试述我国企业法务制度的现实局限性和完善建议

企业发展过程中构建科学完善的法务制度对于企业和市场经济发展来说都十分必要。市场经济是法治经济,随着社会主义市场经济的发展,在激烈竞争中企业法务制度的重要性日趋显现。企业法务制度作为现代企业制度的有机组成部分,建立和完善现代企业的法务制度,妥善应对和有效化解企业法律风险,对企业依法经营管理、长远发展具有重要意义。     

GIS技术在环境影响评价中的应用策略

在当今社会经济的不断发展中,人们的环保意识也实现了不断提升,而伴随着科学技术的发展,各种先进技术也开始逐渐应用到了环境保护工作中。其中,GIS技术就是一项典型的环境影响评价技术。通过该技术的应用,可以让环境资源、生态资源、土地资源、林业资源以及农业资源等领域得到良好的影响评价,以此来保障资源与环境之间的可持续发展。基于此,论文对该技术在环境影响评价中的具体应用策略进行分析,希望可以为GIS技术在环境影响评价中的合理应用以及环境影响评价效果的提升提供足够动力。     

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。     

互联网金融视角下股权众筹融资的投资风险探析

股权众筹是公司通过出让股份获得资金的一种融资方式,它的出现很大程度上解决了中小型企业融资难、融资贵的问题。但是近年来股权众筹的发展速度逐渐放缓,同时模式本身也暴露出了较高的投资风险。针对这些投资风险,论文从退出机制、外部审计和监管三个层面给出了相应的措施建议。     

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.