With the development of China's economy, more and more Chinese enterprises are active on the world stage, and cross-border M&A is the most effective and fastest way for enterprises to go abroad and make overseas investments, and it is also an important path for globalization after the enterprises have reached a certain stage of growth. Compared to domestic M&A, cross-border M&A is a more complex economic activity, requiring more factors to be considered and greater risks to be taken, with the slightest misstep often leading to operational difficulties for the acquiring company. It is important to consider whether cross-border M&A can improve business performance, the factors that influence the performance of cross-border M&A, and how to improve the performance of enterprises in cross-border M&A. This study takes 100 cross-border M&A events of Chinese listed companies in Shanghai and Shenzhen during the period of 2017-2020 as a sample, and on the basis of reviewing the research results of cross-border M&A at home and abroad, combined with the characteristics of cross-border M&A of Chinese enterprises, from different perspectives, a number of financial indicators are selected to construct comprehensive performance evaluation indicators using factor analysis, and the preliminary analysis shows that after cross-border M&A, the companies with increased performance The preliminary analysis showed that the number of companies whose performance increased after cross-border M&A increased year by year. The impact of industry relevance and transaction equity on M&A performance is not significant; the ratio ofM&A amount to current assets negatively affects firm performance in the year of M&A. Finally, based on the empirical results, relevant policy recommendations are made to encourage better development of private enterprises and improving cross-border M&A performance.

The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

Whether risk aversion is time varying and countercyclical is central to modern asset pricing, yet evidence remains limited and is based mainly on experimental, survey, or aggregate stock market data. We provide individual-level evidence from 10 million Chinese auto insurance contracts from 2011 to 2017, estimating policyholders’ risk aversion from deductible choices. We find that risk aversion is time varying and countercyclical. The estimates are negatively related to lottery and stock trading, positively related to insurance sales and bond trading, and vary with psychological factors, including seasonal mood, “zodiac year,” and calendar events.

This paper revisits two widely studied predictors of commodity futures returns, basis and basis momentum, whose conventional measures using first-nearby futures as proxies for spot prices may limit their ability to capture fundamental spot-market risks. Motivated by this limitation, we construct two spot-based signals from observed spot and futures prices, which are theoretically shown to contain incremental information beyond conventional measures. Using 41 Chinese commodity futures, we find that these signals robustly predict first-nearby contract returns and remain significantly priced in time-series and cross-sectional tests, even after controlling for their conventional counterparts. We then develop a spot-enhanced three-factor model, including the market factor and the two spot-based factors, which consistently outperforms three widely used benchmark models in pricing competing factors and explaining return anomalies.

We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.

The Full Guaranteed Purchase System began to replace the subsidy system as the core policy for promoting renewable energy integration, designating grid companies as the sole entity responsible for the physical integration of renewable energy. Meanwhile, the Tradable Green Certificates system provides environmental benefits for renewable power generators through market mechanisms. Therefore, exploring the strategic choices of various entities under the dual policy interventions, and uncovering the mechanisms for realizing electricity energy value and green value under different integration models, is of great significance for advancing China’s renewable energy integration. Based on China’s actual conditions, this study integrates the features of both policies and constructs a three-party evolutionary game model involving government, power generators, and grid enterprises to simulate their interactions and identify key factors influencing strategic choices. The results show that active government regulation effectively encourages positive strategies from both generators and grid companies, and that active power integration by grid companies further promotes green power generation. A “stepwise complementary” relationship exists among reputation gains, reputation losses, and regulatory costs: higher reputation gains can offset decision-making resistance arising from increased regulatory costs. Green power generation costs and innovation costs have significant negative effects on strategic choices, while the guaranteed purchase price has a significant positive effect on generators’ active strategies. The penalty parameter plays a key positive role in grid companies’ active strategies, and the guaranteed purchase price significantly influences their active integration behavior. This paper provides recommendations for motivating all entities actively participate in the consumption process.

