This paper investigates the impact of physical climate risk, measured by local temperature shocks, on corporate debt concentration. Using a comprehensive dataset of Chinese A-share listed companies from 2003 to 2022, we find that firms exposed to more frequent extreme heat events significantly decrease their debt concentration. This is evidenced by a lower Herfindahl-Hirschman Index of debt types and a higher number of distinct debt instruments employed. This finding is robust across a battery of tests, including alternative measures for both temperature shocks and debt concentration, as well as various model specifications. Mechanism analysis reveals that this strategic diversification is driven by firms’ operational vulnerability: the effect is more pronounced for firms with a higher proportion of production workers, a less-educated workforce, and greater reliance on automation, indicating that neither human labor nor robotic substitution is immune to heat-related productivity risks. Further heterogeneity analysis shows that the response is concentrated in firms with the motivation or capacity to act: non-state-owned enterprises, firms that are less financially constrained, those with strong accounting conservatism, and larger, geographically diversified firms. Our study contributes to the literature by identifying debt structure as a key channel for corporate climate adaptation and by introducing physical climate risk as a novel determinant of debt concentration.

Using 4724 A-share listed firms in the Chinese stock market from 2009 to 2024, we investigate how CPU, an index constructed from textual analysis of major Chinese newspapers, affects investors' behavior, measured by turnover ratios and abnormal returns. The baseline regression shows that higher CPU significantly reduces stock turnover ratio and abnormal returns, indicating heightened investor caution and negative sentiment. Extended regression with provincial-level CPU further supports these findings. Heterogeneity analysis reveal that low-carbon firms, non-state-owned enterprises (non-SOEs), and firms in Central and Western China are more negatively affected by CPU, while high-carbon firms and SOEs are relatively resilient when facing higher CPU, mainly due to expected policy support and institutional investor dominance. Further mechanism analysis uncovers that ESG performance moderates CPU's effects. Firms with higher ESG ratings experience smaller valuation declines but lower trading volumes. In addition, consumer sentiment mediates CPU's effects. CPU suppresses public confidence, which in turn reduces investor participation and firm valuations. These findings highlight the importance of improving policy transparency, promoting standardized ESG disclosure, and mitigating retail investor overreaction. The study contributes to behavioral and climate finance literature by linking policy-driven uncertainty with micro-level investor responses in a key emerging market.

We study how payment protection reallocates financing within buyer-dominated supply chains. Using China’s 2020 Regulation on Securing Payments to SMEs and its 2025 revision, we estimate difference-in-differences models on quarterly data for listed Chinese firms, comparing SMEs with near-threshold non-SME firms. Both reforms reduce SMEs’ receivables burden. The initial 2020 regulation generates large and broad declines in revenue, operating profit, gross profit margins, and operating cash flow. The 2025 revision also shows real-side pressure, with lower revenue and gross margins, but the magnitude is smaller and the deterioration is less pervasive than in the 2020 episode. The adverse effects are concentrated among suppliers with high customer concentration and weak innovation-based differentiation. DiD causal forests and neural-network DiD CATE estimates recover the same dimensions as the main sources of treatment-effect heterogeneity. Payment protection improves SME suppliers’ receivables recovery, but its real effects depend on buyer power, supplier differentiation, and the maturity of the enforcement regime.

We construct six monthly abnormal weather indicators by employing physical weather data of temperature, humidity, wind speed, precipitation, sunshine hours and sky coverage in China. We compute the abnormal weather betas to proxy for abnormal weather risks and investigate the pricing effects of these risks in the Chinese stock market. We find a low abnormal weather beta premium in the cross-section, i.e., stocks with high betas to changes in the abnormal weather index significantly underperform those with low betas by 0.10% to 0.37% per month on a risk-adjusted basis. In particular, the low abnormal weather beta premiums of temperature, sunshine hours, and sky coverage are significant over the subsequent six months. The further analyses suggest that the abnormal weather betas exhibit persistent long-term predictive power and have significant explanatory power for size, value, and long-term reversal anomalies. We further explore several potential explanations for the low abnormal weather beta premiums from various aspects, including firm characteristics, industry heterogeneity, firm performance, investor behavior and institutional holdings. The results suggest that neither firm-level characteristics nor industry heterogeneity can adequately explain the low abnormal weather beta premiums. The anomalies related to abnormal weather sensitivity are likely driven by the interaction of investor behavioral bias and arbitrage barriers, rather than compensation for fundamental risk.

Investor disagreement is central to theories of trading, yet existing proxies are either low-frequency or depend on platform-specific investor labels. We develop a daily, replicable measure of disagreement for unlabeled social media and examine its relation to trading activity in China's A-share market. Using 52.3 million investor comments on CSI 300 constituents from 2016 to 2025, we implement an LLM-based stance-simulation framework in which neutral, bullish, and bearish agents generate annotated training data that are then scaled to the full corpus using three parallel pre-trained language model classifiers. This design yields two distinct disagreement measures: within-group disagreement, capturing dispersion within a shared stance-conditioned interpretation, and cross-group disagreement, capturing divergence across stance-conditioned interpretations. We find that within-group disagreement is negatively associated with abnormal trading volume, whereas cross-group disagreement is positively associated with it. The negative within-group relation strengthens during periods of high policy uncertainty, consistent with a value-of-waiting channel, while the positive cross-group relation is stronger when disagreement involves bearish interpretations and under favorable market conditions, consistent with gains from trade under competing valuation models. The results are robust to alternative assumptions about latent group weights, before-market-open measurement, alternative LLM annotation, and a post-release annotation sample. These findings show that the trading implications of disagreement depend on whether heterogeneity reflects within-stance uncertainty or cross-stance conflict. More broadly, the approach provides a scalable framework for measuring belief heterogeneity in unlabeled text environments.

