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.

在全面建设社会主义现代化国家的新征程上，着力促进代际流动和缓解收入不平等是实现共同富裕的重要途径。本文基于2010～2022年CFPS微观家庭经济数据和数字普惠金融指数，构建七期代际收入转移矩阵和六期非平衡面板数据，从宏微观结合的视角切入探究数字普惠金融和家庭代际流动对收入不平等多维度收敛性的内在驱动机制和时空分异规律。研究发现：数字普惠金融发展后，居民收入分配结构更加合理，通过优化教育资源配置、拓宽就业创业渠道和完善社会保障体系三条理论渠道，显著促进了家庭代际流动和收入不平等收敛，其作用机制对农村地区、单亲家庭、男性子代的效果更为显著，对低人力资本、社会资本和物质资本家庭的作用更为明显。数字普惠金融促进家庭代际收入向上流动后，有助于城乡间、区域间以及体制内外等多维度收入不平等收敛，“了不起的盖茨比曲线”检验表明，收入不平等现象在东中西部区域基本收敛，但在城乡和体制内外还未实现收敛，面临城镇和体制内代际流动意愿缺失的现实困境。以上结论经过一系列稳健性检验证实了研究结论的可靠性，为如何认识共同富裕问题提供了全新视角和重要依据。

随着汽车金融下沉市场的拓展与多源异构数据的爆发，传统信用评分模型在兼顾预测精度与特定场景泛化能力时遭遇瓶颈。本文提出一种基于多模态混合专家模型（Multimodal Mixture of Experts, MMoE）的深度风控框架。该框架依托企业级AI中台，通过动态门控网络（Gating Network）将借款人的结构化征信、非结构化文本语义及动态行为特征智能路由至专属专家网络。基于 LendingClub 公开数据集的实证研究（有效映射汽车金融多模态场景）表明，MMoE 模型在 AUC 与 KS 指标上显著优于 LightGBM 等主流基准模型，且其期望校准误差（ECE）降至 0.015。研究证实，门控路由机制不仅提升了长尾人群的逾期预测准度，更为深度学习在金融领域的应用提供了宏观可解释性视角。本研究为金融机构构建高并发、易扩展的下一代智能风控底座提供了系统性的工程路径与理论支撑。