According to the source, a study reports that prompting leading AI models to adopt a female persona results in measurably more risk-averse choices versus default or male personas, indicating prompt-induced shifts in risk preferences in AI decision-making, source: the source. For trading, greater risk aversion mechanically reduces optimal leverage, position size, and turnover under mean-variance and expected-utility frameworks, requiring more conservative sizing and tighter stop-losses in automated strategies, source: Markowitz 1952; Merton 1971; Sharpe 1966. Crypto and DeFi trading bots that embed LLM decision modules should audit persona prompts and recalibrate risk limits and Value-at-Risk to prevent unintended underexposure or regime-dependent drawdowns, source: Jorion 2007; RiskMetrics 1996. Before live deployment, backtests should compare default versus persona-prompted policies on volatility, max drawdown, turnover, and hit rate to quantify prompt-driven risk bias in crypto markets, source: Bailey et al. 2014 Probabilistic Sharpe Ratio; Jorion 2007.