企业筹资风险研究——以汽车行业为例

文章对企业盈利情况、负债情况、营运情况和成长情况与筹资风险的关系进行了综合研究。以汽车行业为研究对象,通过描述性分析详细地说明了汽车行业的总体财务状况,利用相关性分析和多元回归分析全面地分析了各影响因素与筹资风险的关系,结果表明,企业盈利情况、营运情况和成长情况与筹资风险成反比,负债情况与筹资风险成正比。最后针对汽车行业财务现状,提出有效的筹资风险控制措施。     

企业负债筹资风险的分析与控制

负债筹资可以提高企业的市场竞争能力，扩大生产规模，使企业得到财务杠杆收益，降低企业综合资金成本。同时，负债筹资也增加了企业的负债筹资风险，加大了企业的财务风险，降低再筹资能力。企业要树立正确的风险防范意识，建立有效的负债筹资风险防范机制。本文在分析负债筹资的利弊与负债筹资风险产生动因的基础上，指出了树立风险防范意识、建立有效的风险防范机制等防范负债筹资风险的相关对策和措施。     

负债筹资风险分析及其防范

目前阶段,我国的企业普遍面临资金短缺问题,负债筹资是解决其资金问题的主要途径,也是被普遍采用的一种筹资方式。有负债就会有筹资风险,因负债而引起的风险是企业管理者不容回避的现实问题。一、负债筹资风险的分析负债筹资风险,是指与企业负债筹资相关的风险,一般是指由于资金供需情况、宏观经济环境等因素的变化,企业筹集借入资金给财务成果带来的不确定性。它有两层含义,一是指负债筹资导致企业所有者收益下降的风险;二是指负债筹资可能导致企业财务困难甚至破产的风险。企业负债筹资风险的形成既有举债本身因素的作用,也有举债之外因素…     

企业筹资风险成因分析及对策

资金是企业进行生产经营活动的必要条件,但无论是以主权资本方式还是以负债资本方式进行的资金筹措,都存在着一定的风险.筹资风险既有举债本身的作用,也有举债之外的影响.规避筹资风险应确定最佳资本结构,合理安排筹资期限组合方式,科学预测利率及汇率的变动、风险转移等.     

企业筹资风险浅析

筹资活动对于一个企业也是至关重要的,它伴随企业从产生到发展到衰落的整个生命周期过程.在市场经济条件下,企业的筹资风险是客观存在的,它不可避免地合影响企业的整个财务活动,造成企业财务结果的不确定性.本文从企业筹资风险的成因分析入手,提出筹资风险预测方法以及筹资风险的防范和控制措施,以提高企业资金筹集的综合经济效益.     

规避和弱化企业财务风险的对策探讨

财务风险又称筹资风险,是指由于负债筹资而引起的到期不能偿债的可能性。它是因由债务而发生的仅由主权资本承担的附加风险,只要企业存在负债筹资,就不可避免的要面对该风险。因此,如何正确认识和衡量该风险,并通过恰当的方式加以规避和弱化该风险,就成为企业财务管理的重要任务之一。     

论企业筹资风险及其防范

筹资活动是企业生产经营活动的起点，企业通过筹集资金以扩大生产经营规模，提高企业经济效益，可以说负债筹资是现代企业的主要经营手段之一，但是它也是一把双刃剑，运用得当会给企业带来财务杠杆收益，促进企业经济效益的提高；但同时，也不可避免地给企业带来筹资风险。目前，我国大多数企业在筹资风险管理方面还非常薄弱，防范和化解风险的能力不强，如何有效防范和化解筹资风险是企业发展壮大的关键。本文从筹资风险形成的原因以及负债筹资对企业经营的作用出发，探讨和分析了关于企业筹资风险的有效防范措施。     

企业负债经营的若干思考

随着市场经济的发展,现代企业普遍采用负债方式进行筹资。负债经营是一种有效的经营方式,对企业的生产经营同时具有积极作用和消极作用,会给企业带来收益,也会给企业带来财务风险。文章主要对负债经营的利弊和负债风险产生的原因进行了分析,并提出了防范负债经营风险的措施。     

