风险预控管理体系在煤矿安全管理中的作用

当前我国的煤矿行业迈入了转型发展的重要时期,国家对煤矿行业的安全生产管理提出了越来越高的要求,安全生产标准化成为煤矿企业的重点工作。煤矿生产作业的特殊性决定了在各项生产作业中存在各种安全风险,作业流程的繁多性、要素的多样性加大了煤矿安全管理的难度。煤矿行业现代化的发展趋势下,风险预控管理体系的构建为煤矿安全生产目标的实现提供了保障。     

基于GONE理论的LK公司舞弊动因分析与审计风险防范

继浑水发布做空报告后,LK公司于2020年4月自曝2019年年度审计期间发现相关业务数据巨额造假,引起社会广泛关注.企业战略,尤其是商业模式,已经成为管理层产生财务报表层次重大错报风险的重要诱因.文章以GONE理论作为工具来识别和预判财务舞弊的倾向和风险,解释了企业管理层进行财务舞弊的动因.做空机构基于企业战略层面的潜在风险,有针对性地采取手段并取得了有力证据.注册会计师行业应思考如何发挥职业判断在防范审计风险中的作用,尤其要克服认知偏差对保持职业怀疑的影响,同时应借鉴并改进审计程序,以有效降低审计风险.     

Are gold,USD, and Bitcoin hedge or safe haven against stock? The implication for risk management

This study investigates whether gold, USD, and Bitcoin are hedge and safe haven assets against stock and if they are useful in diversifying downside risk for international stock markets. We propose a combined GO-GARCH-EVT-copula approach to examine the hedge and safe haven properties of gold, USD, and Bitcoin. We then examine the attractiveness of these assets in reducing stock portfolio risk by using downside risk measures estimated by the proposed approach and other competing models. We also evaluate the relative performance of the proposed model in reducing downside risk with the competing models. The findings of the study indicate that the USD is the most valuable hedge and safe haven asset closely followed by gold, while Bitcoin is the least valuable. It is also observed that the proposed combined approach performs best in reducing the portfolio downside risk. The findings of this study are of significance for portfolio managers and individual investors who wish to protect the portfolio value during market turmoil.     

A Bayesian approach for more reliable tail risk forecasts

This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.     

Labor unemployment risk and firm risk

This paper examines the effect of labor unemployment risk on firm risk. Using unemployment insurance benefits as a proxy for unemployment risk, we find an economically significant positive relation between unemployment risk and firm risk. This positive relation is more pronounced for firms that are more labor-intensive, have a higher layoff propensity and are more financially constrained. While existing literature that employs corporate policy measures such as debt and cash holdings suggests an opposite relationship, our paper presents evidence that the effects stemming from earnings management, earnings quality and reporting quality appear to dominate.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

环境不确定性与企业党员高管的创新“稳定器”效应

本文选取2010—2019年A股上市公司作为研究样本,研究高管党员身份在企业创新面临环境不确定带来冲击时的“稳定器”效应及其内在机制。研究发现：环境不确定性的提升抑制了企业创新,高管党员身份能通过降低研发支出挤出程度和优化融资环境两个渠道有效缓解环境不确定性的创新抑制效应。党员高管的创新“稳定器”效应更多地体现在环境不确定性增加的阶段,在平稳发展时期,应平衡好党组织参与企业治理与企业创新发展自主权的关系。全面从严治党战略的实施使党员高管决策风格更加稳健、保守,降低了企业风险承担,同时强化了面临环境不确定性时高管党员身份的“创新稳定器”作用。在当前企业创新面临高环境不确定性的大背景下,本文研究一方面丰富了党组织参与创新治理的理论依据,另一方面为高不确定性背景下优化企业创新环境提供了可实践路径。     

What lies beneath—Negative interest rates and bank lending

We study the transmission of negative interest rates to bank lending around an unexpected policy rate cut into deep negative territory by the Swiss National Bank (−0.75%). We exploit a rich data set on transaction-level corporate loans matched with bank balance sheet data. We find that banks more affected by negative interest rates offer looser lending terms and lend more than other banks. This result is consistent with the risk-taking channel, where a lower policy rate spurs bank risk-taking to maintain profits. The result implies that, even in such deep negative territory, the reversal rate has not yet been hit.     

Network based evidence of the financial impact of Covid-19 pandemic

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.     

Noisy chaotic dynamics in commodity markets

The nonlinear testing and modeling of economic and financial time series has increased substantially in recent years, enabling us to better understand market and price behavior, risk and the formation of expectations. Such tests have also been applied to commodity market behavior, providing evidence of heteroskedasticity, chaos, long memory, cyclicity, etc. The present evaluation of futures price behavior confirms that the resulting price movements can be random, suggesting noisy chaotic behavior. Prices could thus follow a mean process that is dynamic chaotic, coupled with a variance that follows a GARCH process. Our conclusion is that models of this type could be constructed to assist in forecasting prices in the short run but not over long run time periods.First version received: June 2001/Final version received: March 2003