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.

Can fully agentic artificial intelligence predict equity returns in real time? We build a novel dataset of daily AI-generated attractiveness scores spanning the entire Russell 1000 universe, continuously updated since April 2025. Each trading day, the model freely searches the live internet and produces quantitative scores for every constituent stock before the next market open, eliminating both look-ahead bias and attribution bias. A daily value-weighted portfolio of the 20 highest-ranked stocks earns a Fama-French six-factor alpha of 16.5 basis points per day (41.5% per annum), with an average bid-ask spread of 1.92 basis points. Predictability is sharply asymmetric: the model reliably identifies future outperformers but not future underperformers. The evidence points to stock picking rather than market timing: performance is strongest on down-market days, and the alpha does not come from betting on aggregate market direction. The AI’s reasoning traces help explain this predictability. Alpha is concentrated in traces with richer live-web evidence and stronger analytical structure. In the theme attribution, most active alpha reflects firm selection under an investment lens, not broad theme choice.

Entry of arbitrageurs need not increase with larger mispricing. Arbitrageurs trade mispriced assets and close positions once mispricing is corrected, seeking to minimize carry cost (shadow value of trading capital). More deployed arbitrage capital shortens the wait, lowering carry costs but also payoffs. Discretionary entry is profitable only in a “Goldilocks” region, where deployed capital is neither too low nor too high: enough capital is deployed to accelerate correction, while profits remain. The region’s upper bound follows from the condition that every sequence of entrants has a last entrant who cannot rely on subsequent discretionary entry.

Less informative strategic advice can produce more informative prices. We study an informed adviser who both trades on his own account and receives compensation linked to the post-report price. Holding private information fixed, we show how promotion can restrain manipulation motivated by trading profits. At sufficiently high reputation, raising compensation enough to eliminate false bad news used to buy cheaply improves market price informativeness in every equilibrium, even though the strategic adviser’s report becomes uninformative. Under further sufficient conditions, always-favourable strategic reporting yields the most informative current prices among equilibria at every interior reputation. We also derive analytical sufficient conditions under which higher reputation lowers the adviser’s expected payoff and worsens prices within the same equilibrium. Financial influencers motivate the joint incentives: a reward for higher prices can counteract the incentive to depress prices before buying, while greater credibility need not benefit its holder.

长周期考核、尽职免责与长期资金入市等制度安排陆续落地，围绕耐心资本培育的国家制度供给正稳步推进。然而，制度供给的完善并不等于政策目标的实现。耐心资本能否真正转化为实体经济中的逆周期投资能力，取决于微观机制是否成立。基于2008-2025年中国A股上市公司并购数据与机构投资者持仓明细，本文将耐心资本解构为身份、行为与方向三个维度，以逆势持仓占比与持仓稳定性构建测度，系统考察耐心资本对企业逆周期并购决策的影响。研究发现，方向耐心资本显著提高了企业在周期低位年发起并购的概率，该结论经置换检验、剔除竞争性解释和以行为口径构造的工具变量估计后依然成立。机制检验表明，耐心资本通过提高长期资金在企业机构持股中的占比缓解融资约束，通过降低持股期间的盈利波动恢复企业的执行能力。进一步分析发现，该效应集中作用于融资约束更强的企业，且耐心资本在低位期主动增持了随后发起逆周期并购的企业。研究发现为评估耐心资本政策、优化相关制度安排提供了企业层面的经验证据。

We examine whether and how the cultural background of corporate chairpersons, specifically clan culture, influences firm-to-firm lending decisions. Using hand-collected data on Chinese entrusted loans, a major segment of the country’s shadow banking sector, we find that firms led by chairpersons from prefectures with stronger clan culture are more likely to originate entrusted loans and charge lower spreads. We identify distinct mechanisms governing affiliated versus nonaffiliated transactions. In affiliated lending, our evidence suggests an in-group favoritism channel: culturally embedded chairpersons support affiliated parties despite higher ex-post default risk. Conversely, non-affiliated lending operates via an informational channel, where the positive impact of clan culture is more pronounced when geographic barriers are low, and manifests in larger size dispersion. Overall, our findings highlight the dual role of cultural norms in shadow banking, demonstrating that clan culture facilitates credit allocation while generating sharply different efficiency implications across relationship-based and arm’s-length markets.

