Using data from 11 major international markets that celebrate six cultural New Year holidays that do not occur on January 1, we find that stock markets tend to outperform in days surrounding a cultural New Year. After controlling for firm characteristics, an average stock earns 2.5% higher abnormal returns across all markets in the month of a cultural New Year relative to other months of the year. Further evidence suggests that positive holiday moods, in conjunction with cash infusions prior to a cultural New Year, produce elevated stock prices, particularly among those stocks most preferred and traded by individual investors.

Corporate site visits provide investors with opportunities to obtain non-standard, tailored "soft" information about the firm. In this study, we investigate the impact of information disclosed from corporate site visits on stock market stability from the perspective of stock return volatility. Our findings suggest that it is the information disclosed rather than the visits themselves that significantly reduce stock return volatility, primarily by mitigating information asymmetry. Moreover, we observe that the volatility-mitigating effect of site visits is more pronounced when the visit information better aligns with investors' concerns and when it is more effectively disseminated. Our study contributes to the literature by demonstrating that the timely disclosure of site visit details serves as a stabilizing mechanism for stock prices through effective information mining and dissemination.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power of LLM signals, we further investigate its implications for information efficiency. The LLM signals contain firm fundamental information, and it takes two days for LLM signals to be incorporated into stock prices. The predictive power of the LLM signals is stronger for firms with more information frictions, more retail holdings and for more complex news. Interestingly, many investors trade in opposite directions of LLM signals upon news releases, and can benefit from the LLM signals. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

This research study primarily examines the digital transformation’s internal mechanism promoting enterprises’ total factor productivity (TFP) based on the matching and learning mechanism. Afterward, this research article empirically examines the digital transformation’s influential mechanism on enterprises’ TFP, using the Chinese listed companies’ data on the “A” stock market for the time period ranging from 2007 to 2019. The major study findings are as follows: (1) the improvement of the digital transformation significantly increases enterprises’ TFP. The proposed conclusion remains robust after a series of robustness- and the endogeneity test. (2) Furthermore, mechanism analysis reveals that digital transformation effectively enhances enterprises’ TFP by eliminating resource misallocation in the industry. In addition to this, digital transformation relies on the mechanism of “learning by doing” to promote the technological innovation’s spillover effect; hence, effectively enhancing enterprises’ TFP. (3) Heterogeneity analysis demonstrates that the digital transformation’s impact on enterprises’ TFP is heterogeneous in the context of enterprise size, enterprise type, and enterprise ownership. Lastly, this study puts forward that government bodies should intensify the construction and investment in digital infrastructure, promote a series of institutional reforms, and support digital technological R&D practices.

We examine how Federal Open Market Committee (FOMC) meetings influence international stock returns, highlighting that the standard Fed news channel creates an even-week pattern in the United States and other highly integrated developed markets. By analyzing the Chinese market, we demonstrate that the news channel contributes to higher returns, operating in non-US countries even without international equity flows. Additionally, we identify an uncertainty channel that produces a contrasting odd-week pattern. Placebo tests indicate that the effectiveness of the uncertainty channel may depend on the financial market’s openness. Overall, our research enriches and extends the existing view on how the Federal Reserve, as the leader of central banks, shapes international stock market returns throughout the entire FOMC cycle.

Based on 31 provinces, municipalities, and autonomous regions in mainland China, this paper explores the temporal and spillover effects of the provincial COVID19 pandemic on stock returns. The results show that stock returns are significantly and negatively correlated both with the pandemic in the firm’s headquartered province (referred to as, local province), and the pandemics in other provinces (referred to as, non-local provinces). By multiple time dimensions analysis, we find that at the weekly (monthly) level, the impact of the pandemic in local province on stock returns is larger (weaker) than the pandemics in non-local provinces, showing the temporal (spillover) effects. Mechanism analysis shows that COVID-19 can quickly reduce investors’ attention to stock market. The heterogeneity analysis shows that firms owned by state, with bad CSR, or a higher proportion of shares held by the largest shareholder are more affected by COVID-19. After replacing samples and time intervals, the results remain robust.

Quantum theory provides a comprehensive framework for quantifying uncertainty, often applied in quantum finance to explore the stochastic nature of asset returns. This perspective likens returns to microscopic particle motion, governed by quantum probabilities akin to physical laws. However, such approaches presuppose specific microscopic quantum effects in return changes, a premise criticized for lack of guarantee. This paper diverges by asserting that quantum probability is a mathematical extension of classical probability to complex numbers. It isn’t exclusively tied to microscopic quantum phenomena, bypassing the need for quantum effects in returns.By directly linking quantum probability’s mathematical structure to traders’ decisions and market behaviors, it avoids assuming quantum effects for returns and invoking the wave function. The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns.Utilizing Fourier decomposition, we derive a Schr¨odinger-like trading equation, where each term explicitly corresponds to implications of market trading. The equation indicates discrete energy levels in financial trading, with returns following a normal distribution at the lowest level. As the market transitions to higher trading levels, a phase shift occurs in the return distribution, leading to multimodality and fat tails. Empirical research on the Chinese stock market supports the existence of energy levels and multimodal distributions derived from this quantum probability asset returns model.

This paper provides new evidence on the predictive power of retail trading for future stock returns using tick data from the Chinese stock market. We explore sources of the predictive power from the novel perspective that sophisticated retail investors may exploit predictive information by observing limit order book and inferring institutional trading intentions. Employing a two-stage decomposition approach, we decompose the retail order imbalance into four components and find that the component related to retail investors’ perception of institutional trading intentions significantly contributes to the predictive power of the retail order imbalance for future returns, accounting for more than 15%.