完善上市公司预测性财务信息披露制度的建议

在市场经济高度发展的形势下,对外提供预测性财务信息日益成为企业对外提供财务报告的重要羞成部分。但预测性财务信息中还存在预测体系不健全,财务预测不规范、不完整等问题。政府应加强上市公司预测性财务信息披露的制度建设,提高公司内部预测性财务信息质量,发挥注册会计师在保障预测     

我国企业预测性财务信息披露:问题与对策

预测性财务信息是指企业对未来财务状况、经营成果、现金流量及其他方面的相关信息的披露.由于它的披露能直接影响着会计使用者的决策,并间接影响着企业的经济效益.本文就我国上市企业预测性财务信息披露存在的问题进行分析,在此基础上,提出若干治理对策和建议.     

提高预测性财务信息质量的途径

<正> 预测性财务信息是关于企业未来的财务状况、经营成果和现金流量等情况的信息,我国上市公司在招股说明书中披露的盈利预测信息就是其中的一种。去年7月14日,证监会对盈利预测产生重大偏差的11家上市公司进行了通报批评,这无疑把预测性财务信息披露的重要性又提到了一个新的     

检验上市公司披露信息的预测能力

上市公司会计信息披露质量的问题是信息披露关注的焦点,而预测性信息披露水平的高低直接关系到投资者等利益相关者的科学决策以及社会资源的有效配置。本文在分析预测性信息理论基础上,以投资者盈利预测模型为逻辑起点,设计检验预测性信息有效性的模型,借以为投资者等利益相关者评价上市公司信息披露的质量和投资行为提供一种较为科学、可行的方法。     

上市公司信息披露质量影响因素的实证分析——以深圳主板上市公司为例

本文以2013年深圳主板信息披露考评的480家上市公司作为研究对象,分析公司的基本因素、财务因素、治理因素和审计因素对信息披露质量的影响。首先做了信息披露质量与各个变量的相关分析,然后建立了多项有序logistic回归模型,得出结论:公司所属行业、公司规模、每股收益、是否ST公司、董事总规模及审计意见对上市公司信息披露考评结果有显著影响。     

企业加强财务信息质量研究

财务信息是企业组织按照财务会计核算理论和法定会计准则向会计信息使用者和相关利益关系人披露的标准财务会计信息。随着市场经济的发展,财务信息使用者对于财务信息质量要求日益提升。本文就影响财务信息质量的各因素进行了分析,并提出了加强财务信息质量的建议和措施。     

对我国上市公司预测性盈利信息披露有关问题的思考

预测性盈利信息对正确评价公司的潜在盈利能力非常重要,有助于投资者做出合理的经济决策,是非常有用的会计信息,因此,需要上市公司公开披露预测性盈利信息,以促进证券市场效率的提高。但作为一种未来导向型信息,其本身又具有较强的不确定性。本文从上市公司预测性盈利信息内涵和特征入手,分析了我国上市公司预测性盈利信息披露的现状和存在的主要问题,并对提高我国预测性盈利信息披露质量的提出了相关建议。     

内部控制信息披露影响因素分析研究

本文从国内外针对国有企业披露内部控制信息研究现状出发,指出了了企业内部控制信息披露的指导理论,进而深入剖析了其影响因素,分析结果表明影响企业发布内部控制信息的关键因素有两个,即企业业绩水平和财务质量水平。具体而言,企业业务水平越强、财务质量水平越高,则其在披露信息方面更接近于《指引》要求。此外,本文针对现状提出了一些有效的对策。     

论财务报告未来发展趋势

随着经济社会的迅速发展、财务环境的不断变化,财务报告必将随之出现新的发展趋势。在信息技术高速发展的推动下,交互式、实时财务报告体系必将建立,公允价值模式将得到更广泛的应用,财务报告不仅能够反映过去,还将披露预测性财务信息,非财务信息的披露将不断完善,而社会责任报告、全面收益表等新的报表形式亦将出现。     

我国物流业上市公司财务软信息披露之探究

现代物流业因为将传统运输方式和信息技术相结合,可以降低成本,提高效益。在我国,上市公司基本上都是各行各业的"领军人物",面对来势汹汹的国外企业,谁能在现代物流业的竞争中胜出呢?除了企业自身练好生产经营管理内功外,对外的财务信息(尤其是预测性的财务信息)披露也很重要。因为它是企业与外界互通的窗口。本文就我国物流业上市公司财务软信息披露存在的问题与对策加以研究,希望对完善我国物流业上市公司财务软信息披露有所启迪。     

浅析财务报告局限性及改进建议

现行的财务报告体系己滞后于经济发展的要求,不能全面客观地进行价值反映,在一定程度上误导了报表使用者的决策行为.应从这些局限性入手,改变财务报告的报告模式,构造多元化的财务报告体系,实时呈报财务报告;加强预测性信息的披露,增加现行财务报告对社会责任信息的披露;应重视对衍生金融工具所产生的收益和风险信息的披露;增加对人力资源的反映;提高财务报告的质量,加强注册会计师审计监督等,使之适应经济发展的要求.     

From spending to understanding: Analyzing customers by their spending behavior

In customer segmentation, a common strategy is to use individual differences as a predictor of future behavior. Recent advances in data management in large financial institutions give an unprecedented and potentially powerful source of data for identifying such differences. We show that spending data can substantially help target the direct marketing of financial products, and constitutes new information, not captured by demographics. In particular, a systematic combination of this independent source and more traditional measures can enhance the predictive power of marketing research and improve the relationship with customers as illustrated in a direct mailing selection method which substantially raises response rates.     

