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.

Structural credit risk models are widely used in default prediction practice, but standard formulations do not explicitly account for external support that may prevent default. We incorporate external support into the Merton and, more importantly, the Leland--Toft (LT) model with endogenous default. In the Merton model, support reduces realized default mechanically through a joint-default channel. In the LT model, anticipated support additionally raises debt value and affects equity holders' default decision. We derive closed-form expressions for the support-adjusted default boundary and default probability, and show that, under an economically relevant condition, stronger support lowers the endogenous default boundary, generating an additional indirect reduction in default risk. Empirically, we construct a newly assembled dataset of corporate debt defaults among Chinese A-share listed firms that combines bank loan defaults with bond default events. Using a composite measure of external support, we find that incorporating support significantly improves default prediction in both models. For the LT model, both the mechanical and endogenous channels contribute to this improvement, while the specification incorporating both channels delivers the strongest predictive performance. Our results highlight the importance of institutional support for structural credit risk modeling in China.

This paper examines how liquidity is priced in the Chinese corporate bond market. Unlike the pervasive liquidity premium across bonds traded in the interbank market, we document a puzzle in the liquidity-yield relation in the exchange market. Before the first significant credit event in March 2014, there was a "liquidity discount" in this market-the more illiquid a corporate bond, the lower its yield spread. This liquidity discount disappears after the first bond default event. The liquidity premium, i.e., a positive relation between illiquidity and yield, emerged when the credit condition for corporate bonds worsened after April 2018. We find that retail investors' yield-chasing behavior in the exchange market is the driving factor behind the liquidity discount effect. The liquidity discount is stronger for bonds favored by retail investors and during the period with a high intensity of yieldchasing behavior. Our empirical analysis further shows that our findings are not due to two alternative hypotheses based on the market microstructure effects of liquidity concentration and nonsynchronous trading.

This study examines the influence of extreme weather events on stock market behavior in China, focusing on the Shanghai and Hong Kong Stock Exchanges. This article’s hypothesis is that local weather affects individual investors in Shanghai more significantly due to their short-term, speculative trading habits. In contrast, institutional investors in Hong Kong are less influenced by short-term considerations due to their long-term strategies and access to resources. The Glosten-Jagannathan-Runkle Generalized AutoRegressive Conditional Heteroskedasticity (GJR-GARCH) estimator can test the hypothesis under different market conditions and volatility clustering. The analysis utilizes daily financial and meteorological data from January 1, 2009, to December 31, 2023. The GJR-GARCH estimator incorporates variables such as air pressure, humidity, sunshine hours, and temperature. The results show that extreme weather has a more pronounced effect on the Shanghai market than the Hong Kong market. Furthermore, extreme weather events influence stock turnover and volatility more than stock returns, reflecting shifts in investment behavior. The hypothesis is further tested to determine whether it remains valid during bull and bear markets, which are emotionally charged periods. The hypothesis still holds, albeit with less pronounced effects. Thus, extreme weather can impact stock market performance, with the composition of investors playing a significant role.

Using the Automatic Earthquake Information Release System (AEIRS) that China launched in 2013 as a quasi-natural experiment, which disseminates real-time seismic information to the public, we explore the impact of frequent information release on the pricing in the credit markets and whether such pricing is efficient. The results show a significant increase in loan pricing for firms with higher peak ground acceleration (PGA) scores following the AEIRS. Such effect is more pronounced in firms in regions where seismic risk was previously unrecognized. The baseline results still stand with several robustness checks including parallel trend analysis, spatial regression discontinuity design, placebo tests, and controlling for loan demand effect, etc. Moreover, the release of earthquake information does not alter the actual frequency of seismic events, ex-post loan defaults, or corporate fundamentals. The increase in loan pricing is primarily driven by banks’ overreaction induced by the availability heuristic, which in turn hinders corporate capital investment and asset growth. Collectively our empirical evidence reveals the dark side of frequent information release.

In Chinese culture, the Zodiac Year (“Benmingnian”) is widely regarded as inauspicious, reflecting a deep-rooted superstition that shapes risk perception and decision-making. Using data on 2,194 Chinese listed firms between 2003 and 2022, this study investigates whether chairpersons’ Zodiac Year superstition affects outward foreign direct investment (OFDI) strategies. We find that firms pursue more conservative OFDI strategies during the chairperson’s Zodiac Year—diversifying across more projects while reducing the size of individual investments—consistent with heightened risk aversion. The effect is amplified under greater operational uncertainty but weakens in firms with stronger formal institutional constraints, such as state ownership or political connections. It also weakens in regions more deeply shaped by Confucian traditions—a rational informal institution that curbs superstitious tendencies—and varies systematically with managerial characteristics such as gender and education. Overall, the findings reveal how culturally embedded, nonrational beliefs shape strategic decision-making in international business.

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.

This paper investigates the role of information asymmetry in how financial decision-making responsibility is allocated within households. Using data from the China Family Panel Studies (CFPS), we document that when only the wife has access to the Internet, the probability of her taking primary responsibility for household financial decision-making increases significantly. Conversely, when only the husband uses the Internet, this probability decreases. Mechanism analysis shows that Internet access expands individuals’ access to financial information, and spouses with exclusive Internet access leverage this informational advantage to influence their partners’ decisions through strategic partial information disclosure. These findings indicate that such informational advantages translate into a bargaining power premium within households.

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.

We examine the impact of government social media on capital markets using China's staggered adoption of local government microblogs. A difference-indifferences design reveals economically significant increases in firm value, with stronger effects for more content-rich, original, and interactive microblogs. We find improvements in the corporate information environment and reductions in perceived uncertainty, with larger effects for firms with limited external information coverage and in stronger business environments. During the COVID-19 Wuhan lockdown, firms in regions with more active microblogs achieved better stock performance. Overall, government social media is a powerful tool for enhancing transparency, firm valuation, and market resilience.