Distribution of Net Operating Cash-Flow-at-Risk: the Dynamic Panel Data Model
Jing Lou, Liyan Han, Jinxia Liu
School of Economics & Management
Beihang University
Beijing, P.R.China
melonshoot@https://www.doczj.com/doc/0e18845877.html,
Abstract—Adopting the basic principles of the comparables approach to measuring Cash-Flow-at-Risk, this paper examines the operating CFaR of Chinese non-financial listed firms with a Dynamic Panel Data estimation based on the comparable industry clusters and the economic determinants of the operating cash flow. The empirical structural functions of the net operating cash flow and the empirical distributions of CFaR are achieved, which provide a solid basis for cash flow risk management. The results show that the representative firm in the comparable industry cluster of construction and real estate industries is exposed to higher operating CFaR than that in the largely monopoly-dominated industry cluster of mining, transport and storage, and utilities industries, while the traditional manufacturing industry shows moderate operating CFaR exposure as compared with the above two industry clusters.
Keywords-non-financial firms; Cash-Flow-at-Risk (CFaR); dynamic panel data; industry analysis
I.I NTRODUCTION
Operating cash flow is crucial to the survival of a non-financial firm; it is the link between the operating activities and the financing and investing decisions, and the sufficiency of cash flow decides not only the liquidity, but also the appropriate capital structure and risk management policies. In this sense, the identification and measurement of the volatility risk of operating cash flow are of great importance to the corporate strategic investments and risk management decisions.
The focus of this paper is a measure of the volatility risk of non-financial firm's operating cash flow known as Cash-Flow-at-Risk (CFaR). It is the cash flow equivalent of Value-at-Risk (VaR) for non-financial firms [1]. CFaR is the maximum cash flow shortfall corresponding to the probability level chosen by the firm. The concept of CFaR is initially originated by [2], which measures the market risk exposure by the sensitivity analysis, estimates the probability of the cash flow realization falling short of the expectation, and proposes hedging strategies for market risks. There have been developed two approaches of measuring CFaR: one is the bottom-up approach which is based on the VaR methodology [3] [4] [5]; the other is the top-down approach put forward by [6], which focuses directly on the total cash flow volatility.
The bottom-up measurement of CFaR is an analytical modeling based mainly on tradable market risks. This approach is useful when non-financial firms have confidence in their estimates of risks and in the understanding of the effects of the market prices changing on corporate cash flows. But in reality, the corporate risk exposures are so complex and multifaceted that it is impossible to model all of them, and then the bottom-up approach will capture only a small part of the firm's overall exposures and thus lead to inaccurate estimates of the overall CFaR.
By contrast, the top-down approach focuses on the total cash flow volatility, which reflects the combined effect of all the relevant risks facing a non-financial firm. Such volatility can be estimated from a firm's historical cash flows. When there are insufficient cash flow observations, Stein et al. [6] propose a comparables method to estimate a pooled cash flow distribution of a large number of comparable companies in order to gain a statistically significant estimate of volatility. The advantage of such an approach is its ability to provide systematically unbiased estimate of CFaR, and it can be easily and at relatively low cost applied to any number of non-financial firms, though it has the drawback that it does not provide an estimate of CFaR conditional on market risks.
There are few studies on the subject of the risk management of non-financial firms' operating cash flows in China currently. In this paper, adopting the basic principles of the top-down, comparables-based approach to estimating Cash-Flow-at-Risk, we examine the operating CFaR of Chinese non-financial listed firms with a Dynamic Panel Data estimation based on the comparable industry clusters and the economic determinants of the operating cash flow. We believe our study contributes to the understanding of the volatility risk properties of Chinese non-financial listed firms' operating cash flows, which will provide a solid basis for the corporate risk management decisions.
The rest of the paper is organized as follows: Section Ⅱpresents the measurement of CFaR and the data set; Section Ⅲprovides the industry cluster comparables method; Section Ⅳpresents the estimation results of our work; Section Ⅴconcludes.
Project supported by the Specialized Research Fund for the Doctoral
Program of Higher Education of China (No. 20050006025) and National
Natural Science Foundation of China (No. 70521001).
978-1-4244-4639-1/09/$25.00 ?2009 IEEE
II. M EASUREMENT OF CF A R
A. Modeling CFaR
CFaR is defined as the maximum cash flow shortfall corresponding to the probability level chosen by the firm. Formally, if cash flow is C and expected cash flow is E(C), CFaR is defined as follows:
()().Prob C -E C <-CFaR =p% (1) Different from the time series model in [6], we propose a
Dynamic Panel Data (DPD) model combing the information from both the cross section dimension and time series dimensions, with the significant determinants of operating cash flow identified by our previous study [7]. Based on firm-level data, CFaR is measured by AR(1) defined as follows:
it i,t-1it i t it CF =rCF +b X +f +d +e ′ (2)
where CF is the firm's net operating cash flow, scaled by
the firm size; X, the control variables including inventory level
and capital intensity. The subscript i indexes firms, and t , time.
i f denotes the firm-specific fixed effect variable, t d represents
the time-specific fixed effect variable, and it e is the residual set apart from the system risk, the distribution of which is the empirical distribution of CFaR. We estimate the above DPD model by GMM, using lagged regressors as instruments. B. Data and Variables The study relies on a primary sample of non-financial firms
in the China Stock Market Accounting Research (CSMAR) database. For the consistency of the data without the interruption of the changing in the accounting standards in China, the time period is selected from 1998 to 2005.
The variable of interest is the annual net operating cash flow (NETLEVEL), and the control variables are defined as follows: firm size (SIZE) is measured by the total assets; inventory level (INVRATIO) is measured by the ratio of inventory to total assets; capital intensity (CAPIRATIO) is measured by the ratio of depreciation to sales. Due to the firm size effects [7], all the net operating cash flows (NETLEVEL) are scaled by the firm assets (SIZE): NETRATIO = NETLEVEL/SIZE.