With the rapid development of the global shipping industry, maritime traffic continues to grow, and maritime traffic risks are becoming increasingly prominent, posing serious threats to economic development, the ecological environment, and public safety. In this context, this study develops an environmental data-driven dynamic Bayesian network (DBN) model to simulate the dynamic evolution process of maritime traffic risks from the massive data of complex shipping systems. Firstly, based on the systems theoretic accident model and processes (STAMP) accident causation analysis framework, risk influencing factors (RIFs) are identified through the analysis of maritime accident report systems. Secondly, addressing the dynamic nature of maritime risks, a novel transition probability matrix (TPM) learning mechanism integrating environmental data is proposed, constructing a DBN model capable of characterizing temporal risk performance. Finally, a case study of typical routes along the Chinese coast reveals that risk performance evolution exhibits significant spatiotemporal heterogeneity across sea areas, with the East Sea and South China Sea regions being the most prominent. Their fluctuations are highly correlated with seasonal meteorological and hydrological changes, and the distribution of accident risks is also closely associated with extreme weather events such as typhoons. Sensitivity analysis validates the model's reliability. This study provides a quantitative tool for the dynamic risk management of intelligent shipping systems and offers policy insights for intelligent maritime transportation safety regulation.

The low-altitude economy (LAE) refers to economic activities conducted within airspace below 1,000 meters. Drawing on related theories on socio-technical transitions, LAE can be understood as a future regime challenging the dominant urban mobility paradigm. As an emerging field, it has yet to be systematically examined through an empirical study, especially about local response. In this paper, we construct an Integrated Local Support Index (ILSI) based on the number of relevant local policies and the level of public interest measured by the Baidu search index. Private sector readiness is measured by the LAE Development Scale (DS) based on the registered capital of relevant enterprises locally. Focusing on the top 50 cities in China’s LAE sector, we conduct a comprehensive empirical study to explore the relationships between DS, ILSI, and other natural and socio-economic factors between 2012 and 2023. The dynamic interactions of key stakeholders (local government, foreign capital, and talents) are analysed by game theory. The findings suggest that the ILSI, education level, and foreign investment have significant positive impacts. Wind speed is identified as a negative factor for LAE development. The game theory analysis further reveals that the three positive factors tend to foster efficient and stable growth when working synergistically. This implies that enhancing local government support could trigger chain reactions that attract more investment and talents, thereby accelerating LAE development. Projecting to the future, local LAE DS in 2026 is predicted via a panel time-series model with random effects. This study provides both empirical evidence and governance strategies for decision-makers navigating the socio-technical transition of the LAE.

Firm survival, as a key indicator of regional economic resilience, has gained increasing attention in the context of global economic uncertainty and the deep adjustments in industrial structures. In the Pearl River Delta (PRD), a core region of China’s Guangdong-Hong Kong-Macau Greater Bay Area, the characteristics of firm life cycles are crucial for understanding spatial development inequalities and institutional effects in emerging economies. This study focuses on firms in the PRD, using full life-cycle data from registration, operation, to deregistration. An XGBoost regression model is employed, incorporating the SHAP explanation algorithm, to systematically analyze the main factors influencing firm survival. The results show that: (1) Firm establishment time is the primary factor influencing survival, with significant “survival threshold” and “growth leap” effects—mature firms exhibit a distinct survival advantage; (2) Among spatial structure variables, moderate industry specialization and diversity enhance firm survival rates, while excessive concentration and diversification show diminishing or negative returns, reflecting an “ecological threshold” effect; (3) External shocks have a significant suppressive impact on startups, while policy support and capital size show limited explanatory power; (4) The ownership structure, especially state-holding, has a positive moderating effect on firm survival in specific contexts, indicating that private enterprises’ flexibility and adaptability can compensate for institutional gaps. This study offers insights into the spatial heterogeneity of firm survival mechanisms, providing quantitative evidence for regional economic policy adjustments and firm resilience-building. Recommendations include promoting differentiated policies, optimizing industrial ecological spatial layouts, and piloting systems to enhance survival resilience, especially for mixed-ownership firms.

Traditional production function theory treats executive characteristics as exogenous fac-tors, failing to capture how managerial heterogeneity dynamically influences productiv-ity. This study integrates Upper Echelon Theory and Dynamic Capabilities Theory to develop an endogenous production function framework where executive attributes serve as dynamic parameters reshaping capital and labor elasticities. Using fixed effect-s panel-data models, we analyze Chinese listed companies, examining how executives’ education, experience, and compensation interact with production parameters. Re-sults show executive characteristics significantly moderate factor productivity: higher education and board independence enhance capital productivity, while compensation intensity transforms labor elasticity from negative to strongly positive values. Hetero-geneity analysis reveals effects vary across ownership types and technology intensity, with technology-intensive firms showing strongest responsiveness. This research recon-ceptualizes executives as active architects of production processes, provides a rigorous framework for endogenizing management factors, and offers evidence-based guidance for corporate governance and talent strategy optimization.