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.

Unlike equity analysis where analysts follow a small group of firms and exercise discretion in incorporating firm-specific knowledge, ESG ratings that intend to capture firm ESG risk are produced through a largely unified model that incorporates a set of common disclosures decided by each rater. Against this backdrop, we assess the ability of local and global ESG ratings in capturing covered firms’ ESG risk in China. We use firm-level negative ESG incidents that occur within a year of ESG ratings’ release as a proxy for raters’ ESG risk assessment and examine whether local and foreign ratings have differential predictive ability. We find that local ratings better capture ESG risk that relates to social and governance issues on corruption, employment conditions, and regulatory violations, which often require the local context to incorporate. The outperformance is also salient among firms that rely on relationship-based transactions and political connections. Our results suggest that the local rater uses local knowledge to inform its model, which makes the ratings more relevant to ESG risk.

Evidence from the U.S. and Europe suggests that current risk assessment tools used by researchers and financial professionals to determine individuals’ risk tolerance and provide suitable portfolio recommendations may be flawed due to “mis”perceptions of risk. Limited research has examined the reliability of these tools as measures of relative risk tolerance for households in emerging economies like China. This study develops a multidimensional index of risk tolerance specifically tailored for Chinese households using a psychometric approach. The effectiveness of this multidimensional index in predicting individuals’ financial decisions is tested and compared to traditional unidimensional measures of risk tolerance commonly used in developed countries. The findings indicate that multidimensional measures are more consistent and significant predictors of Chinese households’ investment decisions. Additionally, the study uncovers evidence that cultural differences, related to market expectations and social networks, which are often overlooked in U.S. and European models, play a crucial role in shaping individuals' risk perceptions and investment choices in China. Robustness checks were conducted to account for potential endogeneity between risk tolerance and investment decisions. The findings provide valuable insights for researchers and financial professionals seeking to develop more accurate risk assessment tools that capture risk attitudes and perceptions in China and other developing countries. By adopting a multidimensional approach that accounts for cultural and psychosocial factors, these improved tools can enhance the precision of risk evaluation and facilitate more appropriate investment recommendations.

Establishing a comprehensive risk management system and establishing a sound and effective internal control mechanism are important conditions for the survival and healthy development of enterprises. Enterprises should continue to innovate their internal control mechanisms, continuously improve and strengthen their internal control and risk management functions, continuously improve their internal control effectiveness, and assist in high-quality development of enterprises. The article elaborates that the digital transformation of auditing helps to achieve internal audit goals and improve the effectiveness of internal control; Blockchain technology, due to its excellent characteristics of distributed decentralization, authenticity, transparency, and tamper resistance, will help transform the methods of financial and accounting business and audit supervision; The application of blockchain technology in the field of auditing will inevitably lead to the reshaping of audit work models; Blockchain technology will become a trigger point for the audit revolution, comprehensively empowering audit and risk management work. The article introduces JD's blockchain ABS standardization solution, while Tencent and TCL Group's industrial blockchain solve the reliable transmission of trust and value at low cost, confirming that the close combination of blockchain technology with enterprise finance and auditing can assist internal control and play an important risk prevention and control role in enterprise operations. The "blockchain+audit" can achieve online real-time audit, risk assessment and warning, and data analysis becomes the core of audit content; Blockchain+auditing will become an important tool for internal control and risk management in enterprises.

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.

Over the last decade, China’s P2P lending industry has been seen as an important credit source but it has recently suffered from a wave of bankruptcies. Using 126,090 P2P loan deals from RenRen Dai, one of the biggest online P2P websites in China, this paper attempts to predict credit default probabilities for P2P lending by implementing machine-learning techniques. More specifically, thisstudy proposes a stacking ensemble machine-learning model to assess credit default risk for P2P lending platforms. A Max-Relevance and Min-Redundancy (MRMR) method is used for feature selection and then irrelevant features are eliminated by using k-means clustering method. Finally, the stacking ensemble model is performed to produce accurate and stable predictions in the feature subset. Experimental results show that stacking ensemble model yields high performance, not only in prediction accuracy but also in precision and recall. In comparison to single classifiers, the stacking ensemble machine-learning model has a minimum error rate and provides more accurate credit default risk prediction. The results also confirm the efficiency of the proposed stacking ensemble model through the area under the ROC curve.

We develop a portfolio-choice model to investigate how regulatory reforms influence the risk-taking behavior of financial institutions with different capital adequacy levels. The model predicts that either all firms reduce their risk-taking, or there exists a capital-adequacy threshold below which risk-taking increases as regulation becomes more stringent. The Chinese insurance solvency regulatory reform provides a unique natural experiment to test our theory. In 2015, each insurer reported two solvency ratios under the original and the new regulatory systems. The difference between them produces an exogenous and insurer-specific measure of the regulatory pressure shock. Consistent with our theoretical predictions, we find that increasing regulatory pressure induces greater risk-taking for less capital-adequate insurers, of which the regulator should want to reduce risk-taking mostly. We show that increasing the penalties of insolvency, increasing the risk sensitivity of capital requirements, and reinforcing the qualitative risk assessment are effective policy remedies for this backfiring problem.