保持金融稳定是目前中央“六个稳定”政策中的重中之重，系统性金融风险关乎经济发展。本文手工整理了 2010 年~2017 年非上市银行数据，利用集成机器学习（Ensemble ML） 技术测算中国 5 家国有商业银行、12 家股份制商业银行及 103 家城市商业银行的系统性风险，弥补了V-Lab 仅包含部分上市银行的缺陷。发现：总体系统性风险不断上升，各年度平均有 25%以上的急速增长，2016 年底出台的一系列政策有效控制了这一上升趋势，2017 年显著下降 10.3%；SRISK 份额最高的 5 大国有商业银行仅占 54.78%，城市商业银行的系统性风险份额不断上升、已成为中国系统性风险的潜在累积点；区域性演进上呈现向东南沿海积聚的特点。控制区域性发展的回归模型进一步揭示了商业银行系统性风险出现和上升的影响机制：总资产有显著的正向影响，支持“大而不能倒”的观点；杠杆率和期限错配是重要影响因素，银行的杠杆率降低 1%，系统性风险上升的概率显著下降 0.2%，系统性风险出现的概率下降 0.84%，上一年度出现风险的银行该年系统性风险上升的概率下降 0.5%，支持了“降杠杆”政策，且对非系统重要性银行降杠杆的效果更显著；提高流动性有利于显著降低系统性风险，但调控效果没有降杠杆强。最后利用国家层面和省际层面累计的系统性风险，发现金融风险对经济增长的确存在显著影响。

本文用中国上市商业银行的数据，研究银行存款规模的利率风险跨期平滑机制。该机制是在存款稳定性的基础上，以利率上升时期的收益自动弥补利率下降时期的损失，实现对批发融资利率风险和非缺口的资产和负债、贷款活动等项目利率风险的跨期平滑。研究发现，银行存款对利率风险具有存款利率粘性、存款规模粘性和存款特权三方面的稳定性特征。银行存款规模对利率变化的稳定性特性，使其能够跨期平滑利率风险。而且，在一定范围内，存款规模越大，越有利于发挥存款的三种稳定性特征，跨期平滑作用越强；当存款规模超过一定的范围，风险跨期平滑功能将会反转，因此应该控制银行存款规模在合理的范围之内。按存款结构细分的讨论中，定期存款、个人存款和公司存款都起到了利率风险跨期平滑作用；个人存款中的个人定期存款的风险跨期平滑功能更强，表明存款的稳定性越强，对利率风险的跨期平滑作用也越强。银行存款规模对利率风险的跨期平滑，为利率风险的管理提供了新思想；在一定程度上，利率风险管理变成银行存款的管理。此外，我们的研究也侧面印证了期限错配可以降低利率风险，利率风险敞口不必为零。我们认为资产和负债不必进行期限匹配，而要重视真实利率敏感性的匹配。

血缘是中国社会关系中的重要纽带。兄弟姐妹关系更是最为密切的亲属关系之一。本文利用中国家庭收入调查（CHIP）数据，研究兄弟姐妹数量对家庭资产选择的影响。研究发现，户主兄弟姐妹数量的增加显著提高了家庭风险金融市场参与和风险金融资产占比。进一步的分析显示，兄弟姐妹数量的增加提高了家庭的信贷可得性。同时，这种“兄弟姐妹效应”主要存在于收入不确定性较大、健康风险较高以及所在地区金融发展水平较低的家庭中。这些结果表明以兄弟姐妹为代表的亲属网络通过建立非正规的风险分担机制促进家庭参与风险资产市场。此外，本文利用计划生育政策这一外生冲击构造工具变量，验证了结果的稳健性。本文的研究具有丰富的理论和政策含义。

