This paper explores the impact of the Integrated Government Service Platform (IGSP) reform, a large-scale digital regulatory initiative in China, on firms’ investment efficiency. Using a firm-level panel dataset of Chinese listed firms, we find that the IGSP reform significantly enhances firms’ investment efficiency. Mechanism analyses indicate that the IGSP enhances firms’ investment efficiency by improving governance service quality and convergence of governance services across cities within a province, thereby reducing economic policy uncertainty and reinforcing market expectations. The heterogeneity analyses reveal that the effect is more pronounced for firms without political connections, those operating in high business volatility and high-tech industries, and regions with stronger informal institutions. Finally, the IGSP encourages firms to expand their investment activities to other cities within the same province. This finding is fully consistent with the underlying mechanisms identified in this study. Our findings provide novel evidence on the economic consequences of digital regulatory integration and offer policy implications for advancing service-oriented and efficiency-driven governance in developing economies.

We investigate how investors assess transition risks arising from the discretionary regulatory mandates for nature conservation. We focus on the UN Biodiversity Conference in Kunming, China, a policy shock signaling heightened political commitment to protection. During the conference, firms exhibited negative abnormal stock returns on average. Using a machine learning approach, we identify historical nature-related subsidies to capture firm-level variations in expectations of mandated conservation engagement, beyond measuring corporate environmental (ecological) impacts. This metric negatively correlates with returns during the conference, particularly for firms with high operational volatility, and those in regions with limited fiscal flexibility and stringent conservation pressures. Investor concerns primarily stem from policy uncertainty, without positive earning expectations from conservation. Our findings underscore the role of authoritative discretion in shaping nature transition risks.

Traditional portfolio optimization typically relies on a single fitted return distribution, thus ignores non-normality and uncertainty across distributional states. Mean–variance–ambiguity (MVA) models, motivated by the smooth-ambiguity preferences of Klibanoff, Marinacci, and Mukerji (2005) and the KMM-to-MVA Arrow–Pratt reduction of Maccheroni, Marinacci, and Ruffino (2013), extend conventional risk penalties to account for distributional uncertainty, but they missed how state-conditional return distributions affect portfolio optimization stability. We develop Deep Mean–Variance–Ambiguity (Deep MVA) to resolve this gap by learning state-conditional return distributions and embedding the resulting conditional information loss into the ambiguity penalty. The resulting local certainty equivalent combines reduced-law mean–variance risk with a quadratic penalty for second-order disagreement. Conditional-mixture state resolution yields a Deep ambiguity covariance equal to the Traditional ambiguity covariance plus a positive-semidefinite state-feature increment. This increment is the conditional L2 loss from averaging state-specific distributions before optimization. On compact convex feasible sets, we show that the state-resolving program has stronger ambiguity-relevant curvature, lower mean-input sensitivity, and a unique global solution under restricted positive curvature or a common ridge; its projected iterations converge linearly. An LSTM-MDN learns state-conditional distribution densities from 60-minute bars, and Probability and VoV scores enter weekly MVA, with GARCH_Resid as a comparator. Tests on the SSE 50, CSI 300, and CSI 500 universes from 2017–2025 find statistically significant Sharpe out-of-sample improvements over Traditional MVA and MVO across all three universes and ambiguity specifications.

Against the backdrop of systemic financial risk prevention and the pursuit of a virtuous cycle in the real economy, whether Local Government Financing Vehicles (LGFVs) shareholding alleviates maturity mismatch of corporate investment and financing activities is a question of critical importance, not only for the development of individual firms but also for high-quality macroeconomic growth. Using a sample of A-share listed firms in China’s Shanghai and Shenzhen stock exchanges from 2009 to 2021, this paper theoretically analyzes and empirically tests the impact of LGFVs shareholding on corporate maturity mismatch. The empirical results indicate that LGFVs shareholding can significantly alleviate corporate maturity mismatch and the finding is still robust across a series of empirical checks. Mechanism analyses show that LGFVs shareholding improves firms’ long-term financing capacity through a resource provision channel and restrains overinvestment through a governance optimization channel, thereby collectively mitigating corporate maturity mismatch. Further analysis indicates that the mitigating effect of LGFVs shareholding is more pronounced in firms with concentrated LGFVs shareholding, firms controlled by non-state-owned capital and firms under higher economic policy uncertainty. Our findings provide valuable theoretical insights for policy design aimed at optimizing corporate financial behavior and curbing corporate financial risks.

