Distribution of Net Operating Cash-Flow-at-Risk The Dynamic Panel Data Model

Distribution of Net Operating Cash-Flow-at-Risk: the Dynamic Panel Data ModelJing Lou, Liyan Han, Jinxia LiuSchool of Economics & ManagementBeihang UniversityBeijing, P.R.Chinamelonshoot@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 analysisI.I NTRODUCTIONOperating 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 DoctoralProgram of Higher Education of China (No. 20050006025) and NationalNatural Science Foundation of China (No. 70521001).978-1-4244-4639-1/09/$25.00 ©2009 IEEEII. M EASUREMENT OF CF A RA. Modeling CFaRCFaR 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 aDynamic 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 bythe firm size; X, the control variables including inventory leveland capital intensity. The subscript i indexes firms, and t , time.i f denotes the firm-specific fixed effect variable, t d representsthe 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 firmsin 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 nextsection.III. T HE INDUSTRY CLUSTER COMPARABLES METHODIn 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 suchlinkages, it is expected that there is comparability in thedistribution 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 clusteranalysis based on the distributing characteristics of the netoperating 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) andChina's economy properties, we group the 11 sample industriesinto 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 manufacturingindustry as a cluster by itself due to its wide sub-category range and enormous numbers of firms; the other industries as anotherindustry cluster.Figure 1. The cluster analysis result of the net operating cash flows ofsample 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 theindustry cluster, and solve the problem in the estimation owing to the lack of existing operating cash flow observations.IV.E STIMATION RESULTSWe 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 ClusterBasicIndustryReal estate &ConstructionManufacturingN firms 64 32 405 N obs 288 173 2111NETRATIO(–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 testconfirms 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 IndustryClusterVariable MeanStandardDeviation Basic IndustryNETRATIO 0.0882 0.0720INVRATIO 0.0406 0.0769CAPIRATIO 0.1161 0.1339Real estate &ConstructionNETRATIO 0.0130 0.0919INVRATIO 0.3849 0.2303CAPIRATIO 0.0095 0.0921 ManufacturingNETRATIO 0.0490 0.0751INVRATIO 0.1482 0.0939CAPIRATIO 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 CLUSTERSBasicIndustryReal estate &ConstructionManufacturing 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 –0.01060.0048 –0.0035P75 0.04110.0585 0.0433P90 0.09170.1018 0.0963P95 0.12690.1588 0.1273P99 0.18450.2732 0.2176Skewness 0.1006 –0.2716 0.5347Kurtosis 5.5594 5.1934 11.8982Jarque-BeraP value0 0 0P1–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 tothe macroeconomic circumstances and demands are subject togreat 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 areless fluctuant, and the prices are under strict governmentregulation. 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 haveeasier 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 areconsistent with the prior expectations. In Fig. 2 it is shown thatthe 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 ascompared 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 operatingCFaRs 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 ofexpectations 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), itsCFaR reaches –0.3133, while the corresponding figure for the basic industry cluster is –0.1493, only 47.65% of the formerfigure. By contrast, at low levels of risk (high CFaR values at the right tail in Fig. 2), it is shown obviously that the CFaR ofthe real estate & construction industry cluster is accelerating much more quickly than the other two industry clusters', whichsuggests 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 muchstricter 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 investingopportunities, 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 largerthan 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 ONCLUSIONSIn 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.R EFERENCES[1]G. 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