This preprint introduces FinPersona-Bench to study whether LLM-based financial agents maintain behavioral mandates over a simulation. The paper crosses 18 models, three financial prompts associated with ENTJ, ISFJ, and INTJ, three synthetic markets, two prompt architectures, and five seeds over 200 steps. The flat market measures deviation from an author-assigned cash fraction, the crash uses maximum drawdown, and the bubble uses a binary rule for buying below and selling above a hidden fundamental value. Aggregated results report that repeating the mandate reduces flat-market cash deviation by 12.7% and crash drawdown by 12.6%, but worsens the bubble value-rule score by 8.8%. Effects depend strongly on prompt and scenario. In a flat market, repetition helps the conservative ISFJ prompt in 17 of 18 models and hurts the aggressive ENTJ prompt in 16 of 18. This interaction is the most useful descriptive result: reinforcing an instruction can improve or degrade policy depending on whether the mandate fits the environment. However, the implementation does not measure the mechanism called Mandate Salience Decay through accumulating context. The static agent is a stateless predictor. Each day makes a fresh call, re-sends the complete persona as a system message, and includes only the current observation and two portfolio values. It retains no conversation, rationales, or prior tokens. The memory agent receives the same system prompt and additionally gets a short imperative mandate in the user message. The contrast therefore identifies effects of duplication, position, wording, and immediate prompt emphasis together with portfolio trajectory; it does not demonstrate long-context forgetting. The reported 4.4x widening of the crash gap is also not a direct salience measure because it uses cumulative drawdown, market phases that change over time, and path-dependent portfolios. Nor is this a psychometric validation. MAS is distance from author-chosen cash targets .2, .5, and 1.0. The Big Five appendix tests two prompt archetypes and a numerical control without an inventory, reliability, factor structure, or criterion validation. The repository audit finds major drift: its public runner uses three models, 100 days, and temperature .2, whereas the paper reports 18, 200, and temperature 0. It lacks the stated Hugging Face/vLLM route and publishes no raw results or classifier. The market generator also uses NumPy's global RNG inside ten threads. Forty concurrent constructions labeled seed 42 produced 40 distinct markets, while five sequential constructions were identical. Static-memory pairing by seed is therefore not guaranteed by the released orchestrator, threatening the paired Wilcoxon design. Tests do not pass from a clean clone. The defensible conclusion is narrow: repeating a mandate in the current prompt heterogeneously changes a synthetic financial policy; longitudinal psychometric decay and instruction loss from accumulating context have not been demonstrated.
Research question
Do LLM-based financial agents maintain a policy compatible with risk mandates defined by prompts over 200 steps, and how does their behavior change when repeating a short mandate in each user message?