上市公司盈余管理与会计造假的十大区别

上市公司会计信息失真是当前我国会计实务界和理论界面临的严重问题,这既关系到我国会计界的声誉和形象,又影响到我国资本市场的健康发展和完善。一般来说,会计信息失真可分为两类:一是会计造假,即违反会计制度、会计准则及其他会计法规,操纵会计数据,如虚构、制造经济交易,对真实经济业务进行歪曲处理等;二是盈余管理,即利用会计的灵活性使会计系统提供的信息脱离经济事实,如运用会计政策选择和会计估计管理会计数据。上市公司盈余管理和会计造假均改变了会计信息,违反了中立性原则,影响了会计信息的真实性和可靠性。但是在实务中,会计造假所引起的会计信息失真很容易判断、识别,而盈余管理所引起的会计信息失真则难以识别。本文试对二者进行辨析,指出他们的不同之处,并提出相应的治理对策。     

会计信息失真的原因及治理

会计信息是经济管理中的一种重要信息.当前,世界经济一体化增强,经济高速发展,要求会计信息的必须是真实可靠的.但是在日常的生活中,会计信息不真实不可靠,严重失真的情况时有发生.本文在结合我国会计信息失真现状的基础上,对会计信息失真的原因进行了分析.     

合法会计信息失真的成因和对策

会计信息失真包括合法会计信息失真和非法会计信息失真.目前会计界对非法会计信息失真讨论的比较多,笔者认为合法会计信息失真比非法会计信息失真现象更普遍存在,是一个不容忽视的方面.因此,笔者试从分析导致合法会计信息失真的成因入手,提出了治理合法会计信息失真的几点对策。     

企业会计信息失真表现及措施探析

会计管理不可或缺的基础便是会计信息,一旦会计信息失真,就会造成会计管理工作失去原有的作用及意义.在我国目前市场经济条件下,许多企业绞尽脑汁的想使企业实现最大化的经济效益,以致不惜利用会计造假这一恶劣手段,但这种行为将会直接造成会计信息失去其原本的真实性.面对日益严重的会计信息失真问题,必须尽快的采取行之有效的应对策略,以便全面的保证会计信息的质量,维持正常的市场经济秩序.本文针对我国会计管理工作的现状,详细的对企业会计信息失真的表现进行了分析,并且深入的探讨了解决会计信息失真问题的有效措施,希望能够对对我国会计管理工作产生积极的影响.     

上市公司偏好、政府策略选择与会计信息失真均衡

上市公司会计信息失真可分为治理性失真、竞争性失真和干预性失真三大类.模型均衡表明,寡占博弈下的竞争性失真由真假会计信息市场信息产品边际净收益决定,与干预性、企业性因素无关,而干预性失真则由政府干预策略选择、干预强度、企业偏好和会计信息产品边际净收益等企业性、市场性和干预性三大因素决定.三大因素不对称地影响上市公司和市场的会计信息均衡,并通过改变干预性失真均衡而改变上市公司和市场会计信息绝对和相对均衡,以调节上市公司和市场的会计信息质量.实际市场信息失真主要是竞争性失真和干预性失真叠加的结果.     

会计信息失真现象研究

会计信息失真问题严重影响了会计信息的质量.文章主要从会计工作人员的角度对会计信息失真进行阐述.     

会计信息失真的原因及对策研究

会计信息是决策者进行决策的重要参考依据,对企业经营管理具有重要作用.但是由于种种原因,会计信息出现失真的问题,对社会经济的健康运行带来了不良的影响.文章将针对会计信息失真的问题,探究会计信息失真的原因,并提出建设性的对策.     

商业银行财务会计目标与会计信息失真关系研究

随着我国金融市场体系的不断成熟,商业银行的地位愈加突出.而且由于我国金融监管体系的不断完善,对商业银行会计信息失真问题尤为重视,而商业银行自身由于事先财务会计目标的需要,同样对会计信息失真十分重视.本文就商业银行会计目标与会计信息失真的关系进行探究,同时对商业银行优化财务会计目标及会计信息失真问题提出优化途径.     

论新会计准则下会计信息失真与商业银行的信贷风险防范

会计信息失真是一个国际性、历史性的问题,也是我国经济生活中较为普遍的现象和十分突出的问题,它直接动摇了人们对会计信息的信任,干扰了正常的经济秩序,导致国家资财的流失,特别是给商业银行的信贷决策造成重大影响。本文就会计信息失真的动因进行分析,并且结合其表现对商业银行信贷风险的影响,来探讨在新的会计准则体系下,商业银行应该如何识别企业会计信息的失真表象、形成的机理,从而作好有效的防范措施。     

部队会计信息失真的原因与对策

近年来我国部队的会计失真现象逐渐增加,这对于部队的财务管理工作有着十分不利的影响.了解部队会计信息失真的原因对于部队会计工作的进一步发展有着十分重要的意义.文章主要是对当前部队会计信息失真的原因进行分析,并就如何改善部队会计信息失真的现状提出合理的对策.     

Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio

We study the impact of machine learning (ML) models for credit default prediction in the calculation of regulatory capital by financial institutions. We do so by using a unique and anonymized database from a major Spanish bank. We first compare the statistical performance of five models based on supervised learning like Logistic Lasso, Trees (CART), Random Forest, XGBoost and Deep Learning, with a well-known model like Logit. We measure the statistical performance through different metrics, and for different sample sizes and features available. We find that ML models outperform, even when relatively low amount of data is used. We then translate this statistical performance into economic impact by estimating the savings in capital when using an advanced ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Logistic Lasso could yield savings from 12.4% to 17% in terms of regulatory capital requirements.     

A PSYCHOLOGICAL APPROACH TO MICROFINANCE CREDIT SCORING VIA A CLASSIFICATION AND REGRESSION TREE

Microfinance institutions' (MFIs') peculiar lending methodology is characterized by an unchallenged decision‐making predominance from the part of loan officers. Indeed, the latter are in charge of providing a great deal of diagnostic information regarding the entrepreneur's psychological traits likely to help them run a business. This paper constitutes an initial attempt towards exploring the role of borrowers' psychological traits in predicting future default occurrences. It builds on a nonparametric credit scoring model, based on a decision tree, including borrowers' quantitative behavioural traits as input for the final scoring model. On applying data collected from a Tunisian microfinance bank, the major depicted result lies in the fact that borrowers' psychological traits constitute a major information source in predicting their creditworthiness. Actually, the variables deployed have helped reduce the proportion of bad loans classified as good loans by 3.125%, which leads to a decrease in MFIs' losses by 4.8%. In addition, the results indicate that the scoring model based on a classification and regression tree (CART) outperforms the classic techniques. Actually, implementing this CART model might well help MFIs reduce misclassification costs by 6.8% and 13.5% in comparison with the discriminant analysis and logistic regression models respectively. Our conceived model, we consider, would be of great practical implication for microfinance and may provide a means for securing competitive advantage over other MFIs that fail to implement such a methodology. Copyright © 2014 John Wiley & Sons, Ltd.     

商业银行信用风险评估的生存分析模型及实证研究

企业发生财务危机,不能归还到期贷款是商业银行信贷资产的主要风险来源,商业银行如何构建恰当的信用风险评估模型来预测企业的财务危机,从而避免这类信用风险的出现就显得尤为重要。本文以我国上市公司为研究对象,结合杜邦分析法建立了基于生存分析的信用风险评估模型,模型对于随机选取的预测样本,其提前1年、2年和3年的预测准确率分别达到86%、72%和68%。通过与Altman模型、Ohlson模型预测结果的比较和鲁棒性检验的结果发现,该模型同时具有可以使用时间序列、无需样本配对、中远期预测能力强和高鲁棒性的特点,这些特点特别对于商业银行中长期信贷风险管理具有较高的应用价值。     

互联网金融资产的多目标投资组合研究

互联网金融的快速发展和不断创新,正在悄然改变着公众的投资理财行为。本文在分析互联网金融创新下公众流动性偏好、投资行为变化与资产选择的基础上,构建基于CRRA(常数相对风险厌恶)期末财富期望效用最大化和VaR最小化的多目标投资组合模型。同时引入多目标优化的NSGA-Ⅱ遗传算法,并选择实际数据对模型进行求解,得出最优的互联网金融资产组合。研究表明:(1)互联网金融给传统金融业带来冲击的同时,也改变了人们的流动性偏好、投资行为和资产组合选择。(2)互联网金融在一定程度上调和了金融资产“流动性、收益性和安全性”之间的矛盾,并兼顾了“三性”的相对统一。(3)模型求解结果显示,投资者对互联网金融资产的投资组合为低风险类资产60%左右、高风险类资产40%左右。     

金融发展对跨境人民币结算的空间溢出效应及异质性研究

基于2011-2019年我国30个省（区、市）平衡面板数据研究跨境人民币结算的空间集聚特征,使用空间计量模型实证分析金融发展对跨境人民币结算的空间溢出效应及异质性。发现省际跨境人民币结算呈现高-高、低-低的聚集趋势。通过构建多维度金融发展指标检验发现,金融发展规模、金融集聚、金融开放度均能有效推动跨境人民币结算,并呈现显著的正向溢出效应,金融开放度的影响效应更为明显。金融发展对跨境人民币结算在时间和空间上存在显著的异质性,正向溢出效应从2015年之后转变为负向溢出效应,通过更换权重矩阵发现结论依旧成立。     