浅谈企业负债筹资的风险防范

<正>筹资对于一个企业是至关重要的,是企业投资经营的起点。但是筹资必然伴随着各种各样不同层次的风险。本文所说的筹资风险,主要是指企业在负债筹资的方式下,由于各种原因而引起的到期不能还本付息的风险。如果从机会成本角度认识筹资风险,可以表现为企业负债经营时,由于资本结构不合理,导致主权资本不能实现保值和增值的可能     

医院财务风险的预警与防范

如今医院之间的竞争是越演越烈，在政府财政补助严重不足的情况下，医院大多采用对外筹资的方式扩张自己的经营规模，以此来提高自己的竞争能力，但是随着负债额度的逐步增加，医院的财务风险也在不断地加大。本文以某三甲医院最近5年的财务数据为例，利用医院财务风险指数分析其随着负债额度的增加，财务风险的变化情况，并剖析原因，提出预警机制和防范财务风险的对策。     

Macroeconomic forecasting using factor models and machine learning: an application to Japan

We perform a thorough comparative analysis of factor models and machine learning to forecast Japanese macroeconomic time series. Our main results can be summarized as follows. First, in many instances, factor models and machine learning perform better than the conventional AR model. Second, predictions made by machine learning methods perform particularly well for medium to long forecast horizons. Third, the success of machine learning mainly comes from the nonlinearity and interaction of variables, which suggests the importance of nonlinear structure in predicting the Japanese macroeconomic series. Fourth, the composite forecast of factor models and machine learning performs better than factor models or machine learning alone; and machine learning methods applied to common factors are found to be useful in the composite forecast.     

How Useful are DSGE Macroeconomic Models for Forecasting?

A review of the literature shows that forecasts from DSGE models are not more accurate than either times series models or official forecasts, but neither are they any worse. Further, all three types of forecast failed to predict the recession that started in 2007 and continued to forecast poorly even after the recession was known to have begun. The aim of this paper is to investigate why these results occur by examining the structure of the solution of DSGE models and compare this with pure time series models. The main factor seems to be the dynamic structure of DSGE models. Their backward-looking dynamics gives them a similar forecasting structure to time series models and their forward-looking dynamics, which consists of expected values of future exogenous variables, is difficult to forecast accurately. This suggests that DSGE models should not be tested through their forecasting ability.     

An Examination of the Statistical Significance and Economic Relevance of Profitability and Earnings Forecasts from Models and Analysts

In this paper, we propose and empirically test a cross‐sectional profitability forecasting model which incorporates two major improvements relative to extant models. First, in terms of model construction, we incorporate mean reversion through the use of a two‐stage partial adjustment model and inclusion of a number of additional relevant determinants of profitability. Second, in terms of model estimation, we employ least absolute deviation (LAD) analysis instead of ordinary least squares because the former approach is able to better accommodate outliers. Results reveal that forecasts from our model are more accurate than three extant models at every forecast horizon considered and more accurate than consensus analyst forecasts at forecast horizons of two through five years. Further analysis reveals that LAD estimation provides the greatest incremental accuracy improvement followed by the inclusion of income subcomponents as predictor variables, and implementation of the two‐stage partial adjustment model. In terms of economic relevance, we find that forecasts from our model are informative about future returns, incremental to forecasts from other models, analysts’ forecasts, and standard risk factors. Overall, our results are important because they document the increased accuracy and economic relevance of a cross‐sectional profitability forecasting model which incorporates improvements to extant models in terms of model construction and estimation.     

组合预测模型在我国旅游业预测中的应用

文章首先选取Holt-Winters非季节指数平滑模型、自回归分布滞后模型以及局部多项式回归模型对中国1978-2011年的国际旅游外汇收入进行定量分析,随后基于预测误差平方和最小方法构造组合预测模型,研究结果表明组合预测模型的精度明显优于单项预测模型。     

Rowing along the computable general equilibrium modelling mainstream

Theoretical, empirical, and technical principles of computable general equilibrium models (CGE models) development have been presented. It is commented how to model the economical processes using this approach. many economists develop the CGE mosels in order to solve the problems of economical forecast. But the resonable criticism of this method is ignored. By analyzing the approaches to CGE models development in the paper it is concluded that the majority of CGE models are not able to describe properly the operation of real economics losing to the method of interindustry forecast. In the recent time the CGE models have been widely used in economics. The presented paper explain why such attractive methods can lose its popularity.     

归并自变量模型估计

在线性回归模型中,如果因变量存在样本归并,普通的LS估计不再一致,Rigobon和Stoker（2004,2007）建议使用部分样本回归或完整形式分析的方法来获得参数的一致估计,但是,它们都不是有效估计。本文使用基于EM算法的ML估计来获得参数的有效估计,在正态混合模型设定下详细推导了观测样本的似然函数以及相应的EM迭代方程。数值模拟的结果表明,基于EM算法的ML估计比部分样本回归和完整形式分析的方法具有更好的小样本表现。     

Prediction of the Netherlands' money stock

Summary This article describes the operational procedure for a mechanical monthly forecast of the money stock in The Netherlands for up to twelve months ahead. In addition to time series data of the money stock itself, the procedure uses the information provided by the disaggregation of the money stock into financial assets, sources of money supply and holdership. The forecast from a univariate ARIMA model of the money stock is combined with three different forecasts from vector ARIMA models for the components distinguished by the three ways of disaggregation. The combination weights, which differ for each number of months to be forecasted ahead, are determined by regression analysis.Professor of Economics at the Free University, Amsterdam and staff member of the Econometric Research and Special Studies Department of De Nederlandsche Bank NV, Amsterdam, respectively. This study was completed when the first author was at the Nederlandsche Bank. We gratefully acknowledge the useful comments of a referee on a previous version of this article.     

Forecasting Inflation: An Art as Well as a Science!

Summary In this study, we build two forecasting models to predict inflation Harmonised Index of Consumer Prices (HICP) for the Netherlands and for the euro area. The models provide point forecasts and prediction intervals for both the components of the HICP and the aggregated HICP -index itself. Both models are small-scale linear time series models allowing for long-run equilibrium relationships between HICP components and other variables, notably the hourly wage rate and the import or producer prices. The model for the Netherlands is used to generate the Dutch inflation projections for the eurosystem’s Narrow Inflation Projection Exercise (NIPE). The recursive forecast errors for several forecast horizons are evaluated for all models, and are found to outperform a naive forecast and optimal AR models. Moreover, the same result holds for the Dutch NIPE projections, which have been provided quarterly since 1999. The aggregation method to predict total HICP inflation generally outperforms the direct method, except for long horizons in the case of the Netherlands. We thank an anonymous referee of this journal and participants of the second Conference of the Euro Area Business Cycle Network, the 10th International Conference on Computing in Economics and Finance and seminar participants at De Nederlandsche Bank, Erasmus University Rotterdam and Maastricht University, in particular Filippo Altissimo, Bob Chirinko, Bertrand Candelon, Denise Osborn and Adrian Pagan for useful comments.     

Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach

Macroeconomic data are an important piece of information in decision making for both the public and private sectors in Thailand. However, the release of key macroeconomic data, usually in a lower frequency such as quarterly, is not always in a timely manner. Using the higher frequency data such as monthly and daily to analyze or forecast the lower frequency data can mitigate the release timing effect. This study applies the mixed-frequency data approach to analyze and forecast Thai key macroeconomic data. The mixed data sampling regressions with various specifications are employed and implemented through some macroeconomic data such as gross domestic product and inflation. The results show that in most cases the mixed-frequency models outperform the autoregressive integrated moving average model, which we used as the benchmark model, even during the COVID-19 period. Some policy implications can also be drawn from the analysis.     

A STOCHASTIC ESTIMATION FRAMEWORK FOR COMPONENTS OF THE SOUTH AFRICAN CONSUMER PRICE INDEX

Under inflation targeting in South Africa, it is important to monitor and forecast changes in prices, not only for aggregate measures of the consumer price index, but also its underlying sub-components. Hypotheses about sectoral transmission of policy and shocks are often more specific than hypotheses about overall transmission. This study employs a stochastic framework to estimate richly specified equilibrium correction models, four-quarters-ahead, for the 10 sub-components of the first targeted measure of the consumer price index, CPIX. The stochastic trends are estimated by the Kalman filter, and interpreted as capturing structural breaks and institutional change, a frequent cause of forecast failure. The trends suggest the design of deterministic split trends for use in recursive forecasting models, towards more accurate overall inflation forecasting. This research also has practical use for monetary policy in allowing identification of sectoral sources of inflation.