This study examines how patent portfolios relate to market capitalization in an economy where intangible assets account for roughly 90% of corporate value. Using a 10-year panel (2015-2024) of 40 public firms across technology, pharmaceutical, and consumer sectors, I estimate OLS, firm fixed-effects, and generalized estimating equation models within the Hall-Jaffe-Trajtenberg valuation framework to test whether citation-weighted patent quality outperforms raw patent quantity as a predictor of market value. Results show that citation elasticity (β₃ = 0.26-0.31, p < 0.01) is approximately 2-3× larger than the elasticity of patent productivity (β₂ = 0.06-0.12), indicating that patent quality is substantially more informative for valuation than simple counts. Technical founder-CEOs sustain about 22% higher R&D intensity and exhibit a positive interaction with citation quality, while geographic filing breadth serves as a cost-effective quality proxy: only 3.3% of Chinese patents are filed internationally versus more than 70% for advanced economies. Sector heterogeneity is pronounced, with the patent-value relationship strongest in pharmaceuticals (ρ ≈ 0.71), moderate in semiconductors, and weak and often statistically insignificant in consumer goods. Case studies of NVIDIA's decade-long CUDA investment, Intel's forgone AI platform bets, and a projected 2025-2030 pharmaceutical "patent cliff" illustrate the mechanisms by which technical leadership and AI-enabled drug discovery reshape the economic returns to patents. The findings imply that conventional patent metrics systematically mismeasure innovation value and suggest that investors, corporate strategists, and policy makers should overweight citation velocity and geographic breadth-rather than raw counts-when evaluating innovation-driven market value.

This paper examines the relationship between childhood left-behind experience and household risky financial asset investment. Based on the 2010 China Family Panel Studies (CFPS) data, we find that the childhood left-behind experience raises both the probability and the share of household risky financial asset investment by enhancing personal material aspiration. Moreover, the left-behind experience depresses household investment returns by depleting human capital accumulation. The heterogeneity analyses tell us that the rural, low-age and low-income households seem to be affected more by left-behind experience. Our evidence further suggests that enhancing information accessibility and financial resource availability constitutes a critical pathway to mitigate the adverse impact of left-behind experience on household investment outcomes. Our key results are robust to a series of sensitivity checks. Our study sheds light on the far-reaching influence of personal experience on households' well-being from the perspective of portfolio choice.

Against a backdrop of economic transformation and industrial upgrading, financing constraints have become a significant barrier to the growth of microeconomic entities. Investor sentiment, a recognized source of irrationality in capital markets, is understood to influence the financing environment through various channels. Concurrently, advances in generative artificial intelligence (AI) are generating novel methods for the precise measurement of this sentiment. Leveraging data from Chinese A-share listed corporations from 2011 to 2023, this study constructs a double machine learning framework to examine the impact of investor sentiment on financing constraints and its underlying transmission mechanisms. Our findings indicate that elevated investor sentiment significantly reduces these constraints. This effect operates primarily through an enhanced capacity for debt financing, a reduction in market scrutiny, and a stimulation of consumer demand that boosts corporate revenue. Conversely, periods of subdued sentiment tend to trigger risk-averse behavior, intensify liquidity pressures, and further restrict access to capital. A heterogeneity analysis reveals that the effects of sentiment vary across industries, ownership structures, and regional levels of information infrastructure. The impact is particularly pronounced within the real estate sector and in more digitally advanced regions. Interestingly, the effect does not fundamentally differ across ownership types, suggesting a degree of market neutrality. In sum, this study offers fresh empirical evidence on the mechanisms through which sentiment drives financing behavior. It also expands the application of generative AI, demonstrating its value as a tool for financial research.

In this paper, we investigate the availability of external finance for state-owned enterprises (SOEs) and private enterprises (PEs) across all manufacturing industries in China. Employing a disequilibrium model, we find that SOEs are generally less financially constrained than PEs, but the difference is of small magnitude and has been steadily declining over time. We also reveal a positive relation between the advantage of SOEs in accessing external funds and the level of SOE monopoly at the industry level. In general, while SOEs and PEs are equally constrained in competitive downstream industries, we find a significant comparative advantage in accessing external finance for SOEs in monopolized mid- and upstream industries. The policy implication is straightforward: to finance PEs better, the Chinese government should consider breaking the existing monopolies or near monopolies of SOEs in certain industries further, rather than encouraging banks to favor PEs in their lending.

Using Chinese A-share listed firms from 2007 to 2024, this study examines whether supply chain shareholding (SCS) affects stock price synchronicity. We find that SCS significantly reduces stock price synchronicity. This finding remains robust to a series of robustness and endogeneity tests. Mechanism tests provide evidence consistent with information asymmetry and agency costs as two channels linking SCS to lower stock price synchronicity. Heterogeneity analyses show that the effect is more pronounced among firms with low supply chain concentration and those operating in unfavorable business environments. Overall, these findings identify SCS as a relationship-based governance arrangement that improves capital market information efficiency, particularly where relational governance and informal institutional substitution are more valuable.