We examine whether the characteristics of economically linked firms predict stock returns in the Chinese A-share market. We construct two peer-based signals: a peeraverage (PA) signal that aggregates the characteristics of fundamentally similar firms, and a peer-deviation (PD) signal that summarizes a firm’s characteristic deviations from industry-peer averages. PA captures shared fundamental information, whereas PD benchmarks a firm’s relative position against economically comparable peers. Portfolio sorts, factor-spanning tests, and Fama–MacBeth regressions reveal economically meaningful and statistically significant return predictability for both signals, with generally larger portfolio returns among non-state-owned enterprises. Both signals retain significant incremental predictive power after controlling for the full set of own-firm characteristics and flexible machine-learning forecasts based solely on these characteristics. Changes in both signals also predict future earnings, while return predictability is generally stronger among firms receiving less investor attention, consistent with limited-attention-induced underreaction. These findings highlight the distinct informational and benchmarking roles of economic networks and their predictive value beyond firms’ own characteristics.

Households form beliefs by recalling past experiences, but recall is context dependent. Variation in contexts alone can therefore generate non-fundamental disagreement. A stylized model à la Wachter and Kahana (2024) illustrates this cognitive source of disagreement. When combined with salience-based frictions in memory encoding, context-dependent recall also generates testable predictions for belief skewness. Using weather as a contextual cue that triggers the recall of past shopping experiences, I construct a measure of memory-based household inflation disagreement in the U.S. and show that it explains survey-based disagreement, with stronger effects in unusual contexts, and predicts greater belief skewness. The memory effect is distinct from experience and mood effects and generalizes across countries and outcome domains. Finally, memory-based disagreement weakens monetary policy transmission by attenuating intended consumption responses.

The development of industrialization runs through the history of the People’s Republic of China. In the early years of the founding of the People’s Republic of China, the Soviet Union provided important help for China’s industrialization construction, capital lending, talent training and technology transfer. By analyzing the proportion of China’s industrial output value in the total output value and the output of major industrial products during the period of the Soviet Union, it shows that the assistance of the Soviet Union had a very positive role in promoting the realization of ChinaIs industrialization, and helped China to lay the foundation of industrialization. In addition, some problems in accepting the Soviet Union’s aid to China also deserve our attention when promoting the new industrialization. The research explores both the internal and external factors that led to China’s acceptance of Soviet assistance. The study examines the political, economic, and historical contexts that created a favorable environment for such aid, analyzes the various modes and scales of the assistance implemented, and presents statistical data that underscore the transformative social and economic achievements derived from this partnership. Furthermore, the paper highlights the contributions of quantitative analyses in understanding the aid’s impact, discusses the experiences and lessons learned from the Soviet aid period, and assesses the evolving dynamics of Sino—Soviet relations, particularly the gradual divergence in economic development models. The findings shed light on how Soviet aid laid the groundwork for China’s industrialization while also foreshadowing future ideological and developmental separations.

环境规制能否通过改善企业行为产生声誉治理效应，是评价环境政策微观经济 后果的重要维度。本文以 2012—2024 年沪深 A 股上市公司为研究样本，将 2018 年《中华 人民共和国环境保护税法》实施视为准自然实验，采用双重差分模型考察环境规制对企业声 誉风险的影响及其作用机制。研究发现：环境规制显著降低了企业声誉风险，经过平行趋势 检验、安慰剂检验、替换变量、排他性检验以及固定效应双重机器学习等一系列稳健性检验 后，该结论依然成立。机制分析表明，环境规制主要通过改善公司治理、提高信息披露质量 和提升 ESG 责任表现降低企业声誉风险。异质性分析发现，在媒体关注度较高、外部审计 监督较强、融资约束较低以及技术水平较高的企业中，环境规制的声誉治理效应更为显著。 进一步研究表明，声誉风险上升会提高企业信贷风险溢价，并对财务绩效产生一定的负向影 响，说明环境规制还能够通过缓释声誉风险产生融资改善效应和一定的经营改善效应。本文 从声誉风险视角拓展了环境规制微观经济后果的研究，为完善环境规制体系、加强企业环境 治理和促进企业可持续发展提供了经验证据。