This study examines the impact of Economic Policy Uncertainty (EPU) on the stock price crash risk of Chinese commercial banks. In addition, it explores how Governance and Digital Transparency curtail the effect of EPU on stock price crash risk. Using a sample of 50 Chinese A-share-listed banks from 2012 to 2024, the study reveals that EPU significantly increased the banks’ stock price crash risk. The findings are robust to alternative measures of EPU and stock price crash risk. Further, governance and FinTech adoption mitigate the positive effect. The mitigating effect persists across high- and low-risk bank subsamples. In addition, we perform a battery of analyses to support our main findings. These findings have important theoretical and practical implications.

The parking reservation systems (PRS), as an intelligent system, was adopted to address urban parking difficulties. However, the parking reservation system has not been widely adopted in China due to the reasons such as the imperfect system and the uncontrollable parking behaviour of users. This study examines the impact of users' non-compliant behaviour on intentions to reuse PRS. Non-compliant behaviours include not arriving or leaving parking spaces not as scheduled, negatively affecting subsequent users. The Technology Acceptance Model (TAM) was expanded by adding perceived risk, social influence, and behavioral attitude. A survey involving 702 PRS users from multiple Chinese cities was conducted. Structural Equation Modelling (SEM) was used for analysis Results show that perceived risk—such as occupied reserved spaces and extra fees from time deviations—significantly reduces behavioral attitudes and reuse intentions. Conversely, perceived usefulness, ease of use, and social influence positively influence both attitudes and reuse intentions. Importantly, the findings highlight that behavioral uncertainty, particularly users’ deviations from scheduled parking times, is a critical source of perceived risk that undermines trust and long-term engagement. Effectively managing this uncertainty through improved system flexibility and reliability is therefore essential to promoting the sustained adoption of PRS.

Why do fertility subsidies consistently fail in China? Why do consumptionand fertility collapse globally despite intact household book wealth? This paper proposes the Family Long Cycle Hypothesis (FLCH), extending thedecision-making unit of the life cycle hypothesis from an individual to an intergenerational family network, and expanding the budget constraint from a singlelifetime resource constraint to a dual constraint of “total resources + liquiditystructure”. The core mechanism is: intergenerational “blood-transfusion” homepurchase locks in network liquidity without changing household net assets, completing a balance sheet morph of “book wealth unharmed, decision-making paralyzed” at the moment of purchase. This liquidity depletion spikes effective riskaversion, forming a multiplier effect with income uncertainty, causing fertility—the irreversible commitment with the longest duration—to enter the corner solution region first. Within this region, the elasticity of fertility decisions to costsubsidies is strictly zero, but they remain highly sensitive to liquidity repairand uncertainty reduction. Consequently, this paper proves the fundamentalmechanism difference between consumption subsidies (cash rewards, childcarefee waivers) and capital transfers (mortgage principal write-down, unconditionalcash transfers), and proposes three effective policy directions: reducing incomeuncertainty, repairing family liquidity, and raising the reference income of thebottom 90% of the population. The model nests the standard life cycle hypothesis as a special case and is distinguishable from the competitive saving hypothesis on six pairs of opposing predictions. The theory also explains theasymmetric “fast-falling, slow-rising” adjustment of housing prices: the drop isdriven by defensive behavior (business cycle scale), while the recovery is constrained by intergenerational liquidity reconstruction (intergenerational scale of10–20 years).

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.

The essay delves into the impact of environmental uncertainty on asymmetric mispricing utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact, and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improve he efficiency of emerging capital markets.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.