8月20日至23日，歐洲金融協會第52屆年會（European Finance Association 52nd Annual Meeting，簡稱EFA年會）在法國巴黎SKEMA商學院舉行。來自上海交通大學上海高級金融學院（高金/SAIF）的潘軍、嚴冬、陳辰、黃秋實等多位全職教授的論文成功入選。

其中，高金金融學長聘副教授嚴冬憑借獨立論文“Do Private Firms (Mis) Learn from the Stock Market?”在年會上斬獲2025年

Review of Finance

作為全球金融學術領域最具影響力的頂級平臺之一，EFA年會自1974年成立以來，始終由歐洲管理發展基金會（EFMD）贊助，并與歐洲管理高級研究所（EIASM）緊密合作，其論文質量、學者參與度及研究前沿性均代表全球金融研究的最高水準。

本屆年會共設72個分會場，每日設置9場平行會議，聚焦“資產價格與機構投資者”“人工智能在金融領域的應用”“資產定價中的信念”“公司治理：股東與董事”等前沿方向，吸引了全球頂尖金融學者參與。

在本屆年會上，高金教授的學術成果備受矚目。除了嚴冬教授的獲獎論文之外，還包括由高金金融學教授、高金講席講授潘軍與合作者（Grace Xing Hu, Zhao Jin）共同撰寫的論文“The Stock-Bond Correlation: A Tale of Two Days in the U.S. Treasury Market”，高金金融學助理教授陳辰撰寫的論文“Network Factors for Idiosyncratic Volatility Spillover”，以及高金金融學助理教授黃秋實與合作者（Bo Bian, Ye Li, Huan Tang）共同撰寫的論文“Data as a Networked Asset”。

同時，嚴冬教授還在公司金融分會場“CEO and Director Incentives”（CEO和董事激勵）中擔任論文評論人。高金金融學助理教授郭然、趙瀟也參加了本次年會。

高金教授多篇論文入選EFA年會并斬獲重要獎項，充分展現出高金在金融學術研究方面的深厚實力。

作為一所按照國際一流商學院模式辦學的金融學院，高金擁有一支國際一流、亞洲和國內領先的師資隊伍。覆蓋金融、會計、管理等不同學科的80余位教授均來自賓夕法尼亞大學、麻省理工學院、斯坦福大學、西北大學、耶魯大學、芝加哥大學等世界一流學府。他們以豐富的國際研究、教學和實踐經歷，成為推動高金建設世界一流金融學院的核心力量。

入選2025EFA年會的高金教授論文介紹：

The Stock-Bond Correlation: A Tale of Two Days in the U.S. Treasury Market （Grace Xing Hu, Zhao Jin, Jun Pan）

潘 軍

高金金融學教授、高金講席教授

Abstract

Motivated by the central importance of U.S. Treasury (UST) and the increasing concern over its resilience, we construct a high-frequency measure of stock-bond correlation to capture UST safety, and more importantly, its vulnerability. On days with highly negative stock-bond correlations, UST serves as the premier safe asset with widening convenience yield and decreasing term premium. By contrast, on days with high stock-bond correlations, UST becomes a source of risk with increased volatility and term premium. Prominent bond risk days captured by large increases of our stock-bond measure are FOMC announcements, the 2020 dash for cash, and the 2021 inflation surge.

Do Private Firms (Mis) Learn from the Stock Market?

嚴 冬

高金金融學長聘副教授

Abstract

This article examines whether and to what extent private firms learn from the stock market. Using a large panel data set for the UK, I find that private firms’ investment responds positively to the valuation of public firms in the same industry. The sensitivity increases with price informativeness. To further pin down the information channel, I construct a price noise measure based on public firms’ unrelated minor segments and show that it positively affects the investment of private firms in the major-segment industry. The results are consistent with models featuring learning from noisy signals and are not driven by alternative channels in the absence of learning. My findings suggest that the stock market can have real effects on private firms through an information-spillover channel, even when these firms do not list their shares on the stock exchanges.

Network Factors for Idiosyncratic Volatility Spillover

陳 辰

高金金融學助理教授

Abstract

I develop a dynamic production-based network model to examine the economic and asset pricing implications of inter-sector idiosyncratic volatility spillovers. I introduce two time-series factors to capture the evolving dynamics of pairwise idiosyncratic volatility spillovers and show that they shape the persistent dynamics of aggregate volatility in equilibrium and are priced as volatility risk factors. Empirically, I construct these factors using stock data and demonstrate that they predict future aggregate volatility in the direction implied by the model. Furthermore, long-short portfolios formed based on these factors generate return spreads that are unexplained by existing factor models.

Data as a Networked Asset,（Bo Bian, Qiushi Huang, Ye Li, Huan Tang）

黃秋實

高金金融學助理教授

Abstract

Data is non-rival: a firm's data can be used simultaneously by others, and information about its customers benefits other firms even across industries. How is data being shared? Using granular information on mobile app usage, functionalities, and connections with data analytics platforms, we uncover a network of inter-firm data flows. Data sharing generates comovements in operational, financial, and stock-market performances among data-connected firms, beyond what traditional economic linkages can explain, and induces strategic complementarity in firms' product-design choices. Apple’s App Tracking Transparency policy, which restricts inter-firm data flows, weakens these patterns, providing causal evidence of the role of data sharing. To explain these findings, we develop a dynamic network model of data economy, where firm growth becomes interconnected through data sharing. The model introduces a network-augmented Gordon growth formula to value data-generated cash flows, capturing direct and indirect network externalities over multiple time horizons. Our metrics of valuation centrality identify systemically important firms that disproportionately influence the data economy due to their pivotal positions within the data-sharing network.

高金項目火熱招生中…

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