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A Multi-Source Data Fusion and PSO-Transformer Decision Support System for Gold Price Volatility Forecasting and Risk Warning

Year: 2026
Type: article
Abstract: Gold volatility responds to shocks and sentiment, yet the two are rarely fused, and forecasts seldom become actionable. We link a multi-source fusion subsystem to a risk-warning layer through a Transformer whose three pre-registered hyperparameters are searched by particle swarm optimization (PSO-Transformer). It fuses video sentiment, headline scoring and market factors screened to 20 of 21 candidates; the decision side maps the predicted realized variance RVt/Mt (bp2, non-annualized) into three traffic-light tiers of a calibrated predictive distribution. On 5-min spot gold data (January–June 2025, 126 trading days), the PSO-Transformer attains a one-step MAE of 8.038, a 36.5% average improvement over seven baselines under protocol P1, where selection and evaluation share one window; under the leak-free protocol P2, the same pipeline attains 10.16 against 8.82 for the GRU, so the first place reflects the P1 design, not an independent out-of-sample comparison. Diebold–Mariano gains over GARCH, SVM and XGBoost survive a Holm correction. Walk-forward backtesting over 59 origins with persistence-anchored alerts yields 67.8% tier accuracy, 78.8% high-risk precision and 92.9% recall, with the neural engine fed into the same rule attaining 45.5% against 61.4%. One instrument, six months: a documented architecture, not a demonstrated transfer.
Source: Mathematics
DOI: 10.3390/math14214115
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225636278
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