This paper introduces reinforcement learning to examine the effect of trading on noise in a dynamic limit order market equilibrium. It shows that intensive noise liquidity provision (consumption) increases speculators' liquidity consumption (provision), improving (reducing) market liquidity. Channeled by uninformed chasing and informed aggressive liquidity provision, the increasing noise liquidity provision and consumption, respectively, improve price efficiency, generating a U-shaped price efficiency to the noise trading uncertainty on liquidity provision and consumption. Associated with a hump-shaped (U-shaped) profitability for the informed (uninformed) at a U-shaped noise trading cost in the noise trading uncertainty, this implies that, at increasing noise trading cost, intensive noise liquidity provision improves market liquidity, price efficiency, order profitability of informed traders, and reduces the loss, even makes profit, for uninformed traders.

We document a novel "news extrapolation" behavior among investors, which distorts the market reaction to corporate news. Specifically, investors tend to extrapolate the value of past news in the immediate reaction to the newly arrived news. News extrapolation generates a biased price reaction to news, which is completely reversed afterwards. Furthermore, the tendency of news extrapolation is related to the recency, consistency, and value uncertainty of news. Investors extrapolate not only from news of the same category but also from news of different categories. By analyzing the trading behavior and sentiment of different investor groups, we find that retail investors tend to be news extrapolators, while institutional investors trade against the news extrapolators.

The Hurst exponent is a key parameter for characterizing the long memory of high-frequency time series. However, traditional estimators often exhibit systematic biases due to the influence of high-frequency endogenous noise and low-frequency trend shifts. Theoretical derivations show that endogenous noise contemporaneously correlated with the latent signal possesses a spectral density in the first-differenced series that is asymptotically equivalent to a squared sine functional form. Accordingly, the proposed estimator incorporates a corresponding spectral density component to fit the high-frequency error. Simultaneously, the model introduces a SCAD penalty term to control the low-frequency spectral divergence caused by structural breaks, thereby mitigating spurious long memory in parameter estimation. Monte Carlo simulations demonstrate that the Penalized Mixture Whittle estimator yields smaller finite-sample biases and root mean square errors in scenarios involving both trend disturbances and endogenous noise. Empirical analysis shows that the estimates obtained using this method are robust to changes in sampling frequency. In further volatility forecasting experiments on commodity futures, the linear forecasting model constructed based on the parameter set achieves higher prediction accuracy than benchmark models such as HAR, as confirmed by the Diebold-Mariano test. This paper provides an effective econometric tool for high-frequency data inference in the presence of composite statistical disturbances.

I study whether workplace smoking bans change how bond investors assess firm risk. Using staggered state adoption across U.S.\ states from 2002 to 2012 and a heterogeneity-robust difference-in-differences design, I find that smoking bans increase six-month cumulative abnormal bond returns by about 90 basis points. The average effect is only the starting point: the response is much larger for speculative-grade issuers and firms with low interest coverage, indicating that investors reprice the policy where downside operating risk matters most for debt values. Mechanism tests point most clearly to improved operating performance and lower worker turnover, while broader financial-constraint, liquidity, and duration channels remain close to zero. Alternative estimators, placebo diagnostics, and geographic spillover checks all support the interpretation that workplace smoking bans trigger targeted credit-risk reassessment rather than a generic regional shock. My findings connect public-health regulation to capital-market outcomes and show how non-financial policy shocks can reveal economically meaningful information about corporate credit risk.

大豆是我国重要的粮油兼用作物，在保障国家粮食安全方面具有战略意义。山东省作为我国大豆主产区之一，面临着种植面积缩减、种植效益偏低、生产成本上升等多重挑战。收入保险在保障农民利益，助力农业蓬勃发展中发挥着不可替代的作用，且随着农业保险的高质量发展，其一定会成为未来农业保险发展的重点。同时，农业生产具有显著的地域差异性，统一费率的农业保险产品难以满足不同地区的实际需求。故本研究以山东省大豆为研究对象，基于2005-2023年的历史数据，构建多维风险区划指标体系，采用系统聚类法将山东省16个地级市划分为低、中低、中高、高风险四个等级。在收入保险定价方面，采用Copula函数刻画单产与价格的相关关系，并创新性地构建双层定价模型（加入村级层面产量波动）捕捉空间异质性风险，基于实际大豆保险赔付率进行参数校准，定价也依照保险实务采用相对免赔机制，比较不同免赔率下的费率变化。研究发现，村级层面的空间异质性风险显著，单层模型严重低估真实风险，双层效应在高风险区尤为明显。本研究对山东省大豆收入保险实际定价的改进具有重要参考价值。

This paper studies how air pollution shapes offline cashless spending in China. Using monthly transactions from 118,698 merchants in 332 cities from 2019 to 2023, we find that higher pollution raises cashless spending. Instrumental variable and regression discontinuity designs confirm a causal effect. The increase comes mainly from more frequent but smaller purchases and greater participation by new customers. Spending also rebalances from postponable durables toward high-frequency, proximity-based categories, while durables respond little. These results uncover a behavioral channel whereby poor air quality shifts the margins and the composition of offline cashless commerce.

在我国当前低利率环境下，债券收益率持续下行，多元化资产配置的战略意义愈发凸显。本文基于桥水全天候理念，构建了适用于中国投资者的宏观因子增广Black-Litterman（ABL）模型，将宏观信号嵌入传统Black-Litterman（BL）模型的观点矩阵，同时整合风险平价先验基准，有效缓解了传统BL模型参数敏感性高及经济解释性不足问题。基于2012-2024年股债商汇四大类14项资产的回测表明，ABL模型实现了17.7%的年化收益和0.66的夏普率，优于传统BL模型及其他常用基准，且在波动率和最大回撤等风控维度更为稳健。ABL模型为资产配置提供了兼具理论创新与实践价值的参考方案。

可转债是上市公司重要的融资工具，其强制赎回条款在实践中可能被上市公司利用，通过操纵股价满足强制赎回条件，以便加速转股进程。基于2006-2025年中国A股市场数据，本文分析了可转债强制赎回背景下的股价异动现象。研究发现：在强制赎回条件触发的关键窗口期，正股价格出现显著异常拉升，强制赎回公告后则迅速反转，呈现“拉高达标”的“倒V”型反转特征；面临较高偿债压力和融资成本的发行人，更倾向于通过市场操纵推动股价达到强赎条件；微观交易数据显示，临近强赎节点时，市场买卖失衡、知情交易增多及大额交易增加，验证了主力资金通过短期内大量买入推升股价的操纵机制。本文揭示了可转债强制赎回条款可能诱发的道德风险，为完善市场制度设计和保护中小投资者权益提供了实证依据。

This study examines how global turbulence factors shape volatility spillovers among emerging stock markets through the lens of risk cycles. We find that emerging market connectedness exhibits clear regime heterogeneity across risk cycles, while also preserving several persistent structural patterns. Specifically, trade policy uncertainty (TPU) and economic policy uncertainty (EPU) serve the dominant drivers during risk outbreak and risk accumulation periods, respectively. Meanwhile, sustainability uncertainty (ESGUI) consistently plays a leading driver role in both regimes, while physical climate risk plays a comparatively limited role. Furthermore, the effects of these core turbulence factors are nonlinear and threshold-dependent, highlighting the importance of accounting for risk cycle heterogeneity and nonlinear dynamics when assessing emerging market risk transmission.

This study investigates the role of contextual cues in memory-based decision-making within high-stakestrading 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.