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.

In China's labor market, there is a phenomenon that enterprises choose whether to provide "social insurance and housing fund" to laborers autonomously. This paper use micro-data from two leading Internet recruitment platforms and empirically finds that in a labor market with double-side information asymmetry, "social insurance and housing fund" is not only a cost but also a signal. Providing workers with "social insurance and housing fund" can both send a signal of stable operation to the labor market and identify high-quality workers for enterprises. With an instrument variable of local average social security payment rate, this paper excludes the endogenous effect of labor supply on wages while the signaling effect above is still significant. In addition, "housing fund" has a stronger signaling effect than "social insurance", and the strength of the two signaling effects is affected by the scale of the enterprises and the level of local payment rates. This paper also introduces a theoretical framework of two micro-mechanisms — signaling and screening — into the analysis. In terms of policies, this paper proposes to strengthen the information disclosure and the propagation of social security payment, and further reduce the financial burden of enterprises.

The Property Rights Theory states that clearly defined ownership is the premise of efficiency, while ambiguous property rights result in great externalities. We use the establishment of local SASACs as a quasi-natural experiment to investigate how unifying the supervision rights and responsibilities internalizes externalities and enhances SOEs’ innovation. The primary results show that the total innovation outputs and high-quality innovation outputs of SOEs governed by local SASACs (i.e., treatment group) improve after creating SASACs. The mechanism analyses show that both the pyramids level and risk-bearing level of local SOEs increase. In cross-sectional tests, we unravel that the innovation improvement effect is subject to the following five factors, including SASACs’ independence, local government quality, industry competition, SOEs managers’ motivation for promotion, and whether the SOE is in high-tech industry. Our paper provides empirical evidence for evaluating the innovation effect of the establishment of local SASACs with a quasi-natural experiment when the public ownership of SOEs does not change. Chun