Firms are excluded from the sample if they are in either of the two cases: firms are tagged as Special Treatment (ST) or Particular Transfer (PT) by China Securities Regulatory Commission; firms have missing values of any of the aforementioned variables during the period 2003–2005 in order that there are at least three latest observations available for each firm.
After the data preprocessing, the sample includes companies from all the 11 non-financial industry categories in accordance with China's Guidelines on Industry Classification of Listed Companies except the communication and cultural industry category due to the lack of sufficient observations:
agriculture, forestry, livestock farming, fishery; mining; manufacturing; electric power, gas and water production and supply; construction; transport and storage; information technology; wholesale and retail trade; real estate; social service; comprehensive industry.
Due to the fact that there are even less available operating cash flow data for Chinese non-financial listed firms, we build the estimation of CFaR on the comparable industry clusters. The comparable industry clusters method is detailed in the next
section.
III. T HE INDUSTRY CLUSTER COMPARABLES METHOD
In our previous study on the determinants of the operating cash flow of non-financial firms in China [7], we observe the significant differences in the operating cash flows between industries; at the same time, we also notice that there are linkages between industries' operating cash flows. A country's economy is after all an organic whole, in which any industry is connected with the others through raw materials purchases, transportation and product sales. Operating cash flows and goods flows are the very links between industries. Due to such
linkages, it is expected that there is comparability in the
distribution patterns of the industries' operating cash flows.
We investigate the distribution patterns of the net operating cash flows of the 11 sample industries by hierarchical cluster
analysis based on the distributing characteristics of the net
operating cash flows, i.e. the 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles of the industry net operating cash flows (squared Euclidean distance and Ward's method used).
Grounded on the cluster analysis results (see Fig. 1) and
China's economy properties, we group the 11 sample industries
into four clusters: the mining, transport and storage, electric power, gas and water production and supply industries as the basic industry cluster; real estate, construction industries as the real estate & construction industry cluster; the manufacturing
industry as a cluster by itself due to its wide sub-category range and enormous numbers of firms; the other industries as another
industry cluster.
Figure 1. The cluster analysis result of the net operating cash flows of
sample industries.
In the following parts of this paper, we treat the industry cluster as a studying unit; the firms in the same industry cluster are considered as comparables. With this method, we can investigate the distribution patterns and risk characteristics of the net operating cash flows of the representative firm of the
industry cluster, and solve the problem in the estimation owing to the lack of existing operating cash flow observations.
IV.E STIMATION RESULTS
We estimate the coefficients given in (2) by GMM. In the following part we will mainly focus on the comparisons of the basic industry cluster, the real estate & construction industry cluster and the manufacturing industry cluster. In Table Ⅰ we report the results of the model after the fixed effects have been removed by orthogonal deviations [8]; yr1999–yr2004 are the time dummies, other variables definition see Section Ⅱ for details.
TABLE I. R ESULTS OF THE DPD AR(1)
Industry Cluster
Basic
Industry
Real estate &
Construction
Manufacturing
N firms 64 32 405 N obs 288 173 2111
NETRATIO(–1) 0.1565***
(11.09)
–0.1010**
(–2.14)
–0.2325***
(–3.69)
INVRATIO –0.3555***
(–16.79)
–0.2239***
(–4.51)
–0.2258**
(–2.51)
CAPIRATIO 0.1014***
(12.69)
–0.0834
(–0.7)
0.1702**
(2.22)
yr1999 –0.0351***
(–9.05)
–0.0467***
(–3.6)
–0.0211***
(–3.32)
yr2000 –0.0126***
(–2.94)
–0.0298***
(–3.7)
–0.0077
(–1.48)
yr2001 –0.0063**
(–2.22)
–0.0325***
(–2.99)
–0.0129***
(–2.74)
yr2002 0.0031
(0.93)
–0.0149
(–1.52)
–0.0076
(–1.53)
yr2003 –0.0110***
(–5.13)
–0.0148*
(–1.97)
–0.0082**
(–1.97)
yr2004 0.0021
(0.94)
–0.0000
(0)
–0.0121***
(–3.26)
The instruments are the 2–5 lags of the regressors. Due to collinearity, the sample period is adjusted to 1999–2004. Heteroskedasticity adjusted t-statistics are in parentheses. *Indicates significance at the 10% level. **Indicates significance at the 5% level. ***Indicates significance at the 1% level. The Sargan test
confirms the validity of the instruments.
The results show that the inventory level (INVRATIO) has negative effects on the net operating cash flows of all the three industry clusters. The basic industry cluster shows a coefficient of –0.3555, whose absolute value is about 58% higher than those of the real estate & construction industry cluster and the manufacturing industry cluster, the coefficients of which are –0.2239 and –0.2258 respectively. This result shows that in the low inventory-leveled basic industry cluster, the firm's net operating cash flow has higher sensitivity to the inventory factor than that in the real estate & construction industry cluster, of which the average inventory level is 0.3849 compared to 0.0406 of the basic industry cluster (see Table Ⅱ).
The capital intensity (CAPIRATIO) has positive effects on the net operating cash flows of the basic industry cluster and the manufacturing industry cluster. The manufacturing industry cluster shows a coefficient of 0.1702, much higher than that of the basic industry cluster. This result suggests that in the traditional manufacturing industries, the firm's net operating cash flow has higher sensitivity to the capital intensity factor, which means an appropriate increase of the operating leverage will lead to a higher increase in net operating cash flows. In contrast, the coefficient of the basic industry cluster is only 0.1014, suggesting that in the typical fixed-asset intensified industries such as the mining, transport and storage, electric power, gas and water production and supply industries, the firm's net operating cash flow becomes much insensitive to the capital intensity factor. The coefficient of the real estate & construction industry cluster is –0.0834, negative but insignificant.
