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

ArXiv ID: 2510.15921 “View on arXiv”

Authors: Amarendra Mohan, Ameer Tamoor Khan, Shuai Li, Xinwei Cao, Zhibin Li

Abstract

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.

Keywords: Spiking Neural Networks, Neuromorphic Computing, Cross-Market Optimization, Event-Driven Processing, Time-Series Prediction, Equities

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced neuromorphic theory including spiking neural networks, Leaky Integrate-and-Fire dynamics, and spike-timing-dependent plasticity, giving high mathematical complexity. It also demonstrates strong empirical rigor with a five-year dataset from Yahoo Finance, backtesting against ANN benchmarks, and reporting of risk-adjusted returns and volatility metrics.

  flowchart TD
    A["Research Goal: Apply SNNs to Cross-Market Portfolio Optimization"] --> B["Data Collection: 5-Year Equity Data<br>Nifty 500 & S&P 500 via Yahoo Finance"]
    B --> C["SNN Architecture Design<br>Leaky Integrate-and-Fire with STDP & Lateral Inhibition"]
    C --> D["Event-Driven Processing & Optimization<br>Hierarchical Clustering, Population Encoding, Risk Constraints"]
    D --> E["Experimental Evaluation<br>SNN vs. ANN Benchmarks"]
    E --> F["Findings: Superior Risk-Adjusted Returns<br>Reduced Volatility & Computational Efficiency"]