The Evolution of Consumer Financial Complaints Driven by Market Panic Sentiment: A Study Based on Machine Learning and Dirichlet Modeling
Drawing on more than 3.77 million (3,778,954) U.S. Consumer Financial Protection Bureau (CFPB) complaints filed between 2015 and 2026, this study examines the dynamic linkage between the Chicago Board Options Exchange Market Volatility Index (VIX), a metric for expected market volatility, and the structural composition of consumer financial complaints. To address severe class imbalance and substantial textual noise present in the CFPB corpus, we aggregate complaints into five categories via a class-balanced linear support-vector machine (SVM) that applies per-class thresholding within an interpretable pipeline combining Singular Value Decomposition (SVD) semantic embedding and isotonic calibration; the classifier achieves 85.35% accuracy and a macro-F1 of 0.8207, and although it is trained only on data spanning 2019-2022, it generalizes across time, meaning the COVID-19 period does not appear to act as a meaningful confounder. We then model weekly category shares using a Bayesian Dirichlet-multinomial regression driven by weekly VIX peak values. A rise in the VIX significantly expands the credit-reporting share while reducing the debt-collection and mortgages-and-loans shares, whereas credit card and retail banking categories exhibit no significant shifts in their shares; this association reflects persistent level co-movement instead of a genuine multi-week causal lag. A lag-augmented Toda-Yamamoto test further identifies no reverse Granger feedback running from complaint structure to the VIX. Combined with the macro-exogenous property of the VIX, this finding supports a unidirectional relationship in which market panic shapes complaint composition rather than the opposite. The framework therefore provides regulators with a forward-looking, high-confidence early-warning signal to predict and mitigate consumer-complaint pressure.