A Community-Structured Spin-Glass Framework for AAPL Price Dynamics with Event-Driven Shocks

We present a community-structured spin-glass framework for modeling financial market dynamics, applied to Apple stock data from 2020 to 2022. Each investor is modeled as a binary spin (buy or sell), interacting within groups of connected agents. The strength and sign of these interactions are determined by community structure and by measures of importance within the network. Successive model enhancements capture increasing realism: networks with distinct investor groups reproduce sectoral segmentation; event shocks, integrated via external news APIs, generate volatility bursts; and nonlinear sentiment transforms keep extreme consensus from producing unrealistic market surges. This layered construction maps clustered sentiment tendencies, frustration effects, and multi-factored equilibria resembling real-world market behaviors. Based on the network spin-glass magnetization, the future stock price is informed and predicted. Our modular Python implementation allows systematic calibration against historical periods, highlighting how statistical physics methods capture emergent, history-dependent behavior in financial systems. We also outline future directions, including quantum-enhanced parameter machine learning, adaptive network rewiring, and heterogeneous agent modeling for six-month predictive forecasts.