This paper studies how mutual fund managers update their beliefs as macroeconomic conditions change. Using regulator-mandated reports from Chinese mutual funds, we measure the intensity of belief updating from year-over-year changes in stated outlooks and decompose those updates into macro and micro themes. We show that belief updating is state-contingent: funds with more intensive belief updating shift their narratives toward macro (micro) topics during recessions (expansions) and concurrently reduce (increase) procyclical stock exposures and on-site company visits. This state-contingent belief updating predicts superior performance when matched to prevailing economic conditions, with macro-oriented updates paying off mainly for high-updating funds in recessions and micro-oriented updates paying off more broadly in expansions. Investors recognize this signal of skill, allocating greater flows to these funds, especially when past returns are less informative. Finally, belief updating is stronger for younger managers and for funds from newer, smaller families, consistent with signaling under career and competitive pressures.

We introduce a real-time dispersion measure based on cross-sectional stock returns explicitly designed to capture retail-driven speculative episodes. Elevated return dispersion effectively identifies periods characterized by intensified retail investor trading behaviors, driven by salience, diagnostic expectations, and extrapolative beliefs. During these high-dispersion states, momentum strategies collapse, and short-term reversals become dominant. Conditioning momentum strategies on our dispersion measure resolves the longstanding puzzle of missing momentum in retail-intensive markets such as China, substantially enhancing profitability. A dynamic rotation strategy between momentum and short-term reversal portfolios guided by dispersion states achieves annualized Sharpe ratios nearly double those of static approaches. Extending our analysis internationally, we employ Google search trends as proxies for retail investor attention, confirming that dispersion robustly predicts momentum and reversal returns globally. Our findings underscore the behavioral channel through which retail-driven speculation conditions momentum dynamics, providing clear implications for dynamic portfolio management strategies.

We develop a deep-visualization framework for timing the factor zoo. Historical factor return trajectories are converted to two complementary image representations, which are then learned by convolutional neural networks (CNNs) to generate factor-specific timing signals. Using 206 equity factors, our CNN-based forecasts deliver significant economic gains: timed factors earn an average annualized alpha of about 6\%, and a high-minus-low strategy yields an annualized Sharpe ratio of 1.22. The outperformance is robust to transaction costs, post-publication decay, and factor category-level analysis. Interpretability analyses reveal that CNNs extract predictive signals from path boundaries and regime shifts, capturing patterns orthogonal to investor attention.

This study develops a novel deep learning framework for bank distress prediction, designed to overcome the limitations of static network analysis and to enhance model interpretability. We propose a Spatio-Temporal Attention Network that uniquely captures the time-varying nature of systemic risk. Methodologically, it introduces two key innovations: (1) a dynamic interbank network whose connection weights are adjusted by the volatility of the Shanghai Interbank Offered Rate (SHIBOR), reflecting real-time market liquidity changes; and (2) a dual spatio-temporal attention mechanism that identifies critical time steps and pivotal contagion pathways leading to a distress event. Empirical results demonstrate that the model significantly outperforms traditional benchmarks across key metrics including accuracy and F1-score. Most critically, the architecture proves exceptionally effective at reducing Type II errors, substantially minimizing the failure to identify at-risk banks. The model also offers high interpretability, with attention weights visualizing intuitive risk evolution patterns. We conclude that incorporating dynamic, liquidity-adjusted networks is crucial for superior predictive performance in systemic risk modeling.

This paper examines the role of institutional investors in shaping pricing dynamics within China’s nascent infrastructure Real Estate Investment Trust market. Introduced in 2021, China’s REITs have rapidly gained policy and market attention as a tool for financing large-scale infrastructure projects through equity-based securitisation. Unlike mature REIT markets, China’s infrastructure REITs are characterised by a high concentration of institutional ownership dominated by state-owned financial institutions. Using panel data on first 9 REITs from May 2021 to April 2024, we find that institutional ownership significantly boosts the premium to net asset value. This effect operates primarily through two channels: reduced market liquidity and increased idiosyncratic return volatility, likely reflecting institutions’ trading activity and informational advantages. The findings highlight how institutional capital serves as a confidence signal in China’s emerging REITs ecosystem. The study contributes to the global REITs literature by offering insights from an emerging market context and provides policy recommendations to guide China’s REITs market development toward greater transparency, diversity, and long-term resilience.