TABLE II. S UMMARY STATISTICS FOR MAIN VARIABLES 1998–2005 Industry
Cluster
Variable Mean
Standard
Deviation Basic Industry
NETRATIO 0.0882 0.0720
INVRATIO 0.0406 0.0769
CAPIRATIO 0.1161 0.1339
Real estate &
Construction
NETRATIO 0.0130 0.0919
INVRATIO 0.3849 0.2303
CAPIRATIO 0.0095 0.0921 Manufacturing
NETRATIO 0.0490 0.0751
INVRATIO 0.1482 0.0939
CAPIRATIO 0.0389 0.0965 The distribution of the residuals of (2) is the empirical distribution of the operating CFaR. The empirical distributions of the operating CFaR of the three industry clusters are shown in Fig. 2, and the percentiles of the empirical distributions are presented in Table Ⅲ. The Jarque-Bera test rejects that the empirical distributions of the operating CFaR are normal distributed; the measure of skewness shows that the distribution of the real estate & construction industry cluster is negative skewed, while the other two industry clusters are positive skewed; the measure of kurtosis shows that the distributions of all the three industry clusters are leptokurtic.
TABLE III. CF A R EMPIRICAL PERCENTILES OF THE INDUSTRY CLUSTERS
Basic
Industry
Real estate &
Construction
Manufacturing P1 –0.1493
–0.3133 –0.2288 P5 –0.0994
–0.1683 –0.1245 P10 –0.0760
–0.1150 –0.0882 P25 –0.0430
–0.0520 –0.0433 P50 –0.0106
0.0048 –0.0035
P75 0.0411
0.0585 0.0433
P90 0.0917
0.1018 0.0963
P95 0.1269
0.1588 0.1273
P99 0.1845
0.2732 0.2176
Skewness 0.1006 –0.2716 0.5347
Kurtosis 5.5594 5.1934 11.8982
Jarque-Bera
P value
0 0 0
P1–P99 are the 1st to the 99th percentiles.
Before analyzing the distributions of the operating CFaR, we will first have a look at the operating characteristics of the industry clusters, particularly the real estate & construction industry cluster and the basic industry cluster.
Regarding the industry operating characteristics of the aforementioned two clusters, there are fundamental differences. The industries in the real estate & construction industry cluster are well-known for the long investment recovery term, large investment budgets, and high capital intensification. The product market for the two industries are strongly sensitive to
the macroeconomic circumstances and demands are subject to
great fluctuations, while in Chinese capital market these two highly-leveraged industries are confronted with stricter financing constraints.
In contrast, the three industries in the basic industry cluster have the features of being large asset-scaled, highly policy-oriented, and largely monopoly-dominated. These three industries play an essential role in the functioning of China's society and economy, being the critical industrial infrastructures of China's economic operation and development. The demands in the product markets for these industries are
less fluctuant, and the prices are under strict government
regulation. In this sense, the volatilities of the operating cash flows of these industries are relatively low. Besides, these industries have less strict financing constraints in that they have
easier access to the government funds support. Therefore, the basic industry cluster is expected to have lower sensitivity to the operating cash flow risk exposure than the real estate & construction industry cluster.
The empirical distributions of the net operating CFaR are
consistent with the prior expectations. In Fig. 2 it is shown that
the real estate & construction industry cluster has higher net operating cash flow risk exposure than the basic industry cluster. This result suggests that the representative firm in the real estate & construction industry cluster is exposed to higher operating CFaR than that in the largely monopoly-dominated basic industry cluster, while the traditional manufacturing industry cluster shows moderate operating CFaR exposure as
compared with the other two industry clusters.
Figure 2. Empirical distributions of CFaRs of the industry clusters.
At the 5th percentile of the empirical distributions (see Table Ⅲ), the operating CFaR of the real estate & construction industry cluster is –0.1683 (as noted in Section Ⅱ, all operating
CFaRs have been scaled by firm assets), while the corresponding figure for the basic industry cluster is only –0.0994. In other words, for the representative firm in the real estate & construction industry cluster, a five-percent worst-case scenario involves operating cash flow falling short of
expectations by $16.83 per $100 of assets; for the basic industry cluster the corresponding figure is $9.94, reaching only 59.06% of the former figure. Moreover, at high levels of risk (low CFaR values at the left tail in Fig. 2), the operating CFaR of the real estate & construction industry cluster is decreasing more sharply: at the 1st percentile (see Table Ⅲ), its
CFaR reaches –0.3133, while the corresponding figure for the basic industry cluster is –0.1493, only 47.65% of the former
figure. By contrast, at low levels of risk (high CFaR values at the right tail in Fig. 2), it is shown obviously that the CFaR of
the real estate & construction industry cluster is accelerating much more quickly than the other two industry clusters', which
suggests that when faced with positive shocks the firms in the real estate & construction industry cluster respond more effectively and flexibly, and thus produce more excess operating cash flows. On the whole, the real estate & construction industry cluster exhibits higher operating cash flow risk leverage. In contrast, the industries in the basic industry cluster are under much
stricter government regulation and the ability to adjust prices to their advantage is bounded; therefore, when faced with positive shocks, the ability of the basic industry cluster to produce excess cash flows, by seizing the operating and investing
opportunities, is lessened. As for the manufacturing industry cluster, as presented in Fig. 2 its CFaR empirical distribution lies between the other two clusters: for example, at the 1st percentile of the empirical distributions (see Table Ⅲ), the operating CFaR of the manufacturing industry cluster is –0.2288; of which the absolute value is 73.03% of the corresponding figures of the real estate & construction industry cluster, and 153.25% of the basic industry cluster; while at the 99th percentile the CFaR of the manufacturing industry cluster is 0.2176, slightly larger
than 0.1845 of the basic industry cluster, but only 79.65% of 0.2732 of the real estate & construction industry cluster. Considering the overall distribution of the operating CFaR, the traditional manufacturing industry cluster exhibits moderate operating CFaR exposure as compared with the basic industry cluster and the real estate & construction industry cluster, which is consistent with our expectations.
