Business Counterparties' Uncertainty and Corporate Credit Risk: Production Efficiency Prospective. Submit to 2015 FMA Annual Meeting.

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1 Business Counterparties' Uncertainty and Corporate Cred Risk: Production Efficiency Prospective Subm to 2015 FMA Annual Meeting Tsung-Kang Chen Associate Professor Department of Finance and International Business Fu Jen Catholic Universy Hsien-Hsing Liao Professor Department of Finance National Taiwan Universy address:

2 Business Counterparties' Uncertainty and Corporate Cred Risk: Production Efficiency Prospective Abstract We explore the effects of suppliers / customers production efficiency uncertainty (PEU) on corporate cred risk by employing American bond observations from the year 1997 to We find that customers PEU is posively related to corporate bond yield spreads whereas suppliers' has an oppose effect. The former result shows the importance of demand uncertainty while the latter one suggests that the benefs of supply chain integration or information sharing exceed the costs of supply chain uncertainty. We also find that the effects are significantly affected by business counterparties' information asymmetry. In addion, the customer-side effect becomes weaker during the financial crisis period, whereas the supplier-side one is insignificantly affected. Keywords: Production efficiency uncertainty; Business counterparties; Cred risk; Supply chain uncertainty; Information flow risk JEL Classifications: D24; G12; M11; M21 1

3 1. Introduction The recent global financial crisis reveals that a firm s cred risk is affected by the risks of s business counterparties, i.e. the firm's suppliers and customers, via the trade relationship between the firm and them (latter denoted as supply chain relationship). The risks conveyed through the supply chain relationship are related to cash flows, inventory flows and information flows. Many previous studies document the importance of these three flow risks (e.g. Lee et al., 1997a; Tsai, 2008) 1 and indicate that the supply chain relationship provides a channel to transm the variations of the three flows of suppliers and customers to a firm. Chen et al. (2013a) and Chen et al. (2013b) demonstrate that the information asymmetry and internal liquidy risk of a firm's suppliers and customers are posively related to the firm's cred risk (bond yield spreads). 2 However, the production efficiency uncertainty of the business counterparties whin a supply chain relationship, one of the major sources of supply chain uncertainty (Davis, 1993; Mason-Jones and Towill, 1998; Simangunsongy et al., 2012), 3 is rarely discussed in the cred risk lerature. A firm's production efficiency is commonly viewed as a crucial factor for a firm's value creation process and is posively related to operating performance. However, production efficiency information is less disclosed than revenue information in current financial reporting system, which contributes to a firm s incomplete accounting information. Therefore, 1 Lee et al. (1997a) demonstrate that the information flows play an important role for the coordination in a supply chain relationship and have a direct impact on a firm's operations, such as production scheduling, inventory policy and shipping plans of the firm s suppliers and customers. Tsai (2008) also describes the variations of inventory flow and cash flow whin the supply chain relationship by employing cash conversion cycle (operating cycle). 2 Chen et al. (2013b) also show that he effects of customers internal liquidy risks (ILRs) are greater and the ILR effect becomes greater upwardly along the business counterparties. 3 Davis (1993) identifies three sources of supply chain uncertainty, including demand, manufacturing process, and supply uncertainty. The model of Davis (1993) suggests that demand and supply uncertainty have an effect on manufacturing process uncertainty, which in turn affects timely order fulfilment. The above three uncertainties of demand, manufacturing process, and supply are closely related to the inventory flow risk. In addion, manufacturing process uncertainty could be reasonably viewed as production efficiency uncertainty since production efficiency is an essence of manufacturing process. 2

4 a firm s production efficiency is not able to be fully detected by outside investors. In addion, Kahn (1987) demonstrates that the variance of production exceeds the variance of sales wh demand uncertainty, 4 and Chen et al. (2014a) show that production efficiency uncertainty lims investors' abily in predicting a firm's operating performance. That is, production efficiency variation substantially influences a firm s inventory flow, cash flow, s asset value distribution and cred risk. Through the supply chain relationship, the production efficiency uncertainty (later denoted as PEU) of a firm s supplier and customer affects the firm s production-related risks and hence s asset value distributions. Based on structural form cred models of Merton (1974) and Duffie and Lando (2001), a firm s cred risk is closely related to the PEUs of s suppliers and customers. Few existing studies consider the risk transmission effect of the supply chain relationship, 5 and incorporate the PEU effects of a firm s suppliers and customers into corporate cred model setting or investigate their effects on bond yield spreads. To fill up part of the gap, this study employs American corporate bond data to examine the effects of a firm's suppliers / customers PEU on the firm's bond yield spreads. Regarding the discussions of the relationship between a firm s cred risk and s business counterparties, most studies focus on the wealth effects of financial distress between a firm and s rivals or s suppliers and customers. More specifically, most of them devote themselves to investigating the effects of bankruptcy announcements on the equy values