债券信用评级旨在为资本市场参与者提供真实可靠的债券违约风险信息，但我国债券信用评级虚高问题备受媒体和学界质疑。然而，评级较高并不必然意味着评级质量较差。本文将债券信用评级拆分为公开信息和私有信息两个部分，重点考察评级机构利用私有信息调整债券评级是否会影响债券信用利差，以探究我国债券信用评级机构是否真的具备专业能力。研究发现，信用评级机构利用私有信息调高（调低）评级能显著降低（抬高）债券信用利差，且以发行人所在地是否开通高铁、发行人和评级机构之间的旅行距离作为信用评级机构获取私有信息的工具变量时，该结论也依然成立。而且，评级机构利用私有信息调高评级的行为并未导致未来年度评级调低。这表明，总体上，我国债券信用评级机构具备一定的专业能力。但进一步研究发现，对于存在刚性兑付预期的债券，如国有企业发行的债券和银行间市场交易的债券，评级机构私有信息的作用会显著下降，而当债券市场的刚性兑付预期被打破后，评级机构私有信息的作用会显著提升。此外，中债资信这一“投资人付费”的信用评级机构并不具有更强的私有信息挖掘能力，但其进入评级市场后，“发行人付费”的评级机构掌握的私有信息作用会显著上升。最后，本文还利用事件研究法发现，债券信用评级调整会引起债券价格显著变化，进一步证实我国债券信用评级中的确包含了有效的私有信息。

The goal of this paper is to discuss how we establish the “Hammer (CAFÉ) Credit System” by applying Gibbs sampling algorithm under the framework of bigdata approach to extract features in depicting bad or illegal behaviors by following the “five step principle” applying international credit rating standards. In particular, we will show that our Hammer (CAFÉ) Credit System is able to resolve three problems of the current credit rating market in China which rate: “1) the rating is falsely high; 2) the differentiation of credit rating grades is insufficient; and 3) the poor performance of predicting early warning and related issues”. In addition the Hammer (CAFÉ) credit is supported by clearly defining the "BBB" as the basic investment level with annualized rate of default probability in accordance with international standards in the practice of financial industries, and the credit transition matrix for “AAA-A” to “CCC-C” credit grades.

This paper investigates whether and how high-speed rail (HSR) construction affects local bank performance. Using the difference-in-difference method, we find that the city commercial banks (CCBs) significantly experience an overall decrease in ROA after HSR is introduced in the headquarters city. Mechanism analysis suggests that the HSR-driven city connectivity imposes the local CCBs on the intensified banking competition related to capital flows, and governance improvements associated with information flows. HSR exerts more pronounced impacts under higher financial liberalization. The findings are robust to the endogeneity concerns. We highlight the indispensable role of transport infrastructure in banking development.

Using cross-border holding data from all custodians in China’s Stock Connect, we provide evidence that Chinese mainland insiders tend to evade the see-through surveillance by round-tripping via the Stock Connect program. After the regulatory reform of Northbound Investor Identification in 2018, the correlation between insider trading and northbound flows decays, and so does the return predictability of northbound flows. The reduction of return predictability is especially pronounced among less prestigious foreign custodians and cross-operating mainland custodians, behind which mainland insiders are more likely to hide. Our analysis sheds light on the role of regulatory cooperation over capital market integration.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

We examine how hometown ties among local politicians affect capital allocation in China. We use a difference-in-differences design that relies on the exogenous replacements of city officials. Our results indicate that hometown ties between city party secretaries increase city-dyad investment by 10% and firm registrations by 1%. These effects are larger between distant cities and for the investment of small and private firms. Comparing the effects before and after the Chinese anti-corruption campaign, we provide nuanced evidence showing that, although hometown ties may entice the rent-seeking activities of officials, such activities may promote economic growth.

We examine the impact of institutional investor networks on firm innovation in China. Employing the unexpected departure of mutual fund managers and the inclusion of the Shanghai-Shenzhen 300 index as identifications, we find that institutional investor networks have a positive impact on firm innovation. Specifically, firms that are hold by well-connected institutional investors are motivated to make R&D investments and receive greater patents than their counterparts. This positive influence is more pronounced for non-SOEs and for firms located in less-developed regions, indicating that institutional investor networks act as information flow facilitator and a value certifier to encourage innovation activities.