以杜邦分析法为主线进行公司财务分析——以华新水泥公司为例

杜邦分析法是以净资产收益率为核心指标,综合评价企业经营绩效的一种财务分析方法,具有综合性、可比性等优点,为很多企业管理者和投资者所常用。但这种方法也存在局限性,随着社会经济的发展,更加需要注意扬长避短。本文以华新水泥公司为例,阐述如何以杜邦分析法为主线进行财务分析,并通过历史财务数据发掘企业内在价值预测未来。     

营运资本管理效率与盈利能力相关性研究——基于我国机械设备行业上市公司的经验证据

文章在国内外研究成果的基础上,选取我国机械设备行业上市公司2006—2015年的财务数据为研究样本,以净资产收益率作为公司盈利能力的衡量指标,采用现金周转期及其三个组成部分来综合衡量营运资本管理效率,并以资本结构、公司规模、企业成长性作为控制变量,对营运资本管理效率与公司盈利能力的相关性进行了实证研究。通过研究发现,应收账款周转期、存货周转期、应付账款周转期与盈利能力负相关,现金周转期与盈利能力负相关。最后针对实证分析结果给出了建议。     

对我国无担保企业债的违约风险因素研究

文章从信用违约风险的角度对无担保企业债收益率进行分析,通过构造度量经济体信用风险水平的CVI指标和选取适当的宏观经济变量指标(如固定资产投资同比增速等),运用"回归和时间序列ARMA组合"模型对我国无担保企业债的收益率建模,得到了统计上显著并且经济意义合理的结果。该模型有较高的数据拟合和预测能力,可以用于预测无担保企业债未来的收益率水平,对无担保企业债的投资决策具有一定参考意义。     

分类方法在财务危机规律发现中的应用

企业财务预警是以财务信息数据为基础,以财务指标体系为中心,通过对财务指标的综合分析、预测,及时反映企业经营情况和财务状况的变化,并对企业各环节发生或将可能发生的经营风险或财务风险发出预警信号,为管理当局提供决策依据。目前的财务预警主要从静态和动态两个方面来研究,静态研究是指选用财务指标对历史数据构建模型,预测未来的财务状况;动态研究除了预测之外还要能够进行反馈和调整。无论是静态还是动态研究,主要都是运用统计、人工智能的方法选择指标建立预警模型后,结合专业知识,对上市公司财务数据进行定量与定性相结合的财务预…     

基于微笑因子的创业板公司价值驱动因素剖析

基于我国创业板上市公司财务数据,首次从盈利能力、创新能力和营销能力三个维度构造综合性指标——微笑(Smile)因子,研究其对股票价格及收益率的解释能力.在Smile因子有效的基础上,运用Fama-French排序分组的方法构建SMC因子,用SMC因子替换原始三因素中的账面市值比因子,验证SMC因子在创业板市场的适用性.用SMC因子构建价值投资组合,比较SMC价值投资组合的累计收益与市场收益.结果表明,Smile因子对创业板市场股票价格及收益率有显著解释力,对未来有着良好的预测能力.另外,基于SMC改进后的三因素模型在创业板市场更加有效,市值因子对小规模公司的股票收益率影响更大且SMC价值投资组合收益明显高于市场收益.本研究为价值投资在我国创业板市场的适用性提供了有力的证据.     

收益率平价在货币危机预测中的应用

在本文中，我们首次提出了用收益率差作为领先指标来预测由危机的发生。本文从理论上证明了过去的实际收益率中包含预期收益率信息，从而从理论上解决了用过去实际的收益率差来预测货币危机的可行性问题。对19个国家25年（1975－2000）月度数据的拟合效果表明，经汇率调整后对美国股市收益差有着较好的预测结果，同时我们的结果也表明收益率预测指标对2001年7月爆发的阿根廷货币危机有较好的样本外测效果。     

