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。

摘要：就业是最基本的民生，也是实现高质量发展的重要目标。实施包括地方政府债务置换在内的“逆周期”财政调节政策对促进企业劳动雇佣、实现“稳就业”目标具有重要意义。本文基于2010-2018年全国税收调查数据，采用广义双重差分法实证检验了债务置换政策对企业劳动雇佣的影响。研究发现债务置换具有显著的“稳就业”效应：城市债务置换强度每增加一个标准差，当地企业劳动力雇佣提升2.59%，并且与政府业务关联度更高的企业其就业提升效应更加明显。机制分析结果表明，债务置换通过缓解企业融资约束，改善企业的资金流动性，提振企业预期以促进企业劳动雇佣增加。本文的研究对评估2024年开始的新一轮债务置换政策效果，优化“逆周期”财政政策设计、实现四中全会提出的“稳就业”目标具有重要的意义。

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。

This paper provides a comprehensive analysis of excess returns specific to corporate bonds. We construct a measure of excess returns that uses synthetic Treasury securities with identical cash flows as benchmarks, thereby fully removing interest rate effects and isolating the component of returns specific to corporate bonds. Using a monthly sample from 2002 to 2024, we find that, in addition to being lower on average, the corporate-bond-specific excess return differs significantly in the cross section from both the standard excess return based on T-bills and the duration-adjusted return. We further examine the effects of a broad set of bond-level characteristics and systematic risk factors on bond excess returns. Together, these findings provide a foundational benchmark for future research on corporate bond returns.

大语言模型等生成式人工智能技术正深刻重构企业的业务模式与创新路径。本文以2018—2024年中国A股上市公司为研究对象，依托企业在线招聘数据与职业层面的大语言模型暴露度，构建了企业人工智能技术暴露度指标，实证检验了企业核心业务面临的技术冲击对其实质性创新的影响及其作用机制。研究发现，企业人工智能技术暴露度能够显著促进企业实质性创新，该结论在经过一系列内生性与稳健性检验后依然成立。机制分析表明，面对外生技术冲击，企业主要通过推动人力资本结构升级与提升研发投入强度，将技术红利转化为实质性的创新产出。异质性分析表明，在技术吸收能力较强、转型动机强烈以及具备长期积累优势的企业中，人工智能技术暴露度对企业实质性创新的促进作用更为显著。本文从外生技术冲击的视角，为理解人工智能重塑企业创新活动的内在机理提供了经验证据，对引导企业妥善应对技术更迭、优化要素配置进而实现高质量发展提供了现实参考。

We document a striking anomaly in international capital flows that we term "sanction arbitrage": U.S. investors exploited the 2014 sanctions on Russia by significantly increasing holdings in Russian equities while Rest-of-World (ROW) investors fled. We rationalize this behavior through a simple game-theoretic model where the sanctioning government faces a trade-off between geopolitical objectives and domestic welfare, effectively creating a protective shield for domestic investors and driving out ROW investors. Empirically, we confirm that pre-sanction U.S flows negatively predicted subsequent sanction designations. Consequently, U.S. investors internalized this protection to act as opportunistic buyers, absorbing fire-sale assets from exiting foreign investors and capturing significant excess returns from Russian stock holdings. These findings reveal that "smart" sanctions designed to preserve market access can inadvertently generate wealth transfers from foreign to domestic agents.

We study asset demand and price formation in a two-asset rational expectations equilibrium with narrow thinking, where traders imperfectly coordinate decisions across assets under non-nested price information. When the price of one asset increases, cross-asset inference from prices reduces expected demand for the other asset, which feeds back into the demand response for the original asset. Narrow thinking weakens internal coordination and amplifies reliance on price-based inference. As a result, more severe narrow thinking leads to higher own-price elasticities. The model delivers sharp implications for market liquidity and price informativeness in the presence of bounded rationality.

Serving on the official unsecured creditors' committee (UCC) of a bankrupt firm provides hedge funds with access to material nonpublic information (MNPI), which can facilitate their informed trading across firms and asset markets. We find that hedge funds increase equity turnover and execute more large trades in the quarters following UCC membership. In contrast, hedge funds do not exhibit such trading behavior after accessing public information about bankrupt firms or holding the bankrupt firm's debt without committee involvement. Importantly, these large trades often target firms with close economic ties to the bankrupt entity. Returns from these MNPI-driven trades are substantial.