Can fully agentic AI nowcast stock returns? We deploy a state-of-the-art Large Language Model to evaluate the attractiveness of each Russell 1000 stock each trading day, starting in April 2025 when AI web interfaces enabled real-time search. Our data contribution is unique along three dimensions. First, the nowcasting framework is completely out-of-sample and free of look-ahead bias by construction: predictions are collected at the current edge of time, ensuring the AI has no knowledge of future outcomes. Second, this temporal design is irreproducible once the information environment passes. Third, our framework is fully agentic: we do not feed the model curated news or disclosures; it autonomously searches the web, filters sources, and synthesises information into quantitative predictions. We find that AI possesses genuine stock-selection ability, but that its predictive power is concentrated in identifying future winners. A daily value-weighted portfolio of the 20 highestranked stocks earns a Fama-French five-factor plus momentum alpha of 19.4 basis points and an annualised Sharpe ratio of 2.68 over April 2025–March 2026. The same portfolio accumulates roughly 49.0% cumulative return, versus 21.2% for the Russell 1000 benchmark. The strategy is economically implementable: the average bid-ask spread of the daily Top-20 portfolio is 1.79 basis points, less than 10% of gross daily alpha. However, the signal remains asymmetric. Bottom-ranked portfolios generally exhibit alphas close to zero, while the strongest predictive content sits in the extreme top ranks. Delayed-entry tests further show that predictability does not vanish after a single day; rather, the signal remains positive over a broad window of subsequent entry dates, consistent with slow information diffusion rather than a fleeting overnight anomaly.

This paper studies how mutual fund managers update their beliefs as macroeconomic conditions change. Using regulator-mandated reports from Chinese mutual funds, we measure the intensity of belief updating from year-over-year changes in stated outlooks and decompose those updates into macro and micro themes. We show that belief updating is state-contingent: funds with more intensive belief updating shift their narratives toward macro (micro) topics during recessions (expansions) and concurrently reduce (increase) procyclical stock exposures and on-site company visits. This state-contingent belief updating predicts superior performance when matched to prevailing economic conditions, with macro-oriented updates paying off mainly for high-updating funds in recessions and micro-oriented updates paying off more broadly in expansions. Investors recognize this signal of skill, allocating greater flows to these funds, especially when past returns are less informative. Finally, belief updating is stronger for younger managers and for funds from newer, smaller families, consistent with signaling under career and competitive pressures.

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.

耐心资本作为一种强调长期价值创造的资本形态，对于企业可持续发展及金融市场稳定运行具有重要意义，但其难以自发形成，需要外部激励与内部治理的共同培育。基于权力—收益矩阵，本文引入了耐心资本的主要来源——股东，构建了股东权力—收益矩阵分析框架，旨在从股东权力和股东收益视角进一步探讨耐心资本的形成机制。选取2014—2023年沪深A股上市公司为样本，采用固定效应回归检验主效应，结果发现，股东权力与股东收益均显著正向促进耐心资本形成，在通过滞后一期、替换被解释变量、调整样本期、增加固定效应以及工具变量2SLS缓解潜在内生性后，结论依然具有稳健性；异质性分析显示，在制造业、获得标准审计意见、上一年未亏损、南方地区以及非重污染企业中，上述效应更为显著；中介效应检验表明，股东权力主要通过缓解融资约束促进耐心资本形成，股东收益主要通过降低融资成本促进耐心资本形成；调节效应检验证明，内部控制质量均能够正向强化股东权力与股东收益对耐心资本的促进作用。研究结论回答了耐心资本何以形成的问题，为通过提升公司股东治理质量、壮大耐心资本供给提供了理论依据与实践启示。

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.

人工智能作为具备通用目的技术特征的新兴技术，其经济效应能否充分释放，在很大程度上取决于与企业既有技术结构的融合程度。本文提出"人工智能技术嵌入"概念，用以刻画人工智能技术与企业既有技术体系之间结构融合的深度。本文利用国际专利分类号（IPC）的共现关系，识别人工智能技术向各非人工智能技术领域的渗透强度，构建IPC4分类层面的嵌入系数，进而加权整合企业专利结构，形成企业层面的人工智能技术嵌入度指标。基于2016—2023年国家知识产权局全量专利数据与沪深A股上市公司匹配数据，本文系统呈现人工智能嵌入扩散的典型事实，并检验嵌入度在人工智能投入与企业全要素生产率之间的调节作用。研究发现：第一，样本期内人工智能与非人工智能技术的共现关系持续增强，2022年后随大模型技术兴起呈现明显加速；第二，行业间嵌入度差异显著，高端制造与智能医疗等技术密集行业嵌入度较高，传统行业则相对滞后；第三，人工智能投入对全要素生产率的边际效应具有显著的结构依赖性，仅在嵌入度较高时，人工智能投入方能与企业既有技术体系形成协同，实现生产率的有效提升。本文研究表明，人工智能并非可以脱离企业技术结构独立发挥作用的生产要素，其经济回报高度依赖于在企业技术体系中的嵌入基础。这一发现为政策层面引导企业提升技术体系适配能力、推动人工智能与实体经济深度融合提供了理论依据与实践启示。

China has become the world’s leading innovator in renewable energy technologies, accounting for 85% of global new patents in 2023 (up from 15% in 2009). This paper examines how China’s hybrid system—state-owned electricity enterprises dominating downstream and private firms manufacturing upstream equipment—has facilitated this transformation. National renewable energy targets, enforced through career incentives for SOE managers, create strong and predictable downstream demand that stimulates upstream innovation. Using global supplier–customer pair-level data, we show that revenue growth among Chinese downstream customers is significantly associated with their suppliers’ subsequent patenting. This effect is absent for non‑Chinese customers but stronger among those politically aligned with the central government. Exploiting the 2022 clearance of feed‑in tariff subsidy arrears to electricity firms as a demand shock provides causal evidence. Direct subsidies to suppliers have no significant effect, whereas subsidies to fast‑growing downstream customers do. Finally, this arrangement also leads to overinvestment and excess capacity among suppliers.

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。