This paper evaluates how purchases of wealth management products (WMPs) influence the performance of Chinese non-financial listed companies. Our main finding is that purchasing WMPs enhances firm performance, but the relationship shows an inverted U-shape: when WMP investment exceeds 62.57% of total assets, its positive effects diminish and ultimately harm performance. Heterogeneity analysis reveals that the performance gains are concentrated among non-state-owned enterprises (non-SOEs), while state-owned enterprises (SOEs) experience no significant benefits or even negative effects. Furthermore, the positive impact of WMPs is more pronounced in firms with higher leverage, abundant cash holdings or lower top-shareholder concentration.

We examine how investors who experienced paper gains during a bubble-crash episode, deemed as investors “escaping from death”, adjusted their future risk-taking. Using detailed transaction-level data and a quasi-experiment based on an unanticipated government intervention in the 2007–08 Chinese stock market, we find that investors who “escaped from death” reduce risk-taking behaviors over the next five years. The evidence shows that the change in risk taking is likely at-tributable to reference-dependent preferences. However, the effect diminishes over time and investors “escaping from death” do not exhibit a diminished tendency toward risk-taking when confronted with a stock market bubble crash again.

This paper documents that the physical appearance of CEOs, specifically excess body weight, is priced in the capital market. In the absence of explicit health disclosures,market participants interpret obesity as a proxy for latent health risks and potential managerial disrupts, thereby demanding a compensation premium. Our analysis reveals that (1) IPOs of firms with obese CEOs have lower first-day performance, (2) these firms achieve a lower valuation, (3) the stocks of these firms have lower liquidity and (4) they provide higher stock returns thereafter. A quasi-natural experiment based on the invention of anti-obesity medications provides supporting causal evidence.

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.

I study whether workplace smoking bans change how bond investors assess firm risk. Using staggered state adoption across U.S.\ states from 2002 to 2012 and a heterogeneity-robust difference-in-differences design, I find that smoking bans increase six-month cumulative abnormal bond returns by about 90 basis points. The average effect is only the starting point: the response is much larger for speculative-grade issuers and firms with low interest coverage, indicating that investors reprice the policy where downside operating risk matters most for debt values. Mechanism tests point most clearly to improved operating performance and lower worker turnover, while broader financial-constraint, liquidity, and duration channels remain close to zero. Alternative estimators, placebo diagnostics, and geographic spillover checks all support the interpretation that workplace smoking bans trigger targeted credit-risk reassessment rather than a generic regional shock. My findings connect public-health regulation to capital-market outcomes and show how non-financial policy shocks can reveal economically meaningful information about corporate credit risk.

This paper investigates how foreign institutional investors, specifically Qualified Foreign Institutional Investors (QFIIs), influence the investment strategies of Chinese mutual fund management companies (FMCs) in which they hold shares. By analysing panel data from 1,766 mutual funds managed by 44 foreign-invested FMCs in China between 2005 and 2021, we explore whether QFII-invested FMCs (Q-FMCs) learn more from their domestic counterparts (D-FMCs) than other foreign-invested FMCs (NQ-FMCs). Our findings show that Q-FMC-managed mutual funds exhibit portfolio allocations more closely aligned with local DFMCs than those managed by NQ-FMCs. This imitation is particularly pronounced when selecting new stocks, enhancing portfolio performance, but not when rebalancing existing positions. Additionally, Q-FMCs trade more actively than NQ-FMCs. Robustness checks confirm these results across various ownership structures, fund characteristics, market conditions, and regulatory changes. These findings highlight the dual role of QFIIs as both investors and learners in China’s evolving financial landscape, offering insights into how foreign capital integrates into emerging mutual fund markets, informing regulatory policy aimed at fostering cross-border financial development.

The flow-performance relation in China’s mutual fund market differs from that in developed markets (e.g., the U.S.). We find that investors actively allocate capital to poorly performing funds, generating a negative relation at the bottom of return distribution. These flows are driven mainly by increased purchases rather than reduced redemptions. We then examine the mechanisms behind this anomaly. First, investors act on rational expectations of performance reversals, with this pattern being more pronounced among funds with higher activeness. Second, product differentiation attracts heterogeneous investors when performance is weak. Third, marketing and fund family effects serve as simple signals that amplify inflows. Overall, our study provides new empirical evidence on fund investor behavior and its economic consequences in an emerging market context.

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 examines the economic consequences of China’s Three-Red-Lines policy, introduced in 2021 to cap real estate developers' leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance. Privately owned developers(POE) are hit harder than state-owned firms (SOE), with larger drops in sales and higher default risk. Using granular project-level data, we show that the policy reduces developer sales primarily by curtailing new-project supply: breached developers launch fewer projects. On the demand side, homebuyers reallocate purchases from privately owned developers to SOEs, further widening the POE-SOE gap. The policy also reduced local governments’ land-transfer revenues and increased reliance on local government financing vehicles (LGFVs) for land purchases. These LGFV-acquired parcels exhibit very low subsequent development rates, which may increase local governments’off-balance-sheet debt risks.

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.