We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ several shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. We find statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Unlike the U.S. stock market, we find little evidence that the long-short anomaly portfolios can help predict market return due to the low persistence of asymmetric mispricing correction. We provide simulation evidence to sharpen our understanding of the differences found in the U.S. and Chinese stock markets.

As a product of the integration of traditional finance and Internet technology, digital finance plays an important role in micro enterprise innovation and even macroeconomic development. Based on the data of China's A-share listed companies from 2011 to 2018, this paper explores the effect of the development of digital finance on enterprise innovation. The research finds that there is an inverted U-shaped relationship between the development of digital finance and enterprise innovation. Further research shows that this inverted U-shaped impact of digital finance is stronger on strategic innovation of enterprises, suggesting that enterprises pay more attention to the "quantity" rather than "quality" of innovation. Finally, the inflection point of the inverted U-shaped relationship is brought forward by the industry competition and media pressure. This paper not only enriches the research on the relationship between digital finance and enterprise innovation, but also provides a theoretical basis for the development of digital finance and the improvement of financial regulatory framework.

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