We explore the value premium in the Chinese stock market and how to exploit it using a new investor sentiment index. We extensively discuss the performance of BM, CFP, EP and SP factors in China. Consistent with the experience of other countries, BM generates more of a value premium in small cap performance, while EP generates more of a value premium in large cap stocks in the Chinese stock market. First, we construct a novel value factor based on BM, EP and SP. We obtain the loading weights of each value indicator in each market value by partial least squares. The novel value factor outperformed all other value factors. Second, we explore the relationship between value premium and investor sentiment. Different from evidence from most developed countries, the value stocks perform better than growth stocks in the bull market in China. Our evidence suggests investing in value stocks can get more profit when market sentiment is low.

We analyze the relationship between insider trading density and the future stock returns in Chinese listed companies. We introduce a new aspect of the trading pattern, insider trading density, to investigate the information advantage held by insiders. Insiders who trade at a low density during their tenure are less likely to be expected to trade than high trading density insiders. The expectedness of trading patterns reflects insiders’ trading incentives and conveys valuable information to predict future stock prices. Controlling for company, deal, and insider-specific characteristics, we find that low trading density insiders earn higher excess returns than high trading density insiders in a portfolio mimicking long strong purchases and short strong sales. In addition, we show that the insider’s position is a source of information advantage: prominent officers such as CEOs and CFOs are more likely to be low trading density insiders, while non-executive directors and supervisors are more likely to be high trading density insiders.

In forecast surveys of aggregate macroeconomic and financial variables, the correlation between forecast errors and forecast revisions is positive at the consensus level, but negative at the individual level. Past literature has interpreted this discrepancy as evidence of underreaction to news at the aggregate level and overreaction at the individual level. In this paper, I challenge this view by arguing that noise in predictive judgment can account for the difference. Using a stylized model, I examine how introducing judgment noise at the individual level changes the interpretation of the correlation coefficients. First, a negative coefficient at the individual level no longer necessarily means overreaction. Second, the coefficient at the consensus level underestimates the degree of underreaction. Using forecast survey data, I provide evidence that judgment noise is large enough to reconcile the difference between the two coefficients. The structural parameter measuring over-/underreaction mainly points to underreaction, regardless of whether the model matches correlation coefficients at the individual or aggregate level.

Understanding the impacts of new technology and innovations on the banking sector is important and of growing interest. However, there is limited research on the detailed channels of the impacts, and consequently, the evaluations for the aggregate welfare impacts. We contribute both empirically and quantitatively. We construct a new data set for Chinese banks. We ffnd banking innovations can improve efficiency, and mostly reduce non-interest costs but not so much on deposit rates. We show the ffnding is quite robust under a battery of checks. In a new structural, quantitative model, banks have heterogeneous capital, decide innovation investment and also risky lending, face regulations on the capital requirement and have limited liability. When aggregate new technology improves, it can reduce financial intermediation costs and social deadweight loss; however, it will also change the bank’s risk consideration and increases moral hazard when the cost is largely reduced. We also find several other new implications for R&D investment credit policy and Capital Requirement policy (CAR).

Fund managers’ ability to evaluate risk has important implications for their portfolio management and performance. We use a state-of-the-art deep learning model to measure fund managers’ forward-looking risk assessments from their narrative discussions. We validate that managers’ negative (positive) risk assessments lead to subsequent decreases (increases) in their portfolio risk-taking. However, only managers who identify negative risk generate superior risk-adjusted returns and higher Sharpe ratios, and have better intraquarter trading skills, suggesting that cautious, skilled managers are less subject to overconfidence biases. interestingly, only sophisticated investors respond to the narrative-based risk assessment measure, consistent with limited attention by retail investors.

This paper uses the enactment of China’s 2007 Property Law (the Law), which reduces the risk of expropriation by local governments, as the setting to investigate the importance of property rights protection for private firm investment. Using propensity score matching and a difference-in-differences design, we find that firms facing weaker property rights protection prior to the Law significantly increase their investment and investment efficiency after the Law. Cross-sectional analyses document evidence consistent with a decrease in firms’ perceived expropriation risk as the main mechanism underlying the Law’s effect. Finally, we show that the Law improves local economic outcomes and employment.

This paper argues that ffnancial lease, a dominant representation of shadow banking in China, plays a special role in improving the capital allocation efficiency. In a two-sector general equilibrium model with heterogeneous firm, information asymmetry and financial frictions, this paper shows that existence of finance lease market increases aggregate TFP by allowing low productivity SOE firms to lend out and allowing high productivity POE firms to leverage up. Due to the repossession advantage, financial leasing is a “good“ form of shadow banking that does not necessarily cause financial systemic risks.

Using administrative universal business registration data as well as primary offline and online surveys of small businesses (including unregistered self-employments) in China, we examine (i) whether digitization helps small and medium enterprises (SMEs) better cope with the COVID-19 pandemic, and (ii) whether the pandemic has spurred digital technology adoption. We document significant economic benefits of digitization in increasing SMEs' resilience against such a large shock, as seen through mitigated demand decline, sustainable cash flow, ability to quickly reopen, and positive outlook for growth. Post the January 2020 lockdown, firm entries exhibited a V-shaped pattern, with entries of e-commerce firms experiencing a less pronounced immediate drop and a quicker rebound. Moreover, the pandemic has accelerated the digital transformation of existing firms and the industry in multiple dimensions (e.g., altering operation scope to include e-commerce, allowing remote work, and adopting electronic information systems). The effect persists more than one year after reopening, and is more pronounced for certain sectors, firms in industrial clusters, and areas with more digital inclusion but less financial efficiency, constituting initial evidence for the long-term impact of the pandemic and the supposedly transitory mitigation policies.

This paper investigates the consequences of Chinese regulators deviating from a long-standing full bailout policy in addressing a city-level commercial bank’s distress. This event led to a persistent widening of credit spreads and a significant decline in funding ratios for negotiable certificates of deposit issued by small banks relative to large ones. Our empirical analysis pinpoints a novel contagion mechanism marked by diminished confidence in bank bailouts, which accounts for the subsequent collapse of several other small banks. However, the erosion of confidence in government guarantees enhances price efficiency and credit allocation while discouraging risk taking among small banks.

We examine communication of political connections in corporate China, and show that politically inclined positive words—words in connotation of political sentiment—serve as a distinct and effective signaling device for corporate political connections. Using a large sample of corporate news, we find that news’ political sentiment, instead of orthodox political measures such as occurrences of political nouns and political entities, reflects executives’ political connections for private enterprises, and is related to rent-seeking benefits in government subsidy, tax refund, financing constraints and political risk. Our results demonstrate that political sentiment is an effective way to decode subtle corporate political connections in modern China’s “Mind Politics” environment that infiltrates into private corporations.