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.     

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.     

Change-point detection for expected shortfall in time series

Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.     

Managing leverage of infrastructure projects: Aggregate and sectoral risk effect

Funding for infrastructure projects recently shows that debt has a portion more than equity, triggering public debates. Therefore, leverage as an instrument to measure the ability and willingness of project sponsors to fund becomes the utmost importance to discuss. Relating to leverage, risk and government participation are two main factors that can explain the choice of funding decisions by the project sponsors. For this reason, this study would analyze the effect of risk and government participation on leverage through the two main sectors of infrastructure projects, namely the transportation sector and the energy sector, and derivating risk to political risk and financial risk. The objects of research were 976 infrastructure projects listed in the Asian Development Bank during 2007−2016. We use censored regression to examine the model by infrastructure sectors, both as individual and through interaction effects. The analysis showed that overall, leverage of infrastructure projects was rather influenced by financial risk than by political risk. However, the leverage of infrastructure projects in the transportation sector was more vulnerable to risk than that in other sectors.     

Larger,counter-intuitive and lasting – The PSM role in responding to the COVID-19 pandemic,exploring opportunities for theoretical and actionable advances

PSM has played an important role in the initial mitigation of risks caused by the COVID-19 pandemic. We explore the nature and scope of this role and develop a roadmap for PSM contributions towards greater supply chain resilience. We find that the role of PSM is (1) multidimensional; responding to supply risks but also to demand and logistics risks, (2) counter to game theory; collaboration increased instead of decrease, (3) multi-stage; beyond the initial response the hardest change efforts are still ahead. The pandemic is accelerating the journey towards future-proof PSM but not necessarily revolutionizing the future of PSM.     

网络预算管理

本文介绍了网络预算管理是如何有效地克服传统预算方法的缺点,通过有效的控制去解决企业预算管理工作中的实际问题,指出了该预算方法能大大提高工作效率和管理效率。     

Derivative activities and the risk of international banks: A market index and VaR approach

The results of a comparison of international banks using a three-factor multi-index model and a modified value-at-risk (VaR) analysis indicate that the use of options increases the interest rate beta for all banks, while both interest rate and currency swaps generally reduce risk. The results are the strongest and the most consistent for U.S. dealer banks, followed by European banks, and then Japanese banks. Furthermore, the evidence suggests that the VaR approach to risk management can effectively be used by both domestic as well as international banks, although the results appear to be somewhat sensitive to the regulatory environment in which the bank operates.     

如何提高我国金融业的国际竞争力

针对经济全球化和我国金融业的对外开放，文章从金融创新、金融风险、金融体制改革等方面论述了我国金融业面对挑战应采取的措施。