报告题目：Uncommon Factors and Asset Heterogeneity in the Cross Section and Time Series

报 告 人： Lin Cong（Cornell University）

报告时间：2023年7月4日（周二）9:00-10:30 

报告地点：ZOOM平台在线交流（会议ID：938 5703 5239）

ZOOM App下载链接：//zoom.us/download, 亦可点击以下链接直接参加会议：//ucincinnati.zoom.us/j/93857035239

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【报告人简介】

Lin William Cong is the Rudd Family Professor of Management and a tenured Professor of Finance at the Johnson Graduate School of Management at Cornell University, where he is the founding faculty director for the FinTech Initiative and the founder of the Digital Economy and Financial Technology (DEFT) Lab. He is also a Finance editor at the Management Science, an associate editor for multiple leading academic journals, a Research Associate (Asset Pricing) at the National Bureau of Economic Research (NBER), a faculty scientist at the Initiative for Cryptocurrencies & Contracts (IC3), a Kauffman Foundation Junior Faculty Fellow, founder of multiple international research forums, a Poets & Quants World Best Business School Professor, and a 2022 Top 10 Quant Professor. Prior to joining Cornell, he served on the faculty of the Finance Group at the University of Chicago Booth School of Business where he created courses on “Quantimental Investment,” was doctoral fellow at the Stanford Institute for Innovation in Developing Economies, and a George Shultz Scholar at the Stanford Institute for Economic Policy Research. He has advised leading investment and FinTech firms including Ava Labs, Blackrock, Chainlink, DataYes, and Modular Asset management. He has also been invited to consult for or advise government and regulatory agencies such as the Department of Justice, New York State Department of Financial Services, U.S. Securities and Exchange Commission, Bank of Canada, the FBI, and the New York State Office of Attorney General.

Professor Cong’s research spans applied finance theory, FinTech, digital economy, entrepreneurship, and AI in finance. He and his coauthors are the first to introduce goal-oriented search and interpretable AI for finance, tokenomics that includes categorization of tokens, cryptocurrency pricing, and optimal token monetary policy design, centralization issues and dynamic incentives in blockchains and DeFi, and have developed tools for detecting market manipulation and better FinTech regulation, among others. He has won over 40 best paper prizes and research grants and is a highly sought-after keynote speaker at various international conferences and forums. 

【内容摘要】

We introduce the Bayesian Clustering Model (BCM), a new general frame work combining decision tree and Bayesian variable selection for modeling panel data with grouped heterogeneity, with an emphasis on economic guidance and interpretability. We apply BCM to estimating uncommon factor models for data-driven yet economically motivated asset clusters and macroeconomic regimes, utilizing marginal likelihood to address parameter/model uncertainties and overfitting in tree growth. We find strong evidence for (i) cross-sectional heterogeneity linked to (nonlinear interactions of) idiosyncratic volatility, size, and value, and (ii) structural changes in factor relevance predicted (i.e., macro-instrumented) by market volatility and valuation. We identify MKTRF and SMB as common factors, together with multiple uncommon factors across characteristics-managed, market-timed clusters. The learned grouped heterogeneity also helps explain volatility- or size-related anomalies, offers effective test assets, and renders many popular factors irrelevant (thus mitigating the “factor zoo” problem). Overall, BCM outperforms benchmark common-factor models, e.g., achieving an out-of-sample cross-sectional R2 exceeding 25% for multiple clusters and an investment Sharpe ratio tripling that of the tangency portfolios built from Fama-French double-sorted portfolios.

撰稿：赵鹏辉  审核：史永东  单位：bet365中国备用网址