This paper examines why tax incentives fail to stimulate participation in China's third-pillar commercial pension insurance, emphasizing the role of rational inattention. Using household survey data from China Family Panel Studies (CFPS) spanning 2014-2022 and a difference-in-differences-in-differences (DDD) design, we find that pilot policy generated a statistically insignificant average effect on participation, with rational inattention - proxied by financial literacy - explaining much of its ineffectiveness. We develop a dynamic consumption-portfolio model featuring costly information acquisition, and then resolve limitations of standard models through a dynamic framework with distinct savings channels and policy-focused rational inattention. The models show that rational inattention distorts perceptions of tax benefits and wage growth, raising participation costs, while multiple savings channels dilute incentives. Only households with higher financial literacy substantially respond to the policy. Our results reveal how cognitive frictions undermine pension reform and offer implications for designing behaviorally-informed retirement schemes.

This study applies large language models (LLMs) to measure technological links and examines its predictive power in the Chinese stock market. Using the BAAI General Embedding (BGE) model, we extract semantic information from patent textual data to construct the technological momentum measure. As a comparison, the measure based on traditional International Patent Classification (IPC) is also considered. Empirical analysis shows that both measures significantly predict stock returns and they capture complementary dimensions of technological links. Further investigation through stratified analysis reveals the critical role of investor inattention in explaining their differential performance: in stocks with low investor inattention, IPC-based measure loses its predictive power while BGE-based measure remains significant, indicating that straightforward information is fully priced in while complex semantic relationships require greater cognitive processing; in stocks with high investor inattention, both measures exhibit predictability, with BGE-based measure showing stronger effects. These findings support behavioral finance theories suggesting that complex information diffuses more slowly in markets, especially under significant cognitive constraints, and demonstrate LLMs’ advantage in uncovering subtle technological connections that traditional methods overlook.

This study aims to examine the causal effect of carbon regulatory risk on corporate bond yield spreads in emerging markets through empirical analysis. Exploiting China's commitment to peak CO2 emissions before 2030 and achieve carbon neutrality before 2060 as an exogenous shock to an unexpected increase in carbon regulatory risk, we perform a difference-in-difference-in-differences (DDD) strategy. We find that exposure to carbon regulatory risk leads to an increase in bond yield spreads for carbon-intensive firms located in regions with stricter regulatory enforcement. This positive relationship is more pronounced for firms with financing constraints, belonging to more competitive industries, and located in regions with a high marketization process. We further identify that higher earnings uncertainty and increased investor attention serve as two mechanisms by which carbon regulatory risk influences the yield spreads of corporate bonds. Moreover, the spread decomposition reveals that the rise in bond yield spreads after an increase in carbon regulatory risk is primarily driven by the rise in default risk rather than the rise in liquidity risk. Overall, our findings highlight the importance of considering carbon regulatory risk exposure in financial markets, especially in developing economies like China.

We examine how social networks shaped by high-speed rail connections influence investor attention and market reactions to earnings announcements in China. Firms in high-centrality cities exhibit stronger immediate and subsequent responses in investor attention, stock price, and trading volume to earnings news. Further analysis shows that earnings-induced local attention predicts future attention spillovers to intercity investors, amplifying both price and volume reactions after announcements. Overall, these findings indicate that high-speed rail networks foster investor social networks that facilitate the dissemination of firm news and help explain predictable patterns in investor behavior and market pricing.

We examine how organizational learning and organizational disruption jointly arise when Chinese audit firms have U.S. cross-listed clients and which effect dominates. Among public companies listed only in China, we define the treatment group as companies audited by Chinese audit firms serving at least one U.S. client, similar companies audited by firms without U.S. clients as the control group. Survey evidence indicates strong incentives and opportunities to learn from U.S. engagements and frequent learning activities in treatment audit firms. The archival evidence however shows that their domestic audit quality declines relative to the control group. The effect is more pronounced when U.S. clients demand more audit resources, when domestic clients are more sensitive to limited audit attention, and when U.S. and domestic clients are more similar. Overall, our findings indicate a negative externality of U.S. cross-listing audit when resource constraints hinder an effective firm-wide learning.