本文重点探讨与人工智能和资本市场相关的一个问题,即如果AI统治算法，证券市场会变怎样？ 本文以哲学上的 “二律背反” 和香农熵理论为方法，分析金融人工智能对证券市场信息效率的影响。 熵作为信息论中的一个重要概念， 被用来衡量信息的混乱程度， 即信息的不确定度；“二律背反” 则强调了一种辩证视角。 经实证研究发现： 人工 智能对于证券市场效率的影响并非二元对立，AI技术降低信息 “噪音”， 提高投资者认知概率（降熵）;但AI算法的同质化、 市场过度反应或其他风险因素会削弱市场效率， 增加市场的信息不对称性 （升熵）。AI对以中美为代表的新兴市场和成熟市场的影响因市场而异， 我们认为人工智能的出现不是一般性技术冲击 而是属于技术 “奇点” 或约瑟夫·熊彼特的 “创造性破坏” 的范畴， 是真正长期将对资本市场产生持续性影响的重大因素， 这也是本文的一个主要研究价值。

随着我国资本市场全面深化改革持续推进、资管新规落地打破刚性兑付，居民家庭财富配置加速向权益类资产转移，证券零售业务作为券商连接居民财富与资本市场的核心端口，正经历从传统通道经纪业务向现代财富管理业务的深刻转型。本文系统梳理我国证券零售业务从线下渠道时代到全链路生态时代的四个发展阶段，结合行业发展政策、市场环境、技术变革与客户需求变迁，剖析各阶段的核心逻辑、业务特征、盈利模式与行业痛点；对比分析传统线下、互联网拓客、生态化运营、TAMP创新模式等主流拓客方式的底层逻辑、优劣势、适配场景与实践效果，并重点结合美国利普乐（LPL Financial）成熟模式，深入探讨国内券商TAMP模式的本土化实践现状、差异与优化路径；在此基础上提炼行业发展面临的同质化竞争、获客成本高、合规约束收紧、投顾能力不足、数字化转型滞后等核心挑战，结合资本市场发展趋势与居民财富管理需求升级方向，提出针对性发展对策与未来趋势预判，为券商零售业务突破发展瓶颈、实现高质量财富管理转型提供理论参考与实践思路。

Do investors respond to what fund managers say, or how they look saying it? Using 2,000 live-streamed sessions by Chinese ETF managers and multimodal machine learning, we show that managers’ facial expressions, not their words, drive fund flows. A one-standard-deviation increase in positive facial affect raises next-day flows by 0.17pp (260% of mean). Vocal tone shows weak effects; textual sentiment shows none. Critically, facial expressions predict flows but not returns, indicating pure persuasion rather than information transmission. Effects strengthen when investors are emotionally vulnerable (down markets, retail-heavy funds) and persist 2-3 weeks before dissipating. Our findings challenge the emphasis on textual disclosure in finance and raise questions about investor protection as video communication proliferates.

This paper examines whether mandatory ownership disclosure aligns incentives or distorts in-vestor beliefs. Using a sample of 1,436 Chinese equity-oriented mutual funds from 2012 to 2023,we find that higher managerial and senior ownership are significantly associated with larger in-flows, suggesting that investors treat ownership as a quality signal. However, we find no evidencethat ownership forecasts superior future returns or risk-adjusted alphas. Mechanism tests showthat the ownership-flow effect is much stronger in low-marketing funds and that managers increaseownership after weak flows, a countercyclical pattern inconsistent with overconfidence and consis-tent with strategic remedial signaling. Overall, ownership disclosure appears to operate primarilythrough investor perception rather than information about managerial ability, weakening the linkbetween capital allocation and true skill in the mutual fund industry.

This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale requirements. Applying this methodology to the U.S. equity market, we document that long-short portfolios formed on the simple linear combination of signals deliver an annualized Sharpe ratio of 2.75 and a return of 54.81%. Finally, our empirics demonstrate that self-evolving AI offers a scalable and interpretable paradigm.

This paper examines the impact of Exchange-Traded Fund (ETF) ownership on corporate earnings management. We find that ETF ownership is associated with a significant reduction in earnings management, and this result remains robust across a wide range of endogeneity tests and robustness checks. Further analyses reveal that ETFs exert a pronounced mitigating effect on sales manipulation, production manipulation, and expense manipulation. Mechanism tests indicate that ETFs curb earnings management by improving stock liquidity and strengthening external monitoring. We also find that the influence of ETFs is stronger in private firms, in firms with lower information transparency, and in firms with CEO duality, suggesting that ETFs serve as a more prominent external governance force when internal governance mechanisms are relatively weak. Overall, this study enriches the literature on the economic consequences of ETFs and provides new empirical evidence that financial innovation in emerging markets can help alleviate the information risk faced by investors.

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, we provide evidence that both extreme events (COVID-19 quarantines) and everyday contexts (geographic locations) 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 substantial losses 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 evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which contextual cues affect financial markets.

This paper documents that the physical appearance of CEOs, specifically excess body weight, is priced in the capital market. In the absence of explicit health disclosures,market participants interpret obesity as a proxy for latent health risks and potential managerial disrupts, thereby demanding a compensation premium. Our analysis reveals that (1) IPOs of firms with obese CEOs have lower first-day performance, (2) these firms achieve a lower valuation, (3) the stocks of these firms have lower liquidity and (4) they provide higher stock returns thereafter. A quasi-natural experiment based on the invention of anti-obesity medications provides supporting causal evidence.

商业保险是家庭风险管理的重要工具，而人口跨文化流动的日益频繁正深刻影响着家庭的保险决策行为。本文基于2019年中国家庭金融调查（CHFS）数据，以北方家庭为研究对象，实证考察了跨文化流动对家庭商业保险参与的影响效应及作用机制。研究发现：第一，跨文化流动显著促进了家庭商业保险购买，该结论在采用工具变量法缓解内生性问题，以及进行倾向得分匹配、替换样本、Oster边界检验等一系列稳健性检验后依然成立。第二，机制分析表明，跨文化流动通过提升家庭风险偏好与金融素养水平两条渠道发挥作用，前者改善家庭风险态度，后者增强风险认知与评估能力，共同推动商业保险参与。第三，异质性分析显示，该效应在非健康、低学历及高收入家庭中更为显著。本文从人口流动与文化交融的双重视角，为理解我国家庭商业保险参与差异提供了新的微观证据，并为完善流动家庭风险保障体系、加强金融知识普及提供了政策启示。

We examine the information content of the call-put implied volatility spread (IVS) of Shanghai Stock Exchange 50 ETF options. Empirically, the IVS significantly and negatively predicts future SSE50 ETF returns at both weekly and monthly horizons. This predictability is robust both in-sample and out-of-sample, which stands in contrast to prior evidence from the U.S. options market. We explore several potential explanations and show that the IVS is closely linked to the option-cash basis. Its predictability is consistent with the model of Hazelkorn, Moskowitz, and Vasudevan (2023), where the option-cash basis reflects liquidity demand common to both options and underlying equity markets.