Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.

We provide the first systematic analysis of the stock return lead-lag effect among firms connected through shared analyst coverage in China’s A-share markets. We measure the shared analysts-weighted average returns of connected firms (CF) and show that CF return is a significant positive predictor of future returns of the focal firms in the following one to 12 months. The CF-based long-short portfolio earns an abnormal return of 10% to 12% per year. The effect is robust to controls for the industry and geographic momentum effects. Further evidence shows that the CF momentum spillover effect is stronger when the focal firm shares more analysts with connected firms, is covered by more non-star analysts or analysts with lower levels of education, or is held by more stress-resistant institutional investors. Our findings contribute to the cross-asset momentum literature by documenting a new, strong, and long-lasting momentum spillover effect in the Chinese stock markets.

To explore what kinds of roles foreign investors take in a gradually opening financial market, we propose the abnormal holding value ratio (AHVR) of northbound investors among stocks through China’s Stock Connect Mechanism. We find that AHVR positively predicts the expected stock returns and significantly relates to firms’ quality-related fundamental information, especially profitability. Foreign investors learn the firm fundamentals before they invest in the Chinese market, which is different from the trading behavior of domestic individual investors. The AHVR premium is larger among firms with higher attention of analysts who focus on effective information and with lower attention of individual investors who have behavioral bias. In all, the northbound inflows are smart money, which will increase the efficiency of the Chinese market instead of simply chasing stars that only grab investors’ attention.

The pilot carbon emission trading schemes (ETSs) of China were created to combat climate change in a cost-effective and economically efficient manner, and their potential impact on regulated firms has drawn increasing attention. This study is conducted to provide empirical evidence on the effect of China’s pilot ETSs on firm-level financial performance during the period from 2008 to 2017. The empirical results show that the ETS pilots have a positive impact on firms’ profitability and value, and a negative impact on operational costs. We also find that the ETS pilots improve total factor productivity (TFP) but that changes in technology have an indirect suppressing effect on the relation between the ETS and short-term financial performance, providing support for the weak version of the Porter Hypothesis. Further, we show that the carbon emission price has a negative impact on firms’accounting-based performance but increases firms’ market value. Finally, we find evidence that, in contrast to state-owned enterprises (SOEs), non-SOEs do not experience significant improvements in their financial performance, led by the ETS pilots. Our findings have policy implications for firms’sustainable development and the transition to a low-carbon economy.

Using the staggered opening of governmental public data platforms in China, we employ the difference-in-difference approach to investigate how public data access affects stock price synchronicity. We find that stock price synchronicity significantly drops after the public data platform is established in a firm’s headquarters city. The underlying mechanism is reducing information acquisition costs rather than increasing market attention or corporate information disclosure quality. Furthermore, the informational role of public data platforms magnifies under higher informed trade risk, poorer corporate governance, or better regional economic and innovation capacity. We highlight the role of public data in facilitating financial market efficiency.

Recent financial frauds in China such as Kangmei Pharmaceuticals’ case have raised suspicion in the capital market and among academics about the reliability of accounting information of listed companies, and as a result, various non-financial information that is compulsory or encouraged to be disclosed by regulators and voluntarily disclosed by listed companies is gradually gaining attention from various stakeholders and academics. The corporate social responsibility (CSR) information is one of the most widely disclosed non-financial information by listed firms, but its reliability and motivation are also questionable, for example, is CSR commitment affected by firms’ financial information quality? Using China a-share listed companies that disclosed their corporate social responsibility reports from 2009-2019 as a sample, we investigate whether earnings management can influence corporate social responsibility by analysing the management’s motives embedded in earnings management, in order to further examine whether Chinese listed companies are morally motivated to undertake social responsibility or use social responsibility as a strategic tool to maintain the reputation of the firm and the management. The results of the study show that earnings management and CSR are positively correlated, and the finds continue to be robust when using 2SLS, Heckman two-step regression and propensity score matching to control for reverse causality and self-selection bias, proving that China's listed companies are ethically motivated to fulfil their social responsibility. Therefore, it is important to focus on the quality of earnings information in order to perceive the motivation of CSR when evaluating a company’s CSR commitment.

This paper creates default-probability-(PD)-implied credit ratings for Chinese firms following the S&P global rating standard. The domestic credit rating agency (DCRA) ratings are higher than the PD-implied ratings by ten notches on average for the identical level of default risk, implying that the domestic ratings are significantly inflated. The PD-implied ratings outperform the DCRA ratings in detecting defaults and complement the latter in predicting yield spreads. The PD-implied ratings draw information from operating efficiency-related variables; in contrast, the DCRA ratings pay attention to scale-based firm characteristics in credit risk assessment.

How to shape a firm’s stock price resilience in the increasingly uncertain environment has become an important topic. This paper investigates the effect of important market participantsfinancial analysts-on stock price resilience. Based on data from 3,444 listed firms from China, we find that firms with higher analyst coverage are more resilient during the Covid-19 induced crisis, which is manifested by a lower pandemic-induced decline in stock price, shorter duration of decline period, higher recovery probability, and shorter duration of the recovery period after the shock. This positive relationship is more prominent for small firms but does not depend on ownership type, and the ratio of star analyst coverage. Further channel tests show that analysts could help in attracting attention from media and institutional investors, improving corporate governance, and reducing financial constraints, which in turn enhance the ability of stock prices to absorb pandemic shocks.

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.

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.