资本市场开放与公司内部控制质量——基于“沪港通”的证据

以中国“沪港通”交易制度的实施为政策背景,采用多时点双重差分模型,考察资本市场开放对标的公司内部控制质量的影响,研究发现,“沪港通”交易制度的实施显著提升了公司的内部控制质量,在控制其他因素并经过安慰剂检验、去除A+H股影响、改变周期范围等稳健性检验后,结论依然成立。机制检验表明,“沪港通”主要通过内部和外部两种机制对公司内部控制质量产生影响,其中,内部机制是对股价波动性风险控制,外部机制是审计师声誉风险与政府部门监管。     

外资进入对本土企业风险承担的影响：资本逐利还是风险规避

外资自由化作为我国资本市场开放的重要内容,在促进境内外资本要素互联互通的同时,也深刻影响着微观企业行为。以我国2007—2017年A股上市公司作为研究样本,实证检验外资进入对我国本土企业风险承担的影响。研究发现：外资进入能够显著提升企业的风险承担水平。进一步地,外资进入对企业风险承担的促进效应在融资约束严重、股权集中度更高和市场化进程缓慢地区的企业中更加显著。此外,在不同资本市场开放水平,外资进入对企业风险承担的作用存在明显差异。     

我国商业银行中小企业信贷风险管理研究

在经济新常态下,我国商业银行中小企业信贷业务面临着机遇和挑战。中小企业发展迅速,在推动就业、增加税收等方面起着重要作用。但其自身稳定性弱、风险大,使得商业银行对其谨慎放贷,中小企业融资难成为制约其发展的一大重要因素。论文通过对我国商业银行中小企业信贷现状及其风险形成进行分析,对商业银行中小企业信贷风险管理提出相应策略和管理措施。     

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.     

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.     

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