Using a large language model (LLM), we construct a narrative sentiment index to investigate the predictability of risk premia in the Chinese Treasury bond market. Both in-sample and out-of-sample analyses demonstrate that narrative sentiment significantly and negatively forecasts future bond excess returns, yielding significant economic value for mean-variance investors. By incorporating this measure into an affine term structure model (ATSM), we identify a dual-channel effect: narrative sentiment relates positively to expected future short rates but negatively to excess returns, with the impact intensifying at longer maturities. Risk premia estimated via this sentiment-augmented ATSM exhibit more pronounced countercyclical dynamics. Finally, we establish that narrative sentiment carries forward-looking signals regarding both subjective expectations and future fundamentals, providing a coherent economic foundation for its predictive power in the bond market.

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 study investigates the pricing of convertible bonds by focusing on the underlying stock price limits, a market structural feature overlooked in conventional models, and provides a comprehensive framework integrating price limits with embedded options (including conversion, call, put and reset clauses) based on Monte Carlo simulation. Numerical analysis reveals that convertible bond prices across varying moneyness states are insensitive to price limits at low underlying stock volatility, while higher volatility amplifies the pronounced impact of price limits on in-the-money bonds. Empirical tests based on data on the Chinese convertible bond market spanning the period from 2018 to 2024 indicate an average overvaluation in the market. The result identifies that there are negative pricing errors for deep-out-of-the-money bonds and positive errors for in-the-money bonds. A trading strategy based on pricing errors yields positive returns, demonstrating the model's practical applicability.

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 explores the determinants of Chatbots adoption for investment purposes using the Unified Theory of Acceptance and Use of Technology (UTAUT). A conceptual framework is proposed, including perceived ease of use (PE), perceived usefulness (PU), social influence (SI), perceived risk (PR), and algorithm aversion (AA). Data from 506 valid responses in China are analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN). Results indicate that: (1) Different groups of people have different perception on five factors. (2) PU and SI exhibit significant positive effects on behavioral intention; PE, PU, and SI exhibit significant positive effects on use behaviors. (3) Neither PR nor AA has significant impacts on intention or behavior. (4) The importance of PU increases 71.48% from intention to behaviors stage. With the findings, we contribute to the technical adoption literature by extending UTAUT, and provide insights for investment Chatbots designers.

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.

Utilizing data from the East Money stock forum, one of China's most influential financial portals, this study examines how online discussions among individual investors influence managerial learning from stock prices to make investment decisions. We find that higher discussion intensity is associated with lower investment-q sensitivity, indicating that investors' online discussions impede managerial learning from prices. This effect operates through a decline in information asymmetry and the probability of informed trading. Furthermore, the negative impact is more pronounced when managers exhibit stronger incentives to learn from market signals. Overall, our findings support a crowding-out mechanism: online discussions crowd out the private information gathering of informed traders, thereby reducing the informativeness of prices for real investment decisions.

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.