投资组合构建，包括基数限制、交易成本和自适应风险厌恶。 实验评估表明，与人工神经网络基准相比，基于SNN的框架在风险调整后收益和降低波动性方面表现更优，同时显著提高了计算效率。 这些发现突显了神经形态计算在全球金融市场中可扩展、高效且稳健的投资组合优化的潜力。 显示英文摘要

摘要： Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study investigates the application of Spiking Neural Networks (SNNs) for cross-market portfolio optimization, leveraging neuromorphic computing principles to process equity data from both the Indian (Nifty 500) and US (S&P 500) markets. A five-year dataset comprising approximately 1,250 trading days of daily stock prices was systematically collected via the Yahoo Finance API. The proposed framework integrates Leaky Integrate-andFire neuron dynamics with adaptive thresholding, spike-timingdependent plasticity, and lateral inhibition to enable event-driven processing of financial time series. Dimensionality reduction is achieved through hierarchical clustering, while populationbased spike encoding and multiple decoding strategies support robust portfolio construction under realistic trading constraints, including cardinality limits, transaction costs, and adaptive risk aversion. Experimental evaluation demonstrates that the SNN-based framework delivers superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, while substantially improving computational efficiency. These findings highlight the promise of neuromorphic computation for scalable, efficient, and robust portfolio optimization across global financial markets.