Prompt repetition has recently been proposed as a simple inference-time modificationcapable of improving the performance of non-reasoning large language models(LLMs). By duplicating the input prompt, the technique aims to improve attentionutilization without incurring additional computational cost. While empirical gainshave been reported on deterministic language benchmarks, it remains unclearwhether such improvements generalize to stochastic prediction domains whereuncertainty originates from external information rather than prompt structure.In this work we conduct a systematic, multi-asset evaluation of prompt repeti-tion in financial time-series forecasting, spanning four representative instruments:GOOGL, MSFT, NVDA, and GLD. We compare a logistic-regression baselineagainst LLM predictions under both standard prompting and prompt repetition,assessing directional accuracy, Brier score, bootstrap confidence intervals, McNemarsignificance tests, and calibration reliability diagrams. Across all assets and allmetrics we find no statistically meaningful improvement attributable to promptrepetition. We further provide an information-theoretic proof showing that anytransformation preserving input entropy cannot increase predictive mutual infor-mation in noise-dominated environments. Our findings establish a clear boundarycondition for prompt-engineering techniques and underscore the necessity of domain-aware evaluation before deploying LLM inference strategies beyond natural languageprocessing.