We develop a theoretical model in which investors evaluate dual-listed stocks through the prospect theory value of their historical return distributions. When the prospect theory value (PTV) of A shares exceeds that of H-shares, the A-share price will be higher than the H-share price, generating a persistent equilibrium price premium despite identical fundamentals. Using Chinese A–H dual-listed firms, we find that the PTV difference positively explains the AH premium. A gain–loss decomposition shows that A-shares have higher PTV because their less negative loss contribution more than offsets their smaller gain contribution. Consistent with this result, loss aversion and value-function curvature exhibit stronger pricing relationships than probability weighting. Investor composition shapes the pricing influence of PTV. Our results also explain why the AH premium increased following deeper market integration, like the Shanghai–Hong Kong Stock Connect.

Firm characteristics help estimate conditional factor loadings. Larger libraries contain new and overlapping information. We use the cross-sectional distribution of firm characteristics as a ruler to evaluate IPCA loading spaces. With factor counts fixed, expanding from 36 to 153 characteristics can improve in-sample fit while representing momentum and size positions less fully. Reallocating shared exposures changes attribution without changing joint fitted returns. With coefficients fixed, exposure references alter holdings, trading, and subsequent risk at the same characteristic target. Statistical factor models should be evaluated not only by return fit, but also by the exposures they represent and the investment decisions they support.

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.

On October 10, 2025, Bitcoin fell 9.1% in thirty minutes on Binance following a geopolitical shock, contributing to a 16.0% peak-to-trough drawdown over 33 hours. Using minute-level Binance and Bybit data across spot, perpetual futures, and mark price feeds for BTC, ETH, and SOL, this paper situates the event within an empirical distribution of 62 BTC drawdowns exceeding 3% over 30 minutes during 2024–2026. October 10 ranks first of 62 on every microstructure metric simultaneously: a SOL futures-spot drawdown gap of 11.0 percentage points (z = +7.65), a BTC basis swing of $1,367 (z = +6.18), and a mark to-spot undershoot of $2,507 (z = −4.21). It also ranks first on multivariate anomaly measures (Mahalanobis distance 7.70; sum of absolute z-scores 45.9, 2.4 times the runner-up). A parsimonious conceptual cascade model identifies the mark price oracle as the crypto-specific amplifier.

This paper develops a delegation-based framework to explain how institutional design shapes pricing incentives under risk. Using China’s 2016 IPO reform—which abolished prefunding requirements and transferred payment obligations from investors to underwriters—as a natural experiment, we show that introducing subscription-payment risk (SPR) renders underwriter’s partial residual claimants with respect to unpaid allocations. Building on Baron’s (1982) delegation model, we argue that the reform amplifies information asymmetry and induces underwriters to adopt more conservative pricing strategies to manage perceived payment risk. Empirically, IPOs exposed to SPR exhibit greater underpricing and lower offer prices, particularly when investor bids reflect stronger valuation pessimism. The effect tends to be less pronounced for reputable underwriters and when foreign institutional investors participate. Overall, the evidence demonstrates how risk redistribution and institutional frictions jointly shape underwriter behavior and pricing efficiency in primary equity markets.

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.

How do market-design reforms affect secondary-market liquidity in an emerging equity market? We examine China’s ChiNext reform, which combined registration-based IPO issuance with changes in secondary-market trading rules. Using firm-month observations and high-frequency order-book data, we compare incumbent ChiNext firms with Main Board firms in a difference-in-differences framework. The results show that the reform significantly improves secondary-market liquidity, with consistent evidence across alternative DID estimators and liquidity measures. Dynamic estimates show comparable pre-reform movements and increasingly favorable liquidity outcomes after implementation. Cross-sectional analysis indicates stronger liquidity improvements among larger and more profitable firms. Microstructure results show narrower quoted spreads, broadly stable displayed depth, and a shift in order flow toward buyer-initiated trading. Overall, the findings demonstrate that the integrated ChiNext reform package improved secondary-market liquidity and reshaped the cross-sectional distribution of liquidity gains, highlighting the market-quality consequences of institutional reform in emerging equity markets.

High-quality development of water conservancy (HDWC) is of critical value for safeguarding the stability of production systems, livelihoods, and ecosystems. As the digital revolution intersects with China’s “dual carbon” targets, development of water conservancy must transition from traditional engineering approaches to data-driven ecological models. Drawing on institutional logic theory and employing dynamic qualitative comparative analysis across 30 Chinese provinces, this study examines how digital ecology facilitates the HDWC. The findings reveal that none of digital government, digital infrastructure, digital economy, digital capability, or digital society constitutes a necessary condition for the HDWC. Instead, it is the result of the combined effects of multiple institutional logics. Five configurations leading to HDWC are identified and categorized into four types: government-market-driven model, government-market-society-driven model, market-society-driven model, and government-society-driven model. The consistency of the configurations significantly increased during the study period. Furthermore, their distribution showed substantial regional differences. There are two configurations that inhibit the HDWC, namely the government-market-absence type and the market-society-absence type. Digital society emerges as a critical factor. This research uncovers multiple pathways through which digital ecology can empower HDWC, providing valuable insights for optimizing regional digital environments.

Capital market internationalization is widely regarded as an important pathway to improving resource allocation efficiency and enhancing governance quality, while simultaneously imposing higher requirements on corporate sustainability and social responsibility. However, its impact on firms’ internal income distribution remains subject to academic debate. This study treats the inclusion of China’s A-shares in the MSCI Emerging Markets Index as a landmark exogenous shock of capital market internationalization. Based on a sample of Chinese A-share listed firms from 2014 to 2022, we manually compile firm-level MSCI inclusion data and construct a multi-period difference-in-differences model for empirical testing. The results indicate that firms’ inclusion in the MSCI Index significantly increases their labor income share. The mechanism analysis reveals that this promoting effect operates mainly through three channels: alleviating financing constraints, fostering innovative development, and strengthening investor governance. Further heterogeneity analysis shows that the effect is more pronounced among technology-intensive firms, firms under greater competitive pressure, and firms with higher degrees of internationalization. By incorporating income distribution outcomes into the framework of capital market internationalization, this paper enriches the evidence on the economic and social effects of internationalization and provides policy implications for emerging economies to advance high-level capital market opening and optimize income distribution.

In this paper, we design two chapters to discuss trade dynamics with heterogeneous fluctuations, contributing new insights to macroeconomic issues related to international trade. In the first chapter, we model general exchange rate fluctuations through stochastic processes and analyze the impact of heterogeneous price shocks on export competitiveness. We find that monetary policy and innovation both show positive effects on export trade, while monetary policy stabilizes exchange rate fluctuations to comprehensively boost provincial export competitiveness, innovation reduces its reliance on exchange rate mechanisms. The optimal policy according to exchange rate fluctuations aims to solve the wealth distribution of exporters, and it suggests that optimal policy should promote dynamic transitions in trade patterns rather than maintain existing comparative advantages in heterogeneous trade structures. In the second chapter, we model labor market fluctuations and the ability to utilize production factors through stochastic processes, and we analyze the impact of heterogeneous aggregate production shocks on general international trade. We find that labor market fluctuations only benefit international trade under the cooperation policy. Moreover, for both sanction and cooperation policy scenarios, positive shocks (i.e., shocks where average wage growth in the labor market exceeds unemployment) strengthen their impact on import trade while weakening their impact on export trade, and vice versa. Regarding the theories proposed in these two chapters, we prove them through empirical analyses using the provincial data of China.