We study the role of superstition in China’s peer-to-peer lending market by ex-amining whether lenders time their bids according to “lucky hours” from the Chinese farmer’s calendar. Loans funded during lucky hours perform better—but only because the platform lists higher-rated loans at those times. This pattern is consistent with a screening mechanism: highly risk-averse lenders place greater value on both true risk reductions and auspicious-day signals, so the platform maximizes surplus by bundling the two—listing low-risk loans on auspicious days. Moreover, listing safer loans at lucky hours can further boost proffts because biased beliefs decay more slowly under asymmetric (bad-news-heavy) learning.

This paper examines how artificial intelligence (AI) adoption influences the total factor productivity (TFP) of Chinese A-share manufacturing firms from 2010 to 2023. Results show that AI significantly raises TFP, robust across multiple specifications and instrumental variable tests. AI also boosts operational efficiency by accelerating accounts receivable and inventory turnover, revealing a “technology–operation–productivity” pathway. The positive effect is stronger in regions with better digital infrastructure and in firms with stronger governance. The findings provide fresh evidence on AI’s productivity effects and offer policy implications for intelligent transformation and high-quality manufacturing development.

In the context of digital transformation, artificial intelligence (AI) has emerged as a pivotal driver for enhancing tourism resource allocation efficiency and promoting the high-quality development of the tourism industry. Grounded in the Technology–Organization–Environment (TOE) framework, this study constructs a multidimensional indicator system by integrating heterogeneous data sources, including Baidu search indices, corporate annual reports, and policy documents. Using a balanced panel dataset covering 31 provincial-level regions in China from 2015 to 2023, we empirically examine the mechanisms through which AI penetration affects the efficiency of tourism resource allocation. The super-efficiency SBM-DEA model is employed to measure allocation efficiency, while the spatial Durbin model (SDM) and geographically weighted regression (GWR) are used to identify spatial spillover effects and regional heterogeneity. Furthermore, tourist satisfaction is quantified using a natural language processing (NLP)-based sentiment index derived from online reviews. The results indicate that AI penetration significantly improves tourism resource allocation efficiency, with stronger effects observed in regions with advanced technological infrastructure. Smart tourism pilot policies demonstrate significant spatial spillover effects, positively influencing scenic areas within a 100-kilometer radius. However, diminishing marginal returns are evident, highlighting capacity absorption thresholds and institutional constraints. Based on the empirical findings, the study proposes targeted policy recommendations, including the establishment of provincial tourism data hubs, promotion of AI toolkit systems, enhancement of scenic area evaluation mechanisms, and reinforcement of collaborative governance between government and enterprises. These insights aim to provide both theoretical and practical guidance for the intelligent transformation and coordinated regional development of China’s tourism industry.

This paper examines how AI investment and data assets affect corporatecredit risk. Using Chinese listed firms, we construct four complementary measures ofAI investment, asset-based, labor-based, LLM-based, and text-based, and link them tofirms’ distance-to-default. We find that benchmark-level AI investment reduces defaultrisk, while excessive ffrm-speciffc investment increases it by eroding profitability andreffecting risk-taking and competitive pressure. The dominance of this adverse effectyields a negative overall relation between AI investment and credit risk. Cash flow riskis the transmission channel: benchmark-level AI improves cash ffow quality, whereasexcessive investment worsens it. High-quality data assets complement benchmark-levelAI by stabilizing cash ffow, but this benefit fades once investment becomes excessive.Overall, the impact of AI on credit risk depends on both investment intensity and dataquality, operating primarily through cash flow dynamics.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

The northwest region of China faces many practical challenges, and its digital economy lags behind other areas of China. Digital transformation is a new source of competitive advantage in the digital economy era, which can help northwest enterprises rebuild their competitive advantage in the digital age, accelerate the development of the digital economy in the northwest region, bridge the digital gap between the East and the West, and promote the high-quality development of the national digital economy. In this study, the PLS-SEM method is used to collect data from 172 enterprises across five provinces in northwest China, to deeply analyze the mechanism and path through which digital transformation reshapes enterprise competitive advantage, identify the key sticking point hindering digital transformation in northwest China, and then propose more targeted strategic suggestions. It is found that the resource base of enterprises in northwest China is generally weak, making it difficult to deliver direct competitive advantage; existing enterprise resources can provide basic conditions for digital transformation and resource-orchestration capability; although digital transformation cannot directly create competitive performance, it can indirectly deliver competitive advantage by positively affecting resource-orchestration capability; resource-orchestration capability directly and significantly affects enterprise competitive performance and is the core competency for enterprises to build digital resilience.

This study examines the evolution and spillover effects of informational efficiency across carbon markets following the launch of China ’s national carbon emissions trading system (NCET). Using a time-varying parameter VAR model, we analyze efficiency transmission among the National Carbon Emission Allowance (CEA), six China’s pilot markets, and the European Union Allowances (EUA). The results reveal substantial heterogeneity in efficiency dynamics. Since early 2023, the CEA and Shenzhen have shown improved efficiency and stability, while the EUA and other pilot markets have experienced declines in efficiency and increased volatility. Despite progress in domestic markets’ efficiency, the EUA remains the primary source of efficiency spillover effects, followed by the CEA, Shenzhen, and Beijing, whereas other pilot markets—particularly Shanghai—act mainly as net recipients. Spillover intensity increases significantly during major regulatory periods, especially around China’s annual “Two Sessions,” highlighting the influence of policy signals on market linkages. These findings offer empirical insights into the time-varying transmission of efficiency under institutional reform and inform the coordinated design of carbon trading policies.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

The value-relevance of accounting information is a cornerstone of capital market research, typically measured indirectly through coefficients and R2 values from returns-earnings models, which have limitations in explaining how accounting information influences stock prices. Based on the theory of financial analyst and the generating process of accounting information, we propose a direct measurement approach using analyst consensus earnings forecasts to capture the effect of accounting information on decision-making. We also construct firm-level measures of predictive and confirmatory value, two qualitative characteristics of accounting information defined by the Financial Accounting Standards Board. Using data from the Chinese stock market, where analysts play a crucial role, we find that our measures significantly explain the relationship between accounting information and stock prices, as well as stock price synchronicity. Our study offers a novel and verifiable method to quantify the abstract concept of value-relevance of accounting information, enhancing the understanding of its effect on decision-making and stock prices.

This study uses Shanghai Crude Oil Futures (SC) as a proxy for the upstream segment of China’s petrochemical industry and investigates how its market efficiency influences five key downstream product markets. Considering that markets differ in how they absorb information and in their structural features, we employ the Feasible Exact Local Whittle (FELW) estimator to construct a continuous market efficiency index. To capture efficiency dynamics across different time horizons, the study applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to decompose the efficiency series into short-, medium-, and long-term components. These are then examined by Quantile-on-Quantile (QQ) regression to trace the varying marginal effects across different efficiency states. The results reveal strong state dependence and structural differences in the efficiency transmission from SC to downstream markets. Among the five markets, Low-Sulfur Fuel Oil and Asphalt exhibit the most stable transmission patterns, with the former showing a “saddle-shaped” structure and the latter following a “dual-path” pattern. In contrast, the links between SC and the markets for Linear Low-Density Polyethylene and Polypropylene are highly nonlinear and less predictable. Purified Terephthalic Acid demonstrates a dual mechanism of efficiency resonance and long-term anchoring. These findings deepen our understanding of information efficiency within industrial value chains. They also offer practical insights for managing market risk, guiding price policies, and designing regulatory frameworks in the energy sector.