V.
C ONCLUSIONS
In this paper we examine the operating CFaR of Chinese non-financial listed firms with a Dynamic Panel Data (DPD) estimation based on the comparable industry clusters and the economic determinants of the operating cash flow. The comparable industry clusters analysis shows that the mining, transport and storage, electric power, gas and water production and supply industries are grouped as the basic industry cluster; real estate, construction industries as the real estate & construction industry cluster; the manufacturing industry as a cluster by itself due to its wide sub-category range and enormous numbers of firms.
Based on the comparable industry clusters, we propose a Dynamic Panel Data estimation model with the economic determinants of the operating cash flow (asset, inventory and capital intensity). The empirical results suggest that the representative firm in the real estate & construction industry cluster is exposed to higher operating CFaR than that in the largely monopoly-dominated basic industry cluster, while the traditional manufacturing industry shows moderate operating CFaR exposure as compared with the other two industry clusters.
We believe our paper contributes to the understanding of the properties of the operating cash flow volatility risk of Chinese non-financial listed firms. The results of this paper can be considered as a base to make corporate risk management decisions and to decide the appropriate financial structure for operating and investing projects. A side product of this paper is the comparable industry-cluster based approach could be extended to explore the dynamics of the transmission and diffusion of the cash flow risks between industries. We intend to pursue these results in another paper.
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我们都知道使用pos机刷卡消费或者转账是十分方便的，对于手续费也是有相关的规定，pos机手续费是根据行业的标准计算的，每个行业费率不同，因此相关的费率计算不同。 对于不同的POS类型还有行业不同相关的费率是不同的，具体的费率以及算法如下： 一、传统出小票POS机： 1、民生类：包括超市家电等，刷卡手续费是0.38%； 2、一般类：包括酒店宾馆等，刷卡手续费是1.25%； 3、批发类：包括家具批发家电批发等，手续费是26-80封顶。 4、标准类：信用卡刷卡0.55%~0.6%；储蓄卡刷卡0.5%，20元封顶。以刷卡一万为例，信用卡手续费55元~60元，储蓄卡手续费20元； 5、是优惠类：比加油站，手续费通常为信用卡0.38%；储蓄率0.4%，18元封顶； 6、减免类，比如公立医院、学校，手续费为0 费率0.38%是指刷卡手续费按照百分之0.38计算，比如刷100块钱手续费
是3角8分，刷1000块钱手续费是3元8角，以此类推。所以，刷卡费率等于刷卡金额乘以所属行业费率。 二、智能POS机 智能POS刷卡手续费和传统出小票POS的手续费是一样的。智能POS机和传统POS机相比，最大的优势就是支持主流二维码付款，收款更加便捷。二维码收款手续费一般为0.38%，交易一万，手续费38元。 三、手机POS机 手机POS机，顾名思义，就是通过手机蓝牙连接POS机刷卡消费的。一般手机POS机手续费不同支付公司会有一点区别，主流机器都是0.68%+3元/笔、0.69%+3/笔。手机POS机一般用于个人使用。 详细上述的内容可以帮助到您。如需了解更多请关注后续文章。投资有风险，加盟需谨慎。
