Large language models increasingly act as informal investment advisors, a role that for human professionals is governed by suitability obligations: advice must match the customer’s investment profile (FINRA Rule 2111; SEC Regulation Best Interest, 2019). Whether LLM advice is personalized in this normative sense—or merely stereotyped—is unknown. We audit six model arms from five vendors (2,396 unique single-turn queries, 2026-07-29; four vendors enter inferential analyses) by injecting synthetic user personas manipulating Big Five neuroticism and conscientiousness plus capital size, and score recommended five-stock portfolios against realized market data. Persona effects replicate in every arm analysed inferentially: low conscientiousness raises recommended volatility by 10–25% and increases portfolio beta by 40–96%, while capital size is largely ignored—a suitability inversion. The sign of the neuroticism effect splits across vendors, indicating that models have internalized different stereotypes rather than any consistent normative principle. Removing the stated risk preference amplifies persona spread by 55%, a dose-style signature of stereotype filling. Rationale texts show personas are explicitly mirrored in reasoning, yet this language accounts for under 15% of the behavioral effect: the persona effect resides in portfolio selection rather than in the accompanying rhetoric. We release all logs, prompts, and code.