大语言模型在经济金融领域的应用——研究评述、学术应用及未来展望  

A Survey of Large Language Models in Economics and Finance:Model Evolution,Academic Applications,and Future Prospects

作　　者：赵宣凯 宝恩德尔[1] 左从江 李涛 ZHAO Xuankai;BAO Endeer;ZUO Congjiang;LI Tao(School of Economics,Central University of Finance and Economics,Beijing100081,China)

机构地区：[1]中央财经大学经济学院 [2]中央财经大学互联网经济研究院

出　　处：《金融评论》2025年第1期69-93,156,157,共27页Chinese Review of Financial Studies

基　　金：国家社科基金重大项目《数字经济高质量发展的创新与治理协同互促机制研究》(项目批准号:22&ZD070);国家社科基金一般项目《冲击机理、经济后果与政策应对视角下的全球不确定性与中国经济韧性研究》(项目批准号:24BJL053);北京市高校高精尖学科“战略经济与军民融合”交叉学科(项目批准号:GJJ2019163)的资助。

摘　　要：本文追溯大语言模型的演进过程,梳理评述经济金融领域相关文献,总结模型优势以及典型学术应用流程,归纳模型潜在问题并展望未来前景,以期为应用此类模型深入分析中国特色经济金融问题提供科学依据。首先,本文系统概括从统计模型到大语言模型四个阶段的发展历程和技术特征。其次,本文从宏观金融政策预期、金融市场投资行为和企业微观决策三重视角,梳理了大语言模型在经济金融学术研究中的前沿应用,包括宏观政策文本分析、宏观经济预期、经济主体决策模拟、金融市场情绪分析、资产价格预测、投资风险管理、企业经营决策、创新活动以及劳动市场行为等诸多方面。相较传统方法,参数庞大的大语言模型具备一定的逻辑推理能力,能更准确地从海量数据中提取关键信息判别经济主体行为。由于模型学术应用尚未有一致的框架,本文还总结了模型应用的一般步骤,并基于识别企业文化介绍典型模型应用流程。最后,本文对于大语言模型存在的模型幻觉、逻辑推理谬误、算力与资金成本较高等三方面问题进行了讨论。在大语言模型仍不断迭代完善的背景下,未来应用需注重可解释性,拓展图片、视频等多模态数据处理,关注经济金融研究范式的改变,并重视模型应用背后的治理问题。In late 2022,ChatGPT rapidly gained hundreds of millions of users worldwide due to its remarkable performance,sparking global interest in large language models(LLMs).These models offer significant technical support for text comprehension,enhancing the accuracy of unstructured data processing,and demonstrate vast potential for applications in economics and finance.This study provides a comprehensive review of the evolution of LLMs,their advantages and limitations in text understanding,and their academic applications and challenges.First,we outline the development of language models from statistical models to LLMs across four stages,highlighting key technical advancements.We then emphasize the superior semantic understanding and reasoning capabilities of LLMs compared to traditional text analysis methods,while also identifying potential challenges in their use.LLMs can extract critical information from text and evaluate economic agents'behaviors more accurately and rationally.This study explores specific applications of LLMs in economics and finance,including policy text analysis,macroeconomic forecasting,labor market structure analysis,corporate information disclosure interpretation,market sentiment assessment,and asset price prediction.As LLMs continue to evolve,future applications should focus on balancing effectiveness with cost and expanding to analyze multimodal data,such as images,audio,and video.In conclusion,by outlining the overall development trends and academic applications,this study aims to provide practical guidance and references for the use of LLMs,particularly in analyzing issues within China's economic and financial sectors.

关 键 词：大语言模型 ChatGPT 生成式人工智能 

分 类 号：F83[经济管理—金融学]