pos机刷信用卡手续费怎么算|信用卡刷pos机手续费收取标准 【--出国祝福语】 如果你是消费者，pos机刷卡是不需要手续费的，pos 机刷信用卡手续费是扣商家的钱。 在全国各地所有POS机的商户，pos机刷卡消费都不收手续费，只要不出国，都不收手续费。无论是任何银行的任何卡。POS终端不向持卡人收取手续费。有收取的情况可拨打银联客服根据POS单的商户及商编投诉。 不同性质的商户，pos机刷卡时商家承担的手续费比例是不同的，这个主要看当时跟POS提供单位的签约情况来确定，通常是按照比例扣。
POS机刷信用卡卡手续费一般分为5类： 第一类商户含餐饮、宾馆、娱乐、珠宝金饰、工艺美术品类（一般扣率为2%-2.5%） 第二类商户含房地产、汽车销售、批发类（一般扣率为1%，可申请单比交易封顶） 第三类商户含航空售票、加油、超市类（一般扣率为0.5%-1%） 第四类商户含公立医院、公立学校（一般扣率视地区不同各有差异） 第五类商户含一般类（一般扣率为1%-3%）
如果你是消费者，pos机刷卡是不需要手续费的，pos 机刷信用卡手续费是扣商家的钱。 在全国各地所有POS机的商户，pos机刷卡消费都不收手续费，只要不出国，都不收手续费。无论是任何银行的任何卡。POS终端不向持卡人收取手续费。有收取的情况可拨打银联客服根据POS单的商户及商编投诉。 不同性质的商户，pos机刷卡时商家承担的手续费比例是不同的，这个主要看当时跟POS提供单位的签约情况来确定，通常是按照比例扣。 POS机刷信用卡卡手续费一般分为5类： 第一类商户含餐饮、宾馆、娱乐、珠宝金饰、工艺美术品类（一般扣率为2%-2.5%）
2017年各银行POS机刷卡手续费收取标准 提交需求，马上获得5家保险公司报价银行卡刷pos机的时候会根据一定费率收取刷卡手续费，那么2017年各银行pos机刷卡手续费收取标准是多少呢?2016年9月6日开始，新的银行pos机手续费收取标准开始实施，对餐饮、百货行业来说，pos机刷卡手续费可降低2—4成，不过信用卡封顶优惠政策也取消了，这意味着消费者可能将承担更多的pos机手续费。下文将为您介绍各银行pos机刷卡手续费收取标准。各银行pos机刷卡手续费收取标准2016年9月6 日银行pos机手续费新收取标准落地后，各银行也调整了手续费收取标准，下表是我国几大银行pos机刷卡手续费收取标准。各行业pos机刷卡手续费收取标准提癌症后，餐饮、宾馆、娱乐、珠宝金饰、工艺美术品、房产汽车类一律按照1.25%的费率收取pos机刷卡手续费，批发、百货、中介、培训、景区门票等则按照0.78%收取pos机刷卡手续费，加油、超市类、交通运输售票、水电气缴费、政府类、便民类pos机刷卡按照0.38%的费率收取手续费。此外，公立医院、公立学校、慈善类刷卡不收取pos机刷卡手续费。下表是各行业pos机刷卡手续费收取标准。 大类细类商户类别码（MCC)适用范围手续费率（％）备注餐娱类餐饮、宾馆、娱乐、珠宝金饰、工艺
美术品类5094贵重珠宝、首饰，钟表零售1.25 5811包办伙食、宴会承包商1.25原手续费率0.8％,2013年2月25日停用。5812就餐场所和餐馆（包括快餐）1.25不包括营业面积100 平方米（含）以下的餐饮5813饮酒场所－酒吧、夜总会、茶馆、咖啡馆1.25 5932古玩店-销售、维修和修复服务1.25 5937古玩复制店1.25 5944银器商店1.25 5950玻璃器皿和水晶饰品店1.25 5970工艺美术商店1.25 5971艺术商和画廊1.25 7012分时使用的别墅或度假用房1.25 7011住宿服务1.25宾馆酒店餐饮部分可再独立申请5812或5813编号7032运动和娱乐露营地1.25 7631手表、钟表和首饰维修店1.25 7033活动房车及露营场所1.25 7829电影和录像创作发行1.25 7911歌舞厅、KTV1.25 7922戏剧制片（不含电影）、演出和票务1.25 7297洗浴、按摩服务1.25原手续费率0.8％,2013年2月25日停用。7298美容、SPA1.25原手续费率0.8％,2013年2月25日停用。7929未列入其他代码的乐队、文艺表演1.25
pos机的费率怎么计算 POS系统基本原理是先将商品资料创建于计算机文件内，透过计算机收银机联机架构，商品上之条码能透过收银设备上光学读取设备直接读入后（或由键盘直接输入代号）马上可以显示商品信息（单价，部门，折扣...）加速收银速度与正确性。每笔商品销售明细资料（售价，部门，时段，客层）自动记录下来，再由联机架构传回计算机。经由计算机计算处理即能生成各种销售统计分析信息当为经营管理依据。 POS机是通过读卡器读取银行卡上的持卡人磁条信息，由POS操作人员输入交易金额，持卡人输入个人识别信息（即密码），POS把这些信息通过银联中心，上送发卡银行系统，完成联机交易，给出成功与否的信息，并打印相应的票据。POS的应用实现了信用卡、借记卡等银行卡的联机消费，保证了交易的安全、快捷和准确，避免了手工查询黑名单和压单等繁杂劳动，提高了工作效率。 磁条卡模块的设计要求满足三磁道磁卡的需要，即此模块要能阅读1/2、2/3、1/2/3磁道的磁卡。 内部分析 通讯接口电路通常由RS232接口，PINPAD接口，IRDA接口和
RS485等接口电路组成。RS232接口通常为POS程序下载口，PINPAD接口通常为主机和密码键盘的接口，IRDA接口通常为手机和座机的红外通讯接口。接口信号通常都是由一个发送信号、一个接收信号和电源信号组成。 MODEM板由中央处理模块、存储器模块、MODEM模块、电话线接口组成。首先，POS会先检测/RING和/PHONE信号，以确定电话线上的电压是否可以使用，交换机返回可以拔号音，POS拔号，发送灯闪动，开始拔号，由通讯协议确定交换机和POS之间的信号握手确认等，之后才开始POS的数据交换，信号通过MODEM 电路收发信号；完成后挂断，结束该过程。
