管理层权力制衡强度、债务约束与企业创新投资

企业创新是国家经济可持续增长的关键，受到管理层意愿的影响，因而需要对内部经营者的权力进行制衡。以2010-2018年我国A股上市公司为研究样本，实证检验管理层权力制衡强度对企业创新投资的影响，以及不同债务约束情境下高商业信用配置、高负债水平的调节效应，此外，还考察了产权性质的差异化影响。研究表明，管理层权力制衡强度越大，企业创新投资水平越高；高商业信用强化了该促进作用，而高负债水平弱化了该促进作用。进一步研究发现，管理层权力制衡强度与企业创新投资的关系在民企中更显著；国企能够更好地获得和运用商业信用，使其高商业信用对该关系的强化效应更显著；民企具有更强的债务约束，其高负债水平对该关系的弱化效应更明显。     

Risk aversion,prudence and temperance: An experiment in gain and loss

We characterize the individual's attitude towards risk, prudence and temperance in the gain and loss domains. We analyze the links between the three features of preferences for a given domain and between domains for each feature of preferences. Consequently, the reflection effect, the mixed risk aversion and the risk apportionment, are key concepts of our study. We also display some determinants for risk aversion, prudence and temperance in each domain. To do this, we conducted a lab experiment with students eliciting risk aversion, prudence and temperance in the two domains, and collected information about each subject's characteristics.     

Cluster Analysis for mixed data: An application to credit risk evaluation

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.     

Non-rating revenue and conflicts of interest

Rating agencies produce ratings used by investors, but obtain most of their revenue from issuers, leading to a conflict of interest. We employ a unique data set on the use of non-rating services, and the associated payments, in India, to test if this conflict affects ratings quality. Agencies rate issuers that pay them for non-rating services higher (than agencies not hired for such services). Such issuers also have higher default rates. Both effects are increasing in the amount paid. These results suggest that issuers which hire agencies for non-rating services receive higher ratings despite having higher default risk.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Hedging and temporal permit issuances in cap-and-trade programs: The Market Stability Reserve under risk aversion

Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.     

政策不确定性、财政扩张与区域商业银行风险——基于中国地方官员变更和银行业的证据

文章利用2007-2017年我国93家区域商业银行的面板数据，并结合省级宏观经济数据和地方官员变更数据，实证考察了中国地方官员变更引起的经济政策不确定性对区域商业银行风险的重要影响。研究结果表明：（1）地方官员变更引起的政策不确定性增大了区域商业银行风险；（2）地方官员变更引起的政策不确定性，通过提高银行的资产收益率降低了商业银行风险；而不确定性时期的财政扩张，通过降低银行资产收益率增大了商业银行风险；并且财政扩张的负面影响大于政策不确定性的正面影响；（3）各省的市委书记发生职位更替引起的政策不确定性对区域商业银行风险的影响更大。文章研究为新时代背景下的金融供给侧结构性改革、政府宏观经济政策的制定以及财政体制与金融体制之间的联系提供参考。     

中国股市行业风险与宏观经济之间的风险传导机制

本文采用行业收益率溢出指数度量股市行业风险,并进一步研究中国股市行业风险与宏观经济的相互影响,同时引入股息率和利率两个中介渠道深入挖掘其传导机制。我们运用GARCH-in-Mean模型对股市行业风险和宏观经济变量之间的一阶矩和二阶矩相互关系同时进行分析,结果发现,股市行业风险和宏观经济变量之间水平值和波动率都存在双向影响,对外溢出效应较大的行业起主导作用。此外,股市行业风险对宏观经济变量的影响方面,股息率和利率均起到中介渠道作用;宏观经济变量对股市行业风险的影响方面,只是利率起到中介渠道作用。股市行业风险与宏观经济的传导效应在不同时期差异显著。本文研究结论有助于深刻理解金融与实体经济之间的风险传导机制,对防范系统性风险、防止金融和实体经济“风险共振”以及提升金融服务实体经济能力等具有参考意义。