  This study examines the risk preferences of Large Language Models (LLMs) and
how aligning them with human ethical standards affects their economic
decision-making. Analyzing 30 LLMs reveals a range of inherent risk profiles,
from risk-averse to risk-seeking. We find that aligning LLMs with human values,
focusing on harmlessness, helpfulness, and honesty, shifts them towards risk
aversion. While some alignment improves investment forecast accuracy, excessive
alignment leads to overly cautious predictions, potentially resulting in severe
underinvestment. Our findings highlight the need for a nuanced approach that
balances ethical alignment with the specific requirements of economic domains
when using LLMs in finance.