刷卡服务的费用也因行业而异。一般行业为1％，餐饮/娱乐/珠宝/票务为2％，大型仓库超市/飞机票行业为0.5％，汽车/房地产为50元/笔，一些批发商品为20元/笔。 刷卡服务费按商家类型区分 百货商店约占1％，餐馆和酒店约占2％。批发，汽车和房地产按笔收费（从10至50不等）。慈善机构可能不收取服务费。如果您使用外国卡，银行将从商家处收取服务费。 异地刷卡服务费 在其他地方（包括香港，澳门和海外），民生卡和工银卡（所有银联卡）均不收取服务费。对于带有签证徽标的银行卡，香港将收取刷卡的服务费，称为货币转换费。因此，如果卡是双币种，则必须在国外使用银联卡，并且不收取服务费。 0.38是POS卡服务费的0.38％。扣除100元后，将扣除0.38元的服务费，扣除后实际收到的金额为99.62元。 根据国家发改委的通知，自2016年6月9日起，银行卡刷卡服务费将正式调整。
根据调整后的计划，卡的总体费率降低了53％至63％。 根据新准则，从2016年6月9日起，餐饮类别的税率从1.25％下降至0.6％。 百货公司刷卡服务费从0.78％下降到0.6％，而超市刷卡服务费从0.5％下降到0.38％。 公益仍然是0％。 房地产和汽车行业参照餐饮和娱乐业的收费标准，并且提高了单一费率。 扩展数据： 银行系统有两种类型的手续费： 1，贷款以外的利息统称为手续费。例如：信用卡分期还款产生的利息和逾期信用卡产生的利息； 2，在银行做生意的成本。
1.中国工商银行：卡手续费为5元，年费为10元/年，小于300元的小账户管理费为3元/季度，在同一个城市免费取款服务费，同城同业取款4元/笔；其他地方的信用卡存款免收手续费，取现金金额的1％，最低1元/笔，最高50元/笔 2，中国农业银行：卡手续费为5元，年费为10元/年，小于300元的小账户管理费为3元/季度，同一城市的银行间取款为2元/笔交易（广东省为4元）；收取信用卡取款金额的1％，最低为1元/笔，其他银行加收2元同业手续费 3）中国建设银行：卡手续费为5元，年费为10元/年，不足400元，小账户管理费为3元/季度，同城同业取款4元/交易+ 1％提款金额 4，中国银行：卡手续费为5元，年费为10元/年，不收取小额账户管理费，同城同业取款为4元/笔 5，交通银行：卡手续费5元，年费10元/年，小于500元的小账户管理费3元/季度，同一城市同业取款2元/交易
现在，使用pos机进行刷卡支付的人群越来越多，因此商家为了满足消费者需求，纷纷办理大pos机设备，然而对于很多商户来说，并不了解具体的费率是多少。 从费改后，刷卡手续费不再像原先细分的区分商户类别，不再对房产汽车、批发行业实行贷记卡手续费封顶计费，同时对标准类实行借贷分离收费：借记卡最低费率：发卡行0.35%+收单服务费(市场调节价); 贷记卡最低费率：发卡行0.45%+收单服务费(市场调节价)。 按收单方需要有0.15%以上的运营成本计算，新版费率收单机构的成本约为： 借记卡费率：0.35%+0.15%=0.5%; 贷记卡费率：0.45%+0.15%=0.6%。 以上是成本，正常运营需加0.05%左右，标准类商户刷信用卡极速到账的手续费在0.6%-0.65%之间是合理的。
标准类属于正常商户，刷卡均有积分，根据表中各方收取的手续费比例，加上公司的运营成本等因素，有积分的商户综合一般在0.60%费率基础之上。根据不同需求选择POS 很多用户都喜欢用低费率的POS，觉得手续费低可以省很多手续费，实际上，自己使用什么样的POS要根据自身的刷卡需求来定。 有的卡友刷卡要求有积分，以完成刷卡任务免除年费;有的卡友想获取积分参加活动换取优惠礼品，有的则是想提高自己的消费品质和信用卡额度。对于此类用户，建议选用0.60%左右的费率POS，刷卡交易有积分，发卡行获得一定利益了才愿意帮你的卡提额，避免出现降额甚至封卡的情况。 对POS的使用需求较少的卡友来说，每个月时不时地用现金消费，那就对费率没有太大要求，可选择快捷、扫码等方式，只要资金安全到账，加上平时逛商场、吃饭、交水电费等，既是正常现象，又丰富了账单，反正没人要求你需在有积分的商户那里消费。
pos机的费率怎么计算 依据我国银联和人行规定，商户使用银联卡POS机需要支付如下费用： 一般商户类：0.78%（是指一般零售商店，服装店，理发店，小餐馆等等绝多数商户） 餐饮娱乐类：1.25% （是指洗浴按摩，酒店旅馆，珠宝黄金等等）民生行业类：0.38% （是指大型卖场超市，加油站，票务等等） 封顶费率：26至80元（是指批发市场，房产汽车销售等等） 上述收费依据来源为中国人民银行2012（263）号文件，自2013年2月25日执行。 POS(Pointofsales)的中文意思是"销售点"，全称为销售点情报管理系统，是一种配有条码或OCR码技术终端阅读器，有现金或易货额度出纳功能。其主要任务是对商品与媒体交易提供数据服务和管理功能，并进行非现金结算。 POS是一种多功能终端，把它安装在信用卡的特约商户和受理网点中与计算机联成网络，就能实现电子资金自动转账，它具有支持消费、预授权、余额查询和转帐等功能，使用起来安全、快捷、可靠。大宗交易中基本经营情报难以获取，导入POS系统主要是解决零售业信息管理盲点。连锁分店管理信息系统中的重要组成部分。
POS系统基本原理是先将商品资料创建于计算机文件内，透过计算机收银机联机架构，商品上之条码能透过收银设备上光学读取设备直接读入后(或由键盘直接输入代号)马上可以显示商品信息(单价，部门，折扣...)加速收银速度与正确性。每笔商品销售明细资料(售价，部门，时段，客层)自动记录下来，再由联机架构传回计算机。经由计算机计算处理即能生成各种销售统计分析信息当为经营管理依据。 POS机是通过读卡器读取银行卡上的持卡人磁条信息，由POS操作人员输入交易金额，持卡人输入个人识别信息(即密码)，POS把这些信息通过银联中心，上送发卡银行系统，完成联机交易，给出成功与否的信息，并打印相应的票据。POS的应用实现了信用卡、借记卡等银行卡的联机消费，保证了交易的安全、快捷和准确，避免了手工查询黑名单和压单等繁杂劳动，提高了工作效率。 磁条卡模块的设计要求满足三磁道磁卡的需要，即此模块要能阅读1/2、2/3、1/2/3磁道的磁卡。
