This study investigates the impact of employee ownership on firms’ investment responsiveness to stock market feedback. Using data from Chinese listed firms, we find that employee ownership significantly enhances the sensitivity of corporate investment to Tobin’s q. This effect is more pronounced in firms with higher information asymmetry, greater labor intensity, weaker corporate governance, and higher financial distress risk. While employee ownership is linked to increased subsequent profitability volatility, it also mitigates financial mismatch and downside operating risk. Our findings also suggest that employee ownership drives a shift in corporate strategy, leading to more aggressive approaches and improved risk preferences. These results highlight the role of employee ownership in mitigating agency problems and enhancing firms’ ability to incorporate market information into investment decisions.

We interpret industry discount rates as proxies for value investment, grounded in Q theory, while capturing gambling preferences through salience theory. Integrating these perspectives, we propose a novel asset pricing model (ST-ICAPM) that unifies value investment and salience factors, evaluating its pricing efficacy across Chinese industries from 2004 to 2023. Empirical results show that investment factors tend to negatively predict future returns, while growth factors command risk premia. However, profitability factors exhibit limited explanatory power. Investor expectations are primarily driven by profit growth, emphasizing the need to enhance profit stability for a value-oriented market. Salience intensity, especially when measured via eigenvector centrality within industry networks, serves as a strong negative predictor of returns, emphasizing the importance of conceptual connections over purely economic linkages in shaping investor behavior. Robust tests confirm that the ST-ICAPM outperforms benchmark models (FF3, CARHART4, FF5, ICAPM, and STCAPM) in terms of pricing power. Our findings emphasize the need to promote value investing and restrain gambling behavior as essential strategies to cultivate a resilient capital market in China.

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

This paper examines how AI investment and data assets affect corporatecredit risk. Using Chinese listed firms, we construct four complementary measures ofAI investment, asset-based, labor-based, LLM-based, and text-based, and link them tofirms’ distance-to-default. We find that benchmark-level AI investment reduces defaultrisk, while excessive ffrm-speciffc investment increases it by eroding profitability andreffecting risk-taking and competitive pressure. The dominance of this adverse effectyields a negative overall relation between AI investment and credit risk. Cash flow riskis the transmission channel: benchmark-level AI improves cash ffow quality, whereasexcessive investment worsens it. High-quality data assets complement benchmark-levelAI by stabilizing cash ffow, but this benefit fades once investment becomes excessive.Overall, the impact of AI on credit risk depends on both investment intensity and dataquality, operating primarily through cash flow dynamics.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in China's A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the S-score demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and book-to-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors' limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in China's A-share market, indicating that ESG-integrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

We use nearly a decade’s worth of panel data from European Union Chamber of Commerce in China business confidence surveys to analyse the deteriorating outlooks of EU firms in China from 2017 to 2025. All firms in China currently face challenges including slow profit growth and deflation. These circumstances have contributed to a rare drop of foreign direct investment into China over the last two years. However, certain challenges are particularly acute for foreign firms, including those from the EU. According to survey results, business sentiment among EU firms operating in China has never been bleaker. Respondents view their profitability, growth opportunities and competitiveness negatively, while fewer respondents than ever plan to expand their Chinese operations. Moreover, significant shares of respondents report recent increases in political pressure from the Chinese state and media, while nearly a third of respondents say they are siloing their Chinese operations, meaning separating them from other global activities. Disaggregated by size, sector, and years of operation in China, insightful differences emerge between the business strategies of EU firms. We broadly classify these into four categories: doubling-down, hedging, hibernating and ready to exit. EU policymakers should consider how to address the challenges EU firms in China face, such as asset-heavy sectors being ‘stuck’ in China and smaller firms lacking the capacity to operate at a loss in China’s market. The EU might need to facilitate transitions for these companies, helping them to reduce exposure to China and diversify into other emerging markets.

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.

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.

Using a sample of 501 Chinese non-financial firms listed on the Hong Kong Stock Exchange from 2008 to 2020, we find that both foreign exchange (FX) derivatives and foreign currency (FC) debt significantly reduce firms’ probability of default. We further observe that larger, non-state-owned enterprises (SOEs), Hong Kong-headquartered firms, firms operating after China’s 2015 exchange rate reform and firms under high trade policy uncertainty (TPU) are more likely to use both FX derivatives and FC debt concurrently, thereby diversifying their strategies for managing default risk. Our analysis indicates that these tools reduce firms’ default risk primarily by improving firms’ profitability, raising their likelihood of obtaining credit ratings, and increasing their use of interest rate derivatives. Importantly, we reveal that FX derivatives and FC debt act as substitutes in mitigating firms’ default risk. Notably, this substitution effect is more pronounced for larger, non-SOEs, Hong Kong-headquartered firms, firms operating after exchange rate reform and firms facing high TPU. Finally, we find that using FX derivatives significantly dampens firms’ investment, which may explain why Chinese firms tend to prefer FC debt to manage their default risk.

This study examines how firm-specific characteristics and monetary tightening influence the composition and dynamics of trade credit received by Chinese listed firms. Using panel data, the analysis distinguishes among three sources of trade credit: related parties, non-related parties, and controlling shareholders. The findings reveal a clear asymmetry in firms’ financing responses to monetary tightening: while trade credit from non-related parties declines, credit from related parties—especially controlling shareholders—increases. This underscores the strategic role of intra-group financing in buffering firms against external financial shocks during periods of constrained liquidity. Moreover, firm-specific factors such as size, profitability, market power, and ownership have differing effects depending on the source of trade credit. These effects are most pronounced when the credit is extended from controlling shareholders, reflecting the influence of intra-group trust and reduced information asymmetries. The results also highlight a substitute relationship between bank credit and trade credit, which weakens when trade credit is sourced from related parties and disappears entirely in the case of controlling shareholders. By shedding light on the distinct mechanisms of intra-group trade credit in China’s underdeveloped financial system, this study contributes to a deeper understanding of corporate financing strategies of Chinese firms.