供给侧结构性改革实施前后我国钢铁行业上市公司财务状况比较分析

论文选用我国22家钢铁行业上市公司供2012-2017年发布的历史财务数据作为数据分析来源。通过盈利能力、营运能力、偿债能力和发展能力系列指标进行数据分析,对比分析供给侧结构性改革前后的财务状况,通过系列财务指标分析发现,相对于供给侧结构性改革前,以净资产收益率、总资产收益率、销售毛利率和每股收益衡量的盈利能力得到大幅度提升,总资产周转率和流动资产周转率指标也到到提高,企业销售收入和净资产增长幅度较大,以资产负债率衡量的企业杠杆水平得到较大幅度下降。最后提出钢铁行业上市公司未来持续健康发展的对策建议。     

总体情况分析

本次研究按照中国证监会公布的行业分类标准进行了分类,一共分了13个大行业,40个小行业。同时对各行业,企业的财务数据进行处理:对每个行业内的所有公司的总资产收益率,净资产收益率,主营业务利润率三个财务指标用1999年、2000年和2001年三个年份分别进行排序,一共得到9个序列。其中,把排名前1/3的企业定义为“上游     

2003新领袖决定未来的商业力量

在全球商界，行业领先企业的领袖往往担负这么一个使命——代表整个行业来预测未来一段时间整个行业的产业和科技的趋势，我们称它为“领袖意识”或者“商界领袖意识”。你如果是企业领袖，你有义务、有责任来预测未来。     

医院分级成本效益评估体系探讨

目前，医院大多采用院科两级成本核算级次，医院成本效益评价指标体系也由院级考评指标和科级考评指标组成。一、院级成本核算效益评估指标院级成本效益评估工作常与财务经济活动分析同步进行，因而成本效益评估指标与财务分析指标也有一定的联系，它着重从人力成本、物力成本、管理水平的角度来考核评价投入产出的效益。通过对医院成本管理的绩效进行合理的评价，能够预测医院经济运行发展的未来趋势，及时为经营决策提供科学、实用的管理信息。院级人力成本效益评估指标主要有“人员经费增长率”和“人均收益率”。     

基于LVQ神经网络的财务舞弊识别模型实证研究

为了保护广大投资者和规范国内资本市场,对财务舞弊识别的研究具有重要的意义。在参考前人研究的基础上,选择能识别财务舞弊的指标,利用主成分分析法约减指标,得到9个综合变量。在此基础上,利用学习矢量量化(Learning Vector Quantization,LVQ)神经网络建立财务舞弊识别模型;此模型对测试样本的判断准确率高达90.9%,验证了模型的有效性。最后把此模型与用其他方法建立的财务舞弊识别模型进行比较,发现LVQ神经网络建立的财务舞弊识别模型,能更有效地识别测试样本有没有财务舞弊。     

Neural network forecasting of quarterly accounting earnings

This study uses an artificial neural network model to forecast quarterly accounting earnings for a sample of 296 corporations trading on the New York stock exchange. The resulting forecast errors are shown to be significantly larger (smaller) than those generated by the parsimonious Brown-Rozeff and Griffin-Watts (Foster) linear time series models, bringing into question the potential usefulness of neural network models in forecasting quarterly accounting earnings. This study confirms the conjecture by Chatfield and Hill et al. that neural network models are context sensitive. In particular, this study shows that neural network models are not necessarily superior to linear time series models even when the data are financial, seasonal and non-linear.     

改进财务比率在财务报表分析中的运用

财务比率分析法是财务报表分析中重要的分析方法之一。随着企业经营环境、经营范围的不断变化,一些传统财务比率已不能较准确反映企业财务状况。本文根据万科2011年年报数据,运用2个经过改进的财务比率(核心利润率、营业性资产周转率)对财务报表进行简要分析,以期在报表分析方面做一些有益的探索。     

Text-based crude oil price forecasting: A deep learning approach

This study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts.     

