高金教授多篇论文入选2025欧洲金融协会年会，严冬获Review of Finance最佳非投资论文奖 | SAIF动态

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

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?

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

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）

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.