This paper studies how dividing candidates in a tournament into separate tracks with differentiated performance criteria affects incentives and aggregate outcomes. We exploit China’s Major Function Oriented Zoning plan, which assigns counties to devel-opment or conservation tracks, with the latter de-emphasizing growth indicators. Using a staggered Difference-in-Differences design, we find that prefectures introducing a con-servation track achieved higher aggregate economic performance despite relaxing growth-based evaluation for part of their subordinate counties. To explain this counterintuitive ef-fect, we develop a stylized Tullock contest model that highlights two institutional features: promotion opportunities remain open to officials in both tracks, and counties within the same prefecture continue to interact across tracks. The model further predicts an inverted U-shaped relationship between the size of the conservation track and overall performance, which is supported by empirical evidence.

Purpose – Estimates disclosure of human capital management for Chinese listed companies. Investigate the patterns ofthe disclosure of human capital management acrossindustries and regions. Examine the determinants of human capital management disclosure in China. Examine the association between human capital management disclosure and firm performance. Design/methodology/approach – We employ natural language processing techniques on annual reports’ management discussion and analysissections.We construct exposuremeasuresforten human capitalmanagement dimensions and synthesize them into one comprehensive measure of human capital management disclosure. We conduct empirical analysis on the measure using a sample of Chinese listed companies during 2009–2022. Findings – We construct a measure of human capital management disclosure for 5,153 Chinese companies during 2009–2022. We find that firms with high HCM disclosure are more labor intensive and have more cash holdings and R&D expenditure but have lower sales growth, market-to-book ratio and leverage. HCM disclosure is associated with better future accounting performance but poor future market valuation. There are substantial variations in HCM disclosure across industries, geographic regions and ownership types. HCM disclosure has increased significantly during the COVID-19 pandemic. Social implications – The increased HCM disclosure and its association with firm operating performance and market valuation indicate the relevance of HCM in corporate management and underscore the need for more robust and standardized disclosure of HCM in China. Our findingssupport recent regulatory efforts by CSRC to enhance the transparency and accountability in HCM disclosures and advocate for more explicit and specific HCM disclosure requirements in the future. Originality/value – We propose a quantitative measure of human capital management disclosure, which can be modified to apply to other markets. We construct a comprehensive, ready-to-use dataset for HCM disclosure for Chinese listed companies and conduct descriptive analysis on the dataset. We identify the patterns of human capital management disclosure and its determinants in China.

This study examines the mean-reversion behavior of trading volume in China’s A-share market, with a focus on the speed at which abnormal surges dissipate. We compare two competing hypotheses: the stealth-trading hypothesis, where persistent volume reflects order-splitting by informed traders, and the informational-efficiency hypothesis, which interprets faster reversion as a sign of efficient information absorption. Using the Ornstein–Uhlenbeck (OU) model, we estimate the reversion speed for over 3,000 stocks and link it to firm- and industry-level characteristics. We find that trading volume is strongly mean-reverting, with over 98% of stocks classified as stationary. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger size, higher analyst coverage, lower volatility, and greater liquidity. Notably, reversion speed increased after the 2006 IFRS reform but declined following Stock Connect, suggesting that stock market policies can influence informational efficiency. Our OU-based methodology offers a simple, observable proxy for monitoring how quickly markets process information. These results position trading volume as a core variable in market microstructure research and policy evaluation.

As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

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 documents that the physical appearance of CEOs, specifically excess body weight, is priced in the capital market. In the absence of explicit health disclosures,market participants interpret obesity as a proxy for latent health risks and potential managerial disrupts, thereby demanding a compensation premium. Our analysis reveals that (1) IPOs of firms with obese CEOs have lower first-day performance, (2) these firms achieve a lower valuation, (3) the stocks of these firms have lower liquidity and (4) they provide higher stock returns thereafter. A quasi-natural experiment based on the invention of anti-obesity medications provides supporting causal evidence.

Mutual funds create liquidity for investors by issuing demandable equity shares while holding illiquid securities. We study the implications of this liquidity creation by examining frequent trading suspensions in China, which temporarily eliminate market liquidity in affected stocks. These suspensions cause significant mispricing of mutual funds due to inaccurate valuations of their illiquid holdings. We find that investors actively acquire information about suspended stocks held by mutual funds, driving flows into underpriced funds. This information is subsequently incorporated into stock prices when trading resumes. Our findings suggest that mutual fund liquidity creation stimulates information acquisition about illiquid, information-sensitive assets.

Traditional decision tree models, which rely exclusively on numerical variables, often encounter difficulties in handling high-dimensional data and fail to effectively incorporate textual information. To address these limitations, we propose the Integrated Multivariate Segmentation Tree (IMST), a comprehensive framework designed to enhance credit evaluation for small and medium-sized enterprises (SMEs) by integrating financial data with textual sources. The methodology comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization; (2) selecting salient financial features using Lasso regression; and (3) constructing a multivariate segmentation tree based on the Gini index or Entropy, with weakest-link pruning applied to regulate model complexity. Experimental results derived from a dataset of 1,428 Chinese SMEs demonstrate that IMST achieves an accuracy of 88.9%, surpassing baseline decision trees (87.4%) as well as conventional models such as logistic regression and support vector machines (SVM). Furthermore, the proposed model exhibits superior interpretability and computational efficiency, featuring a more streamlined architecture and enhanced risk detection capabilities.

Assessment in vocational education institutions is frequently organized around performance metrics—graduation rates, employment outcomes, and satisfaction scores—gathered too tardily to avert institutional dysfunction. In increasingly unstable policy situations, these models have become precarious: they quantify collapse more frequently than they avert it. This paper presents fuse logic as an innovative mechanism for risk-responsive governance in technical and vocational education and training (TVET). Utilizing systems control theory and the analogy of circuit breakers, fuse logic is a threshold-sensitive, dynamically activated assessment paradigm designed to disconnect institutional activities prior to complete failure. The research formulates a four-stage model—situational sensing, threshold definition, fuse activation, and adaptive reconfiguration—and implements it in a simulated scenario reflecting Chinese TVET trends. When critical metrics surpass risk thresholds (e.g., dropout rate, employment mismatch), fuse logic triggers systematic program shutdowns, stakeholder consultations, and conditional reintegration procedures.This study's contribution is in redefining evaluation from measurement to protection. It advocates a governance framework that permits temporary disconnection to maintain system integrity. Fuse logic enhances conventional quality assurance frameworks by providing an integrated, failure-tolerant layer of organizational resilience. The report concludes with a discussion on transferability, ethical considerations, and prospective avenues for implementation across varied educational systems.

In this paper, we investigate alternative one-factor and two-factor continuous-time models with both affine and non-affine variance dynamics for the Chinese options market. Through extensive empirical analysis of the option panel fit and diagnostics, we find that it is necessary to include both the non-affine feature and the multi-factor structure. For performance evaluation, we examine various measures from both aggregate and dynamic perspectives. Our results are statistically significant.