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Large Language Models (LLMs) exhibit diverse and stable risk preferences in economic decision tasks, yet the drivers of this variation are unclear. Studying 50 LLMs, we show that alignment tuning for harmlessness, helpfulness and honesty systematically increases risk aversion. A ten percent increase in ethics scores reduces risk appetite by two to eight percent. This induced caution persists against prompts and affects economic forecasts. Alignment therefore promotes safety but can dampen valuable risk taking, revealing a tradeoff risking suboptimal economic outcomes. Our framework provides an adaptable and enduring benchmark for tracking model risk preferences and this emerging tradeoff.

This review article synthesizes the burgeoning literature on the intersection of (generative) ar- tificial intelligence (AI) and financial economics. We organize our review around six key areas: (1) the emergent role of generative AI as analytic tools, external shocks to the economy, and au- tonomous economic agents; (2) corporate finance, focusing on how firms respond to and benefit from AI; (3) asset pricing, examining how AI brings novel methodologies for return predictability, stochastic discount factor estimation, and investment; (4) household finance, investigating how AI promotes financial inclusion and improves financial services; (5) labor economics, analyzing AI’s impact on labor market dynamics; and (6) the risks and challenges associated with AI in financial market. We conclude by identifying unanswered questions and discussing promising avenues for future research.

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Large Language Models (LLMs) exhibit diverse and stable risk preferences in economic decision tasks, yet the drivers of this variation are unclear. Studying 50 LLMs, we show that alignment tuning for harmlessness, helpfulness and honesty systematically increases risk aversion. A ten percent increase in ethics scores reduces risk appetite by two to eight percent. This induced caution persists against prompts and affects economic forecasts. Alignment therefore promotes safety but can dampen valuable risk taking, revealing a tradeoff risking suboptimal economic outcomes. Our framework provides an adaptable and enduring benchmark for tracking model risk preferences and this emerging tradeoff.

This paper explores novel applications of quantum computing in cryptography and optimization problems. We present a comprehensive analysis of quantum algorithms and their potential impact on modern computational challenges. Our findings demonstrate significant improvements in processing efficiency and security protocols.

This research addresses a fundamental question through systematic investigation and rigorous methodology. We develop novel analytical tools and apply them to diverse scenarios, uncovering consistent patterns and relationships. Our results provide strong evidence for our central hypothesis and offer new perspectives on longstanding debates. The implications extend beyond our immediate field and suggest interdisciplinary applications. We conclude with recommendations for future research and practical implementation.

We investigate the underlying mechanisms governing complex systems through advanced computational and experimental techniques. Our research reveals previously unknown relationships between key variables and proposes a unified theoretical framework. The experimental validation confirms our hypotheses and demonstrates the robustness of our approach. These findings have important implications for both theoretical understanding and practical applications. We discuss potential extensions and future research directions.

We present a comprehensive investigation of emerging phenomena in our field of study. Our multi-faceted approach combines theoretical modeling, computational simulation, and experimental validation. The findings reveal complex interactions and non-linear dynamics that challenge conventional understanding. We propose new theoretical constructs and demonstrate their utility through extensive case studies. This research opens new possibilities for technological innovation and scientific discovery.

This study presents a novel approach to addressing fundamental challenges in the field. Our methodology combines theoretical frameworks with empirical validation, demonstrating significant improvements over existing methods. We conducted extensive experiments across multiple datasets and scenarios, revealing consistent performance gains. The results suggest potential applications in various domains and open new avenues for future research. Our findings contribute to the growing body of knowledge and provide practical insights for practitioners.

This research addresses a fundamental question through systematic investigation and rigorous methodology. We develop novel analytical tools and apply them to diverse scenarios, uncovering consistent patterns and relationships. Our results provide strong evidence for our central hypothesis and offer new perspectives on longstanding debates. The implications extend beyond our immediate field and suggest interdisciplinary applications. We conclude with recommendations for future research and practical implementation.

We investigate the underlying mechanisms governing complex systems through advanced computational and experimental techniques. Our research reveals previously unknown relationships between key variables and proposes a unified theoretical framework. The experimental validation confirms our hypotheses and demonstrates the robustness of our approach. These findings have important implications for both theoretical understanding and practical applications. We discuss potential extensions and future research directions.

This study presents a novel approach to addressing fundamental challenges in the field. Our methodology combines theoretical frameworks with empirical validation, demonstrating significant improvements over existing methods. We conducted extensive experiments across multiple datasets and scenarios, revealing consistent performance gains. The results suggest potential applications in various domains and open new avenues for future research. Our findings contribute to the growing body of knowledge and provide practical insights for practitioners.