Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. (2001) to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China’s automobile insurance market, leveraging IoT devices to track telematics data, this method captures risk heterogeneity among the insured. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to capture the underlying relationship between customer risk and claim behavior. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.

Central bank digital currency (CBDC) is intended to boost financial inclusion and limit threats to bank stability posed by private cryptocurrencies. Our study examines the impact of implementing CBDC on the bank stability of two countries in Asia and the Pacific, the People’s Republic of China (PRC) and India, that initiated research on CBDC within the last ten years (2013 to 2022). We construct a bank stability index by utilizing five dimensions, namely capital adequacy, profitability, asset quality, liquidity, and efficiency, using a novel “benefit-of-the-doubt” approach. Employing panel estimation techniques, we find a significant positive impact of adopting CBDC on bank stability and a moderating role of monetary policy. We also find that the effect is greater in India, a lower-middle-income country, than in the PRC, an upper-middle-income nation. We conclude that by taking an accommodative monetary policy stance, adopting CBDC favors bank stability. We confirm our results with various robustness tests by introducing proxies for bank stability and other model specifications. Our findings underscore the potential of adopting CBDC, when carefully managed alongside appropriate monetary policy, for enhancing bank or overall financial stability.

This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.

While many studies document cross-stock predictability where returns of some stocks predict returns of other similar stocks, most evidence comes from US markets. Following Chen et al. (2019), we identify peer firms based on historical return similarity and construct a Peer Return Gap (PRG) measure, defined as the difference between a stock’s lagged return and its peers’ returns. Our empirical evidence from Chinese markets shows that past-return-linked peers strongly predict focal firm returns. A long-short portfolio sorted on PRG generates an equal-weighted monthly return of 1.26% (t = 3.81) and a Fama-French five-factor alpha of 1.10% (t = 2.86). These abnormal returns remain unexplained by several alternative factor models.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

We study tail-risk contagion in Belt and Road (BRI) stock markets by conditioning on shocks from China and global commodities. We construct time-varying contagion indices from conditional higher co-moments (CoHCM) estimated within a DCC-GARCH model with generalized hyperbolic innovations, and apply them to daily data for 32 BRI markets. The higher-moment index isolates two channels: a China-driven financial-institutional channel and a WTI-driven commodity-real-economy channel, whereas a covariance benchmark fails to recover this separation. Furthermore, the system-GMM estimates link the China-conditional channel to institutional quality and financial depth, and the WTI-conditional channel to real activity. In out-of-sample portfolio tests, the WTI-conditional signal improves risk-adjusted performance relative to equally weighted and mean-variance benchmarks, while the China-conditional signal does not. Tail-based measurement thus sharpens identification of contagion paths and yields information that is economically relevant for risk management in interconnected emerging markets.

Contrary to the US, Chinese stock markets exhibit negative overnight returns that appear to be highly affected by the extent of information asymmetry. China's "T+1" trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. An information asymmetry-driven price discount thus emerges at market open, generating negative overnight returns, which further decrease with information asymmetry. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative relationship between overnight returns and information asymmetry proxied by firm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. This framework also sheds light on China's "opening return puzzle", the phenomenon that prices rise rapidly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

政府补贴支持企业创新固然是必要且现实的举措，但其实际成效在企业间存在差异，关于“创新悖论”的讨论众说纷纭。本文将研究焦点从“评说得失”引向“何以有效”，以理论模型刻画企业配置政府补贴用于创新活动的决策过程，解析营商环境这个“外因”如何通过“内因”影响企业创新决策进而影响其创新质量的内在机制，揭示其间存在的营商环境门槛。本文采用中国A股上市公司2017—2021年的专利申请数据、财务数据以及296个地级市的营商环境数据，实证检验了营商环境门槛效应及其实现机制。研究发现，政府补贴整体上并未显著提升企业创新质量，但存在显著的营商环境门槛；政府补贴在营商环境较好的地区能有效提升企业创新质量，而在营商环境较差的地区，其作用基本失效，甚至可能会削弱民营企业创新质量。机制检验表明，在营商环境所决定的制度性交易成本与制度性收益两侧均存在门槛效应。异质性分析发现，在政府补贴影响企业创新质量过程中，战略性新兴产业企业和民营企业均存在营商环境门槛效应。本文突破了两极思维方式的局限，在同一模型框架内运用统一的逻辑体系，解释了现有文献关于政府补贴影响企业创新的不同见解，以“条件有效性”分析调和了学术界关于政府补贴政策“结果有效性”的认识分歧，并获得突破“创新悖论”的重要政策启示，即政府补贴应以优化营商环境为先导，只有将营商环境提升到相应水平，政府补贴才能有效促进企业实现高质量创新。

张可奕

年金保险是个体应对长寿风险的有效金融工具， 但市场需求却普遍不足， 在学术 界被称为“ 年金谜题” 。本文基于我国各地区人口死亡率与全国平均人口死亡率的差异性， 提 出了“ 长寿风险调整年龄” 概念， 借鉴相对风险厌恶系数， 引入个体长寿风险感知系数， 测算了 中国各地区的长寿风险调整年龄， 将长寿风险调整年龄和长寿风险感知系数结合， 对个体的长 寿风险感知进行度量， 并将其带入生命周期模型， 模拟个体的年金保险购买行为。结果表明， 长寿风险感知较高的个体对年金保险的需求更大， 为解释“ 年金谜题” 提供了一个以长寿风险 感知为基础的视角， 强调了个体长寿风险感知对年金保险需求的影响。