运用财务比率预测财务危机的模式与局限性

文章首先介绍了运用财务比率预测公司财务危机的两种模式:比威尔和赞靡杰威斯基的单变量预测模式与奥特曼的多变量Z值预测模式,接着从财务报表质量、财务比率幻觉、财务比率的比较基准、资产负债表的不完全性和通货膨胀等角度分析了运用财务比率预测财务危机的局限性。     

基于神经网络集成的失业预警方法

提出采用神经网络集成技术对中国失业预警系统进行建模,以克服当前失业预警系统建模中存在的小样本、高维度、非线性、噪音数据等难题。采用BP神经网络回归模型对失业率进行预测;基于两种集成技术Bagging与AdaBoost对多个神经网络进行集成,以获得比单个预测模型更好的精度与稳定性;最后基于广东省的社会经济调查数据进行了实证分析,实验结果表明:在对失业率的预测上,Bagging集成方法的预测效果优于Adaboost集成方法,也优于单个最好的神经网络模型。     

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Falsifying financial statements involves the manipulation of financial accounts by overstating assets, sales and profit, or understating liabilities, expenses or losses. This paper explores the effectiveness of an innovative classification methodology in detecting firms that issue falsified financial statements (FFS) and the identification of the factors associated to FFS. The methodology is based on the concepts of multicriteria decision aid (MCDA) and the application of the UTADIS classification method (UTilités Additives DIScriminantes). A sample of 76 Greek firms (38 with FFS and 38 non-FFS) described over ten financial ratios is used for detecting factors associated with FFS. A jackknife procedure approach is employed for model validation and comparison with multivariate statistical techniques, namely discriminant and logit analysis. The results indicate that the proposed MCDA methodology outperforms traditional statistical techniques which are widely used for FFS detection purposes. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of FFS and highlight the importance of financial ratios such as the total debt to total assets ratio, the inventories to sales ratio, the net profit to sales ratio and the sales to total assets ratio.     

Enforcement Releases,Firm Characteristics,and Earnings Quality: Insights from Germany's Two‐tiered Enforcement System

The implementation of a two‐tier external enforcement mechanism in Germany in 2004 permits the examination of the interrelationship of enforcement releases, firm characteristics, and earnings quality in a unique institutional framework. A total of 151 cases are examined where the announcement of errors in prior disclosed and audited financial statements was mandatorily ordered by the enforcement authority. Prior literature documents the correlation between underperformance in financial ratios and the probability of erroneous disclosure of financial statements. Evidence is provided of differences in characteristics between firms with enforcement releases and control firms as well as all German publicly listed firms. The research affirms the connection of financial ratios to earnings quality metrics. Overall, the results document the underperformance in important financial ratios as well as indicate an inferior earnings quality of firms subject to enforcement releases vis‐à‐vis the control groups. These results hold with regard to both different earnings quality specifications and different periods observed. This study adds to the earnings quality discussion and contributes to the development of a comprehensive picture of accounting quality in the unique institutional settings of Germany. The findings show that a conjoint two‐tier public and private enforcement system is effective and could be a model for consideration by other countries.     

An analysis of non-financial factors associated with financial distress

This article examines factors associated with financial distress among 1006 Spanish manufacturings (SMEs), distinguishing high and low technology industries. Financial distress is analysed using industrial organizational theory through the Porter's five competitive forces model (external factors) and the resource based view through strategic variables (internal factors), such as training, planning, innovation, technology and quality. Two different sources of information were used in the study: Qualitative information related to environmental conditions and strategic variables was gathered through a questionnaire addressed to the firm manager. Quantitative information to identify whether the firm was in financial distress was gathered from the balance sheets and earning statements of the firms. Evidence from this study shows that environmental conditions and some strategic variables are associated with financial distress. The results found that young SMEs with low technology and in a highly competitive environment had a higher probability of financial distress. High bargaining power of buyers and high degree of rivalry among existing competitors were positively associated with financial distress. Financial distress in high-technology industries was not affected by external factors. However, firms with a quality certification have better quality control procedures that ultimately improve financial performance of firms in the technology industries.     

人工神经网络用于短期电力负荷预测网络结构初探

目前,在电力系统短期负荷预测的手段中,已由人工预测方式逐步被软件预测方式所代替。BP人工神经网络是最常用的建立负荷预测模型的工具之一,文章对建立电力负荷BP神经网络预测模型（网络结构）进行了讨论。