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北大经济史学名家系列讲座
第216讲
新兴大国怎样跨越“市场规模陷阱”？
主讲人：
欧阳峣（湖南师范大学教授）
时间：
2024年11月14日（周四）
10：10-12：00
地点：
北京大学经济学院305会议室
主持人：
周建波（北京大学经济学院经济史学系主任、教授）
评论人：
兰日旭（中央财经大学经济学院教授）
熊金武（中国政法大学商学院教授）
管汉晖（北京大学经济学院长聘副教授）
赵一泠（北京大学经济学院助理教授）
主讲人简介：
欧阳峣，湖南师范大学商学院教授，大国经济研究中心主任。兼任牛津大学技术与管理发展中心高级研究员，第19届国际熊彼特学会主席。曾任湖南商学院党委书记，湖南师范大学副校长。入选第一批国家万人计划哲学社会科学领军人才，文化名家暨“四个一批”人才，新世纪百千万人才工程国家级人选，享受国务院政府特殊津贴专家。主要研究世界经济和发展经济学，在大国经济发展理论领域做出开拓性和系统性贡献。主持国家社会科学基金重大项目4项和重点项目2项，在《中国社会科学》《经济研究》《管理世界》等期刊发表论文130余篇，《新华文摘》全文转载16篇；国家哲学社会科学成果文库著作2部，荣获教育部高等学校科学研究优秀成果奖（人文社科）一等奖1项和二等奖1项，张培刚发展经济学奖1项，安子介国际贸易奖1项，世界政治经济学杰出成果奖1项，湖南省哲学社会科学优秀成果奖一等奖3项。
主办单位：
北京大学经济学院经济史学系
北京大学社会经济史研究所
北京大学外国经济学说研究中心
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北大经院工作坊第988场
Deep Learning for Search and Matching Models
宏观-数字经济联合工作坊
主讲人：
杨雨成（苏黎世大学和瑞士金融学院助理教授）
主持人：
（北大经院）李博
时间：
2024年11月15日（周五）
10:00-11:30
地点：
北京大学光华管理学院新楼478会议室
主讲人简介：
Yucheng Yang is an Assistant Professor of Finance at the University of Zurich and Swiss Finance Institute. He has been a visiting faculty at Yale and NYU. His research interests span macroeconomics, finance, and machine learning. He has developed deep learning methods to study quantitative macro and finance models with rich heterogeneity. Professor Yang has received the CICF Yihong Xia Best Paper Award, CES Gregory Chow Best Paper Award, and a Swiss SNF Grant on "Heterogenous Agent Macro-Finance: Models and Methods". He holds a Ph.D. from Princeton University and dual Bachelor degrees from Peking University.
摘要：
We develop a new method for characterizing global solutions to search and matching models with aggregate shocks and heterogeneous agents. We formulate general equilibrium as a high dimensional partial differential equation (PDE) with the distribution as a state variable. Solving this problem has previously been intractable because the distribution impacts agent decisions through the matching mechanism rather than through aggregate prices. We overcome these challenges by developing a new deep learning algorithm with efficient sampling in a high dimensional state space. This allows us to study search markets that are not "block recursive" and compute variables (e.g. wages and prices) that were previously unattainable. In applications to labor search models, we show that distribution feedback plays a more important role when aggregate shocks have an asymmetric impact across agents. Business cycles have a "cleansing" effect by amplifying positive assortative matching in recessions, and the magnitude of the countercyclicality depends on the bargaining process between workers and firms. In applications to OTC markets, we show how default risk impacts bond prices across different maturities.
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北大经院学术午餐会第196期
Does Confidence (in Fund Skill Estimates) Matter for Investors?
主讲人：
罗荣华（西南财经大学教授）
主持人：
高明（北京大学经济学院长聘副教授）
时间：
2024年11月15日（周五）
10:30-12:00
地点：
北京大学经济学院305会议室
主讲人简介：
罗荣华现任西南财经大学教授、博士生导师，金融学院院长、中国金融研究院院长，西南财经大学“光华杰出学者计划”青年杰出教授。他先后于南开大学数学科学学院和北京大学光华管理学院取得理学学士和经济学博士学位，主要关注金融计量、资本市场和商业银行相关领域的研究。在《经济研究》《管理世界》《经济学（季刊）》和Annals of Statistics、Journal of Banking and Finance、Journal of Business and Economic Statistics等国内外期刊发表论文60余篇，著有《FOF管理：策略与技术》《家庭资产配置与风险管理》等专著和教材。
摘要：
Existing literature often assumes that mutual fund investors learn fund skills solely from individual fund performance, overlooking the critical role of model uncertainty in the fund return-generating process. This paper introduces the framework of “learning with model uncertainty,” where sophisticated investors balance a bias-variance tradeoff between individual fund performance and the group-average performance of comparable funds. We develop a novel metric, Confidence, derived from pairwise t-tests, to capture investors’ relative confidence in individual fund performance. Using data from US actively-managed domestic equity mutual funds, we demonstrate that Confidence significantly increases flow-performance sensitivity while reducing sensitivity to group-average performance. This framework offers explanatory power for fund flows comparable to, or exceeding, that of Morningstar ratings. Additionally, fund flows predicted by this framework positively forecast future fund performance. Finally, high-Confidence funds, particularly those with extreme performance, display a strategic shift from systematic to idiosyncratic risk. Our study thus broadens the understanding of investor confidence and provides a more comprehensive perspective on investor learning.
供稿：科研与博士后办公室