Household Wealth and Macroeconomic Activity: 2008–2013

This paper provides estimates of the effects of the fall in financial and housing wealth in 2008–09 on overall macroeconomic activity. When the wealth losses are run through a structural macroeconometric model, it is estimated that the fall in wealth contributed about 2.1 percentage points to the rise in the unemployment rate in 2009 and about 3.3 points in 2010. The contribution to the fall in real GDP was 4.5% and 5.4% in the 2 years. These estimates account for most—but not all—of the recessionary increase in unemployment. The remaining increase in unemployment may have resulted more directly from financial stresses, but little evidence is found for this in this study.     

The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR

We study the changing international transmission of financial shocks over the period 1971–2012. Global financial shocks are measured as unexpected changes of a U.S. financial conditions index (FCI), developed by Hatzius et al. (2010). We model the FCI jointly with a large international data set through a time‐varying parameter factor‐augmented VAR and find that financial shocks have a considerable impact on growth in the nine countries considered. Moreover, financial shocks during the global financial crisis are found to be large by historical standards. They explain approximately 20% of GDP growth variation on average over 2008–9, compared to an average of 5% prior to the crisis.     

Financial hedging with derivatives and its impact on the Colombian market value for listed companies

The financial theory (Modigliani & Miller, 1958) presents risk management as a matter without importance in companies, given that the shareholders themselves managed their hedges, diversifying their portfolios. However, subsequent studies dispute said premise and present evidence that business financial hedging improves performance and increases the value of the same (Ahmed, Azevedo, & Guney, 2014; Allayannis & Weston, 2001; Kapitsinas, 2008). The efficient market risk management is supported in the financial derivatives, and demands strategic and efficient administrators in hedges that add value, especially in the face of clashes and macroeconomic and financial imbalances. The empirical evidence analyzes the behavior of the Q-Tobin as an indicator of the effect of the hedge strategies for the exchange rate associated to the market value. The aim of this work is to find evidence in Colombia on the effect of the use of derivatives in the market value of the company. Its added value lies in the analysis that is done by economic sectors, identified by ISIC codes and grouped into five (5) macro sectors (Agriculture and livestock, Commercial, Industrial or Manufacture, Services, and Construction). The methodology used includes the estimation of several regression models in data panels, using a Pooled regression model with fixed and random effect estimators through the maximum likelihood estimator. In general, a statistical and financially significant premium for hedges was found for companies exposed to exchange rate risks that use derivatives of a 6.3% average on the market value. Additionally, mixed results were found in relation to the variables analyzed in the model.     

Precautionary Savings with Risky Assets: When Cash Is Not Cash

U.S. industrial firms invest heavily in noncash, risky financial assets such as corporate debt, equity, and mortgage‐backed securities. Risky assets represent 40% of firms’ financial portfolios, or 6% of total book assets. We present a formal model to assess the optimality of this behavior. Consistent with the model, risky assets are concentrated in financially unconstrained firms holding large financial portfolios, are held by poorly governed firms, and are discounted by 13% to 22% compared to safe assets. We conclude that this activity represents an unregulated asset management industry of more than $1.5 trillion, questioning the traditional boundaries of nonfinancial firms.     

Effects of culture and financial literacy among Chinese-Americans on participating in financial services

The main contribution of this study is the theoretical development of the moderating effect of cultural-related variables such as generational status and gender in predicting attitudes toward paid professional service. Our analysis establishes that the cultural-related variables significantly affect the Chinese-American respondents’ attitude toward paid professional services. Our findings also highlight the substantial role of cultural background in determining financial literacy. We empirically examined the influence of financial literacy, risk attitude, gender role, and generational status of Chinese-American consumers on their adoption of paid financial services. Results reveal that Chinese-Americans with a low level of financial literacy reported a more positive attitude toward paid professional financial advisors than those who were more financially knowledgeable. In addition, we found that respondents who were risk-seeking reported a more positive attitude toward paid professional financial advisors than those who were risk-averse. In our conceptual model, we identified two particular moderators of interest: generation status and gender. We also found an interaction between Chinese-Americans’ level of financial literacy and their generational status. Moreover, we discovered that gender moderates the effect of consumers’ attitude toward paid professional financial advisors. These findings offer practical implication and guidance for financial service providers when they market and deliver services to this particular ethnic group and encourages researchers to treat immigrant diaspora as not being as monolithic as assumed in prior research.