Using a difference-in-differences (DID) approach, this research assesses the effect of a firm’s credit rating issued by domestic rating agencies on stock price crash risk (SPCR). The results show that SPCR for treated firms decreases by 11% after firm ratings, suggesting that they can aggravate information content at the firm level. The effect is consistently more evident when stock price synchronization is higher and is stronger in firms with low media coverage, in firms with low audit quality, in state-controlled firms, and in firms with low investor protection. In addition, during a bear market year, the quality of firm ratings is higher. Overall, our findings support that investors could gain more information via firm ratings issued by credit rating agencies. Through our research, policymakers and investors can pay more attention to firm ratings that help play the information intermediary role of credit rating agencies.

Stock market prediction provides the decision-making ability to the different stockholders for their investments. Recently, stock technical indicators (STI) emerged as a vital analysis tool for predicting high-dimensional stock data in various studies. However, the prediction performance and error rate still face limitations due to the lack of correlational analysis between STI and stock movement. This paper proposes a correlational strategy to overcome these challenges by analyzing the correlation of STI with stock movement using neural networks with the feature vector. This strategy adopts the Pearson coefficient to analyze STI and close index of stock data from 8 Chinese companies in the Hong Kong stock market. The results reveal the price prediction of BiLSTM outperformed the GRU and LSTM in various datasets and prior studies.

This paper examines the ‘certification’ of government venture capital (GVC) programs, disputes whether the Chinese government venture capital (CGVC) can promote target firms’financing through the ‘certification’ on target firms, and how the ‘certification’ work. Using a dataset of 87865 Chinese listed firms over 2008–2018, we confirmed that CGVC’s investment promotes target firms’ equity financing but inhibits corporate debt financing through the certification effect and CGVC’s reputation. Moreover, the high reputation of GVC and high market awareness could strength the ‘certification effect.’Simultaneously, the ‘certification effect’is only effective for early and late-stage firms and private-owned firms, and invalid for mature stage and state-owned firms.

Despite the importance of property rights protection, evidence of their impact on thebirth, survival, and operations of the whole universe of firms, and the broad impact on the economy, is limited. In this paper we address this important question by utilizing unique administrative firm-level datasets in China. Using a difference-in-differences design, we find that the China’s 2007 Property Law led to significant more new private firms, firms that eventually survive, firms with less shareholders, and more new exporters, whereas the impact is the opposite for state-owned enterprises (SOEs). Moreover, we find that the switch in resources between private firms and SOEs contributes to higher economic growth without sacrificing environmental quality.

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.