This paper examines the impact of Exchange-Traded Fund (ETF) ownership on corporate earnings management. We find that ETF ownership is associated with a significant reduction in earnings management, and this result remains robust across a wide range of endogeneity tests and robustness checks. Further analyses reveal that ETFs exert a pronounced mitigating effect on sales manipulation, production manipulation, and expense manipulation. Mechanism tests indicate that ETFs curb earnings management by improving stock liquidity and strengthening external monitoring. We also find that the influence of ETFs is stronger in private firms, in firms with lower information transparency, and in firms with CEO duality, suggesting that ETFs serve as a more prominent external governance force when internal governance mechanisms are relatively weak. Overall, this study enriches the literature on the economic consequences of ETFs and provides new empirical evidence that financial innovation in emerging markets can help alleviate the information risk faced by investors.

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.

We examine the information content of the call-put implied volatility spread (IVS) of Shanghai Stock Exchange 50 ETF options. Empirically, the IVS significantly and negatively predicts future SSE50 ETF returns at both weekly and monthly horizons. This predictability is robust both in-sample and out-of-sample, which stands in contrast to prior evidence from the U.S. options market. We explore several potential explanations and show that the IVS is closely linked to the option-cash basis. Its predictability is consistent with the model of Hazelkorn, Moskowitz, and Vasudevan (2023), where the option-cash basis reflects liquidity demand common to both options and underlying equity markets.

We study asset demand and price formation in a two-asset rational expectations equilibrium with narrow thinking, where traders imperfectly coordinate decisions across assets under non-nested price information. When the price of one asset increases, cross-asset inference from prices reduces expected demand for the other asset, which feeds back into the demand response for the original asset. Narrow thinking weakens internal coordination and amplifies reliance on price-based inference. As a result, more severe narrow thinking leads to higher own-price elasticities. The model delivers sharp implications for market liquidity and price informativeness in the presence of bounded rationality.

This paper introduces reinforcement learning to examine the effect of trading on noise in a dynamic limit order market equilibrium. It shows that intensive noise liquidity provision (consumption) increases speculators' liquidity consumption (provision), improving (reducing) market liquidity. Channeled by uninformed chasing and informed aggressive liquidity provision, the increasing noise liquidity provision and consumption, respectively, improve price efficiency, generating a U-shaped price efficiency to the noise trading uncertainty on liquidity provision and consumption. Associated with a hump-shaped (U-shaped) profitability for the informed (uninformed) at a U-shaped noise trading cost in the noise trading uncertainty, this implies that, at increasing noise trading cost, intensive noise liquidity provision improves market liquidity, price efficiency, order profitability of informed traders, and reduces the loss, even makes profit, for uninformed traders.

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 government fiscal constraints transmit to the private sector through procurement. We exploit three rounds of VAT rate cuts in China (2017–2019) as exogenous shocks to local government revenues. Combining city-level fiscal pressure measures with 9,189 procurement contracts from A-share listed firms, we construct a firm-year exposure index weighted by procurement volumes across cities. We find that exposure to fiscally stressed government buyers significantly depresses firm valuation: a one-standard-deviation increase reduces Tobin's Q and price-to-sales ratios by 5.3% and 4.3%, respectively. This effect concentrates among private firms, those lacking industrial policy support, and firms with lower rent-seeking expenditures—precisely those with weaker bargaining power against government counterparties. Beyond valuation, such exposure leads to a subsequent deterioration in firm fundamentals, characterized by tightened liquidity constraints, reduced investment and financing, and worse information disclosure over a three-year horizon. Land finance partially buffers these effects. Our findings highlight an unintended micro-level consequence of macro fiscal policy: expansionary tax cuts designed to stimulate the private sector may inadvertently harm firms by weakening the government's capacity to fulfill procurement payments.

We exploit a unique financial regulatory tool with data-governance functions as a quasi-natural experiment to explore the determinants of bank risk-taking. The paper finds that Examination Analysis System Technology (EAST) reduces bank risk-taking. This result is more pronounced in banks with higher capital adequacy ratios and higher liquidity levels. We also find that the inhibitory effect of EAST on bank risk is more significant for banks in eastern regions and listed banks. Our findings highlight the positive impact of data regulation on promoting financial stability.

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.