手续费是大家在使用POS机时候需要进行支付的费用，而且是每个POS机刷卡机使用者必须进行支付的费用，本次就分享商用pos机刷卡机手续费的相关介绍，希望对大家使用商用POS 机刷卡机有所帮助。 各大支付机构的商用POS机的手续费是不尽相同的，也就是说大家在使用的时候手续费会根据商用POS机品牌的不同而有所不同。 下面与大家分享常见的商用POS机费率： 付临门POS机在刷信用卡时的费率是0.6%，正好是国家规定的费率收取标准。 汇付天下的POS机刷信用卡的费率分为两种情况，其中使用APP自主进件的贷记卡费率则为0.58%。平台商户进件的费率会根据情况在0.55%到0.65%之间进行调整。 嘉联立刷POS机的刷卡费率则为0.6%+3，而国家自九六费改后费率规定为0.6%。除此之外，嘉联立刷的POS机在使用的时候无押金不冻结，而且办理嘉联支付的VIP后，POS机的费
率为0.5%+3。 瑞银信POS机采用的是一机三费率的模式，具体为小额付款的费率为0.49%费率并且带积分模式。刷卡时可以自由选择切换；大额付款模式是35元封顶模式即35元手续费，手续费单笔最低收取0.1元；超级付款模式是1%费率模式即刷卡金额的1%。 当然值得一提的是如果大家在使用POS机刷信用卡的时候到账模式是秒到的还需要额外缴纳3元的手续费。但是无论机构对于POS机费率的规定为多少，其中都有0.515%部分是缴纳给发卡行和银联的，所以大家不要为了节省这笔费率而做出一些不会规定的事情。 江苏勇斌电子支付技术有限公司是一家专业从事pos机办理的服务商，公司秉承“以人为本、科学管理、真诚服务”的经营理念，以开发市场、创造市场、服务市场为战略目标，为不同行业的企业、公司以及个人办理对私以及对公POS机办理业务。
pos机: POS的中文意思是“销售点”，全称为销售点情报管理系统，是一种配有条码或OCR码技术终端阅读器，有现金或易货额度出纳功能。 POS是一种多功能终端，把它安装在信用卡的特约商户和受理网点中与计算机联成网络，就能实现电子资金自动转账，它具有支持消费、预授权、余额查询和转帐等功能，使用起来安全、快捷、可靠。大宗交易中基本经营情报难以获取，导入POS系统主要是解决零售业信息管理盲点。连锁分店管理信息系统中的重要组成部分。 原理解析 pos机基本原理 pos机基本原理 POS系统基本原理是先将商品资料创建于计算机文件内，透过计算机收银机联机架构，商品上之条码能透过收银设备上光学读取设备直接读入后（或由键盘直接输入代号）马上可以显示商品信息（单价，部门，折扣...）加速收银速度与正确性。每笔商品销售明细资料（售价，部门，时段，客层）自动记录下来，再由联机架构传回计算机。经由计算机计算处理即能生成各种销售统计分析信息当为经营管理依据。 POS机是通过读卡器读取银行卡上的持卡人磁条信息，由POS 操作人员输入交易金额，持卡人输入个人识别信息（即密码），POS 把这些信息通过银联中心，上送发卡银行系统，完成联机交易，给出
成功与否的信息，并打印相应的票据。POS的应用实现了信用卡、借记卡等银行卡的联机消费，保证了交易的安全、快捷和准确，避免了手工查询黑名单和压单等繁杂劳动，提高了工作效率。 磁条卡模块的设计要求满足三磁道磁卡的需要，即此模块要能阅读1/2、2/3、1/2/3磁道的磁卡。 通讯接口电路通常由RS232接口，PINPAD接口，IRDA接口和RS485等接口电路组成。RS232接口通常为POS程序下载口，PINPAD接口通常为主机和密码键盘的接口，IRDA接口通常为手机和座机的红外通讯接口。接口信号通常都是由一个发送信号、一个接收信号和电源信号组成。 MODEM板由中央处理模块、存储器模块、MODEM模块、电话线接口组成。首先，POS会先检测/RING和/PHONE信号，以确定电话线上的电压是否可以使用，交换机返回可以拔号音，POS拔号，发送灯闪动，开始拔号，由通讯协议确定交换机和POS之间的信号握手确认等，之后才开始POS的数据交换，信号通过MODEM 电路收发信号；完成后挂断，结束该过程。 pos机的费率怎么计算: 刷卡手续费是按行业不同点位也是不同的，一般的行业都是1%，餐饮/娱乐/珠宝/票务都是2%，大型仓储超市/机票行业0.5%，汽车/房地产50元/笔，一些批发类20元/笔。
---------------------------------------------------------------范文最新推荐------------------------------------------------------ 2018pos机刷卡手续费标准 目前国内刷卡费率与行业分类挂钩，餐娱类的刷卡手续费率最高，为1.25%;百货等一般商户为0.78%;超市、加油站等为0.38%;医院、教育等公益类则是零费率。 根据新规，对发卡行服务费实行不再区分商户类别。也就是说，商户行业分类定价取消，总体上大幅降低了刷卡的费率水平。 新政显示，发卡银行服务费费率水平降低为借记卡交易不超过交易金额的0.35%，贷记卡交易不超过0.45%。 银行卡清算机构收取的网络服务费费率水平降低为不超过交易金额的0.065%，由发卡机构、收单机构各承担50%。从类别上看，餐饮类企业刷卡手续费支出可降低53%63%，百货等行业商户可降低23%39%。新政实行后，医院、教育等公益类刷卡仍为零费率。 信用卡大额消费成本高 不过，信用卡刷卡手续费取消封顶后，一旦遇到大额消费，商户费用成本必将增加，这部分成本由谁买单。 此前信用卡交易虽然费率高，但单笔交易有封顶，如果不封顶，持卡人刷信用卡消费10万元，单笔手续费要500多元。 记者注意到，最近几天，不少汽车4S店通过朋友圈开始营销：信用卡改革，9月6日前信用卡购车，省千元手续费。 南京一家汽车4S店负责人告诉记者：目前已经有一部分4S店明确， 1 / 24