Adoption of Online Social Networks to Communicate with Financial Institutions

Spain's financial sector is not in a healthy state, and the problems that some financial institutions currently face perpetuate the widespread perception of risk across the entire sector. Moreover, the online social networks (OSN) that emerged a decade ago are suddenly at the very heart of digital society. In this study we develop a predictive model to determine that the motivational factors that influence Spanish users' intention to use OSN to communicate with financial institutions are quality management, availability of information, external conditions, trust, perceived compatibility, perceived usefulness, attitude, and intention. Data were collected from 335 Spanish OSN users through an online survey. The results suggest that quality management has a highly significant and positive effect on perceived usefulness and that perceived usefulness has a positive and significant effect on the intention to use social networks to communicate with financial institutions.     

Using Inductive Learning to Predict Bankruptcy

An emerging trend in organizational computing is using information technology to learn decision knowledge from enterprise data. The primary contribution of this study is the presentation of a sound theory and a comprehensive technique for learning the decision model for predicting bankruptcy. The theory is based on the information contained in cash flow components, which is the foundation of valuation theory, and an analytical system that measures the amount of uncertainty in the cash flow information. The approach links a tree-based inductive learning system that relies on the concept of entropy, with an information system based on the cash flow of a firm. A test of the cash flow approach involves the cash flow components for a sample of 99 failed and 99 non-failed companies. The structural instability of cash flow components generated by an inductive learning system is a serious issue for financial analysts. However, this shortcoming is overcome by using a jackknife procedure to develop a global tree that identifies the most important cash flow components. The final global tree found only 3 cash flow components were needed to classify correctly 89% of the companies as either failed or non-failed. Only a few early studies achieved a higher level of predictive accuracy. The 3 significant cash flow components were dividends, net investment, and net operating cash flow. Using the same data, a probit statistical technique generated a 67.5% predictive accuracy. In summary, the inductive learning results indicate that cash flow components are not only a natural tool for explaining the bankruptcy process, but they provide a high level of predictive accuracy.     

企业财务困境预警及对策研究

为了有效地预警企业财务困境,论文以156家上市公司为研究样本,运用逐步判别分析方法从33个财务比率指标中筛选出6个变量,构建了一个六变量财务困境判别模型。经检验,新建模型对企业财务困境的预测正确率达到93.59%,且具有一定的超前预测能力。企业可以应用该模型及早预警财务困境,及时采取措施防范和应对财务困境。     

Credit funding and banking fragility: A forecasting model for emerging economies

Our paper proposes an empirical model to forecast banking fragility episodes using information from the credit funding sources. We predict the probability of occurrence of such events 3 and 6 months ahead, employing a Bayesian Model Averaging on logistic regressions. We perform prediction exercises for nine emerging economies under a broad set of prior specifications, whose results are evaluated using predictive ability tests and the signaling analysis approach. Our findings indicate that the increasing use of wholesale funds provide signals of banking frailness. Moreover, pseudo out-of-sample predictions show that our warning tool is able to forecast financial fragility events.     

Equity market data, bank failures and market efficiency

The paper examines the informational content of market data for long-term horizons in models, which predict bank failure. Univariate results document patterns such as declining prices, negative returns, declining dividends, and rising return volatility, up to 4 years before failure. Multivariate analysis shows that market information improves the failure predictive content of traditional models, which are based on accounting data. Out-of-sample predictions show that the use of stock market data does improve the forecast of bank failure. Furthermore, the persistence of this contribution generally increases with greater distances from the date of failure documenting the forward-looking nature of financial markets.     

基于小波神经网络的上市公司财务风险预警研究

简述小波神经网络的基础理论和算法,建立财务风险预警指标,利用小波神经网自适应能力强、收敛速度快和预测精度高的特点对上市公司财务风险进行实证研究。应用结果表明,小波神经网络在财务风险预警中能够提高准确度,加快收敛速度,为财务风险预警提供了新的研究方法。     

基于电商大数据的农产品短期经营风险预测研究——以家庭经营梨果种植户为样本

农产品电商具有农业经营改善和农户福利提升的积极作用，在电商参与模式下识别和预测农产品短期经营风险有助于农户家庭主动管理和积极干预电商嵌入结构，也为区域电商布局优化提供理论依据和预警方案。文章以江苏省3755户家庭梨果种植户为样本，采集种植投入、电商交易和借贷保险等电子台账大数据，对比分析线性计量模型、一维非线性机器学习模型和二维深度学习模型对农产品短期经营风险结果和风险决策的预测能力。结果表明，电子台账大数据包含预测农产品短期经营风险的信息含量，基于特征灰度的二维卷积神经网络具有精准而稳定的预测能力，电商交易的预测信息含量高于种植投入和借贷保险且三者相互印证。在此基础上，从电商数据规范、台账软件升级、电商咨询建设和数字平台管理角度提出建议。     

Early‐Stage of Innovations: Selection System Criteria for Funding U.S. Biotech SMEs

The focus of this paper is small and medium‐sized enterprises (SMEs) operating in the U.S. biotechnology industry and how they compete for financial resources during the early stages of innovation development. We utilize selection system theory, which describes how selectors use reputation‐based information about selectees as decision factors when making investments. Our findings suggest that there are different predictive variables for SME categories and the types of investors attracted to these categories, which is consistent with selection system theory. We extend prior studies by providing context to early‐stage innovation investment funding within an environment characterized as having a long development cycle and representing high uncertainty.