A seminar by Professor Xiaofei Zhao from Georgetown University

Title: Corporate News and Stock Return Jumps Around the World

Abstract: What role does corporate news play in generating jumps in stock returns worldwide, and why does this relationship differ across countries? Using corporate-news data for publicly listed firms in 55 countries, we show that stock-return jumps are systematically related to news arrivals and their textual content in most markets. More importantly, we document substantial cross-country heterogeneity in the strength of this relationship. Countries with stronger news-jump effects tend to have higher Internet penetration, greater financial market development, and higher income levels, with Internet penetration emerging as the strongest correlate, consistent with broader information access and faster diffusion. This relationship remains robust when we control for other country characteristics, use alternative measures of Internet access, apply different jump identification thresholds, and focus on specific news categories. Extending the analysis to international information flows, we find evidence of cross-border spillovers: news about foreign industry peers helps explain domestic stock-return jumps beyond the effect of own-country peer news. These spillovers are stronger when the affected country has higher Internet penetration. Overall, our findings highlight the crucial role of information infrastructure and global information transmission in shaping discontinuous price discovery following corporate news across a broad set of firms, countries, and market environments

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A seminar by Professor Michael Grabchak from University of North Carolina Charlotte 

Title: TBA

Abstract: TBA

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A seminar by Assistant Professor Dongchen Zou from Indiana University

Title: Buying the Dip: Return-Contingent Retail Trades

Abstract: Using a novel dataset of daily order flows from actual retail trades for 2017–2024, we document a nonlinear and highly asymmetric relation between retail activity and past returns. Specifically, retail inflows rise sharply following large price declines, whereas selling after gains is weak. This 'buying the dip' pattern is stable across return horizons and persists through the 2020–2021 retail trading surge. We demonstrate that this behavior is not driven by stock-specific clientele effects, but is instead linked to episodes of mandated institutional selling. Retail purchases are strongest when institutional selling pressure coincides with negative returns and tightening risk constraints. Consistent with retail investors providing liquidity during distress, we find that retail purchases support market quality and lower transaction costs.

A seminar by Dr Liuhua Peng from The University of Melbourne

Title: Enhanced localized conformal prediction with imperfect auxiliary information

Abstract: There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. However, methods like localized conformal prediction (LCP) may face challenges in ensuring reliable prediction sets in regions with sparse calibration data. This paper introduces Enhanced Localized Conformal Prediction (ELCP), a novel approach that incorporates auxiliary data to refine localized prediction sets while preserving finite-sample marginal coverage guarantees. By utilizing a density-ratio-weighted kernel estimator, ELCP seamlessly integrates auxiliary and calibration data, accommodating potential distributional shifts and improving the local reliability of prediction sets. Theoretical analysis confirms that ELCP maintains marginal coverage and enhances asymptotic test-conditional coverage. Simulation results demonstrate its superior local coverage and smaller prediction sets compared to standard LCP, highlighting its effectiveness in settings with limited calibration data but available auxiliary information from related tasks.

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A seminar by Dr Adam Nie from RSFAS

Title: Spurious Factor Analysis for High-dimensional Functional Time Series

Abstract: This article explores a general factor structure for high-dimensional nonstationary functional time series, encompassing a wide range of factor models studied in the existing literature. We investigate the asymptotic spectral behaviors of the sample covariance operator under this general data structure. A novel fundamental sufficient condition, formulated in terms of a new introduced effective rank tailored to this setup, is established under which empirical eigen-analysis yields spurious results, rendering sample eigenvalues and eigenvectors unreliable for accurately recovering the underlying factor structure. This generalizes the results of Onatski and Wang (Econometrica 2021) from typical high-dimensional time series (HDTS) to the more intricate functional framework. The newly defined effective rank is rigorously analyzed through a decomposition of the effects attributable to functional factor loadings and functional factors. Contrary to the findings in the HDTS setting, empirical eigen-analysis of models with only a small number of strong non-stationary factors may still produce spurious limits in the functional framework. Therefore, additional caution is warranted when applying covariance-based statistical methods to potentially nonstationary functional data. Simulation studies are performed to determine conditions under which spurious limits occur. Real data analysis on age-specific mortality rate data from multiple locations is conducted for evidence of spurious factors induced by empirical eigen-analysis.

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A seminar by Professor Brandon Julio from University of Oregon

Title: TBA

Abstract: TBA

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A seminar by Professor Ron Masulis from UNSW

Title: How Long-Lasting Demand Shocks Affect CEO Compensation?

Abstract: We examine how long-lasting demand shocks shape CEO compensation structure and firm outcomes. For identification, we exploit persistent, Census-driven increases in U.S. government spending that expand procurement opportunities and reduce demand uncertainty for government contractors. Boards respond to positive Census shocks by increasing the convexity of executive pay, raising expected CEO pay to better align manager-shareholder incentives, whereas negative shocks reduce executive pay convexity. Contrary to the rent extraction hypothesis, these effects are driven by better-governed firms. Improved risk-taking incentives induce greater investment activity and improved operating performance, highlighting how CEO compensation design is a key channel through which firms respond to persistent demand shocks.

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A seminar by Professor Xiaoyan Zhang from Tsinghua PBC

Title: AI, Opinion Ecosystems, and Finance *

Abstract: Generative AI use for content generation is associated with divergent outcomes
on different financial social media platforms: indications of reasoning
enhancement on Seeking Alpha, and of belief distortions on WallStreetBets. On
Seeking Alpha, adoption is associated with information frictions. AI-assisted postings
tilt toward analysis/credibility, and their sentiment positively predicts future
returns. Use of AI is associated with more informative retail order flow and lower
bid-ask spreads. In contrast, AI adoption on WallStreetBets follows surges in retail
buying, and AI-assisted content is associated with emotionality and sentiment
contagion. Such content precedes higher trading volume, greater volatility,
and more lottery-like return distributions.

Co-authors: David Hirshleifer, Lin Peng Qiguang Wang, Weichen Zhang

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