客户负担多出的刷卡成本。对比看，9月6日前信用卡购车，刷卡手续费是0元。但是，9月6日后信用卡购车，如果刷10万元，手续费600元;20万元则是1200元;50万元则需要支付3000元成本。 事实上，免手续费的概念早已深入人心，突然出现数百元刷卡付费成本，不少持卡人很难接受。不过，上述负责人指出，虽然成本增加了，但是，上不封顶提高了套现成本，可以防止一些非正常的套现客户。上述负责人指出：有些车商没有获得厂家授权，就无法贷款。但是，他们会刷卡套现支付车款，等于是变相贷款。 pos机刷卡手续费标准 新版手续费施行在即，银联5月正式开始对全国市场存量的商户进行重新入网的施行工作，原本MCC的几大类都被改名字了，MCC几大类别的概述变化，分为：标准类，优惠类，减免类，特殊计费类(或取消) 原一般类、餐娱类改称标准类：1.25%/1.28%-80封顶/0.78%/0.78%-26封顶 2018年9月6日实行的新版刷卡手续费取消了以上两类商户行业分类定价，对房产、汽车、批发行业不再实行贷记卡手续费封顶计费，并对标准类实行借贷分离收费，即 借记卡最低费率：发卡行0.35%(单笔13元封顶)+收单服务费(市场调节价) 贷记卡最低费率：发卡行0.45%+收单服务费(市场调节价) 按收单方需要有0.15以上%的运营成本计算，新版费率收单机构的
pos机的费率怎么计算 使用标准费率0.6%以上较为合理，低于0.6%的反而要进行辨识。 一、传统POS机：0.6%以上（市场上的流行费率） 二、手机POS机：0.6%以上（0.6%以下100%商户有问题） 有很多同行问在96费改政策新的标准后我们的费率到底多少，为什么商户看电视报纸说“国家降费率0.35%、0.45%了你们却收我们0.65%、0.7%”。 那么，刷卡手续费调整为多少?银联刷卡手续费标准怎么算?新的手续费收费有哪些标准?接下来跟你一一道来。 先说说以前，在费改以前，国内刷卡费率与行业分类挂钩，餐娱类的刷卡手续费率最高，为1.25%;百货等一般商户为0.78%;超市、加油站等为0.38%;医院、教育等公益类则是零费率。 根据96费改后的要求，对发卡行服务费实行不再区分商户类别。也就是说，商户行业分类定价取消，总体上大幅降低了刷卡的费率水平。 新政显示，发卡银行服务费费率水平降低为借记卡交易不超过交易金
额的0.35%，贷记卡交易不超过0.45%。但是商户朋友们你别就看定了，得往下看，银联和收单公司的还没算上呢。 银行卡清算机构收取的网络服务费费率水平降低为不超过交易金额的0.065%，由发卡机构、收单机构各承担50%。从类别上看，餐饮类企业刷卡手续费支出可降低53%—63%，百货等行业商户可降低23%—39%。新政实行后，医院、教育等公益类刷卡仍为零费率。 POS机刷卡手续费，信用卡大额消费成本高。 不过，信用卡刷卡手续费取消封顶后，一旦遇到大额消费，商户费用成本必将增加，这部分成本由谁买单。 此前信用卡交易虽然费率高，但单笔交易有封顶，如果不封顶，持卡人刷信用卡消费10万元，单笔手续费要500多元。 一家汽车4S店负责人告诉我：“目前已经有一部分4S店明确，客户负担多出的刷卡成本。”对比看，9月6日前信用卡购车，刷卡手续费是0元。但是，9月6日后信用卡购车，如果刷10万元，手续费600元;20万元则是1200元;50万元则需要支付3000元成本。 事实上，免手续费的概念早已深入人心，突然出现数百元刷卡付费成
刷卡手续费： 商店接受客户刷卡后，需支付百分之二至三的手续费给银行和信用卡中心。称为刷卡手续费。2012年11月央行下发《中国人民银行关于切实做好银行卡刷卡手续费标准调整实施工作的通知》称，此次手续费下调仅涉及境内银行卡的消费交易，2003年施行的商户刷卡手续费规定自2013年2月25日起同时废止，此次刷卡费率总体下调幅度在23%至24%。 特约商店接受客户刷卡后，需支付百分之二至三的手续费给银行和信用卡中心。有些厂商为节省成本，会要求持卡人另外支付手续费，因为刷卡就必需开立发票，使商店无法逃税。 根据现行的《中国银联入网机构银行卡跨行交易收益分配办法》，银行卡收单业务的结算手续费全部由商户承担，但不同行业所实行的费率不同，费率标准从0.5%到4%不等。一般来说，零售业的刷卡手续费率在0.8%-1%，超市是0.5%，餐饮业为2%。 商家转嫁： 案例举证 2010年08月29日上午，市民陈先生反映，他在大利嘉城刷卡购物时被商家收取手续费。对此，银联工作人员指出，商家的做法违反了相关合同协议，消费者可向银联公司举报。 陈先生告诉记者，他去大利嘉城买相机，没带现金只带了银行卡，老板说如果刷卡需要他支付20块钱的手续费。平时他在永辉超市、
沃尔玛以及国美电器等商场超市也是刷卡购物，可商家从没向他收取任何手续费。 随后，记者在大利嘉城调查了15家安装了POS刷卡机的商家。其中，14家商家表示遇到消费者刷卡消费时，会要求持卡人交手续费。“手续费不是商家要的，而是直接从卡里转给了银行。”大利嘉城一家专营笔记本电脑的郑老板称，对消费者来说，刷卡购物方便快捷，还有积分累积等优惠，但对商家来说，消费者每使用POS机刷卡消费一次，商家需支付给银行消费数额1%的手续费。因此，大利嘉城大部分商家都会向消费者收取1%~2%的刷卡手续费，将POS 机的交易费用和成本“转嫁”到消费者头上. 银联解答 “商家把刷卡手续费转嫁给消费者，这是一种违规行为。”中国银联客服工作人员告诉记者，在全国范围内，银联卡刷卡消费，不分异地本地都不向持卡人收取任何手续费。刷卡消费后结算方是银行和商家，按照当初安装刷卡机时的相关协议，应是商家向银行支付一定比例的手续费。因此，如果刷卡消费时被要求加收手续费，持卡人有权拒绝，并可向中国银联客服热线投诉.