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.

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.

The inequality of opportunity is a fundamental driver of the widening income gap among rural residents, yet it remains to be empirically tested whether the equalization of public services can narrow this gap. Drawing on data from the Chinese General Social Survey (CGSS), this paper constructs a multi-dimensional index of basic public service equalization that includes education, social security, labor and employment, housing, and cultural and sports services. It decomposes the pathways through which rural income inequality is generated and measures separately the total and direct contributions of circumstances to the income gap. The findings reveal that the equalization of public services has significantly alleviated income inequality among China's rural residents—particularly the portion attributable to differences in circumstances, known as the inequality of opportunity. This result remains robust after addressing endogeneity issues and conducting a series of robustness checks. Mechanism analysis indicates that this effect operates primarily through three channels: improving social participation among rural residents, expanding their non-agricultural employment opportunities, and unlocking the developmental potential of the female labor force. Among various public services, improvements in education, social security, and employment play a particularly prominent role in reducing the inequality of opportunity. Further quantile regression analysis shows that the mitigating effect of basic public service equalization on inequality of opportunity is more pronounced among samples with higher initial levels of inequality, suggesting a targeted “short‑board” compensating characteristic.