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.

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 paper investigates how the Global Supply Chain Pressure Index (GSCPI) affects long-term stock–bond correlations in China, employing mixed-frequency data from April 2005 to June 2025 in a DCC-MIDAS-X framework. Results show that higher GSCPI significantly reduces long-term stock–bond correlations, thereby enhancing the hedging property of bonds. This effect is both state-dependent and asymmetric, remaining significant in low-volatility regimes and following negative shocks, while becoming largely muted during high-volatility periods or after positive shocks. However, the impact of GSCPI weakens substantially after China’s 2014 financial liberalization, as global financial factors increasingly drive cross-asset dynamics. Moreover, GSCPI provides incremental information that enhances portfolio diversification and hedging performance.

We use nearly a decade’s worth of panel data from European Union Chamber of Commerce in China business confidence surveys to analyse the deteriorating outlooks of EU firms in China from 2017 to 2025. All firms in China currently face challenges including slow profit growth and deflation. These circumstances have contributed to a rare drop of foreign direct investment into China over the last two years. However, certain challenges are particularly acute for foreign firms, including those from the EU. According to survey results, business sentiment among EU firms operating in China has never been bleaker. Respondents view their profitability, growth opportunities and competitiveness negatively, while fewer respondents than ever plan to expand their Chinese operations. Moreover, significant shares of respondents report recent increases in political pressure from the Chinese state and media, while nearly a third of respondents say they are siloing their Chinese operations, meaning separating them from other global activities. Disaggregated by size, sector, and years of operation in China, insightful differences emerge between the business strategies of EU firms. We broadly classify these into four categories: doubling-down, hedging, hibernating and ready to exit. EU policymakers should consider how to address the challenges EU firms in China face, such as asset-heavy sectors being ‘stuck’ in China and smaller firms lacking the capacity to operate at a loss in China’s market. The EU might need to facilitate transitions for these companies, helping them to reduce exposure to China and diversify into other emerging markets.

This study documents a novel, culturally embedded calendar anomaly in the Shanghai Composite Index (SSE Composite) derived from the Nayin (纳音) Five Elements system—a traditional Chinese sexagenary calendrical framework. Utilizing daily data from 1990 to 2025, the analysis reveals a significant correlation between elemental two-year periods and market performance. Key findings include: Earth-Element Dominance: Earth periods exhibit a 100% positive return rate (4/4) with a mean return of +123.4%. The effect size is substantial (Cohen’s d=1.50) compared to non-Earth periods. Metal-Element Declines: Metal periods universally display a structural peak-and-decline morphology, with an average −30.4% late-cycle decline. Water-Element Momentum: Water periods systematically mirror the directional momentum of their predecessors with 100% accuracy (3/3). These patterns fail to replicate in the S&P 500, suggesting a unique cultural-behavioral channel where traditional metaphysical cycles modulate investor sentiment in the Chinese market. This research provides the first empirical validation of Nayin-based cyclicality in financial asset pricing, offering a predictive framework for institutional and individual investors focused on the China-specific market. Keywords: Calendar anomaly, Chinese traditional calendar, Nayin Five Elements, Shanghai Composite Index, Cultural behavioral finance, Sexagenary Cycle, Market Sentiment Declaration of Interest The author declares no conflict of interest. To ensure the objectivity of this research, the author further declares that he holds no active personal trading positions in the securities discussed. The author's personal trading account has been inactive with zero transactions over the past five years.

We posit and demonstrate that, in China’s retail-dominated market, quantitative trading over-relies on non-fundamental signals, thereby crowding out fundamental information from stock prices and increasing crash risk. Using trading data from quantitative mutual funds and Chinese A-share firms during 2009-2023, we find that greater exposure to quantitative trading is associated with higher future crash risk. Mediation analysis further reveals that reduced information efficiency constitutes a key channel through which quantitative trading elevates crash risk. The effect is stronger for stocks with more retail investors, consistent with our proposed mechanism. Overall, we identify a novel potential risk of quantitative trading in underdeveloped emerging markets.

This paper investigates whether firms’ overtime intensity affects the cost of debt financing. Using satellite-based night-time light data for Chinese listed firms between 2013 and 2022, we construct an objective measure of weekday overtime that captures firms’ operational effort and capacity utilization. We find that higher overtime intensity is associated with significantly lower bond offering yield spreads. The effect is stronger among smaller, less-followed, less-profitable, and non-AAA-rated issuers, consistent with an information-asymmetry channel where investors rely more on observable operational behavior when hard information is weaker. The findings suggest that overtime functions as a priced form of soft information in debt markets, offering new evidence that real-time operational signals influence credit risk assessment.

This study investigates how venture capital (VC) reputation affects initial public offering (IPO) exits in the Chinese VC market using a two-sided matching mechanism. Research that distinguishes the sorting and influence effects of VCs in the Chinese market is lacking. To address this gap, Chinese VC transaction data, comprising 3,606 VC firms and 8,173 investment transactions, was used to construct a structural econometric model. The Markov Chain Monte Carlo Bayesian estimation techniques were employed to identify the sorting and influence effects of VC reputation. We demonstrate that the likelihood of IPO exits is considerably increased by VC reputation, whereas historical investment experience has a dampening effect on exit outcomes. The IPO success rates are significantly higher for firms in the biotechnology, electronics, medical, and late-stage industries. The difficulty of IPO exits increases with investment age. Compared to influence effects, sorting effects were the dominant mechanism. VCs with a high reputation systematically selected firms with potential advantages, such as high-quality management teams, to promote IPO success. This study’s novelty lies in its application of an endogenous two-sided matching solution to the Chinese VC market. Using a structural model, we discovered the importance of the reputation sorting effect in the Chinese VC market and refined the VC’s investment preferences in high-tech industries. This study’s practical significance lies in the findings that enterprises must pay attention to the sorting capabilities of VC institutions, the government can guide capital flows to efficient exit industries, and VC institutions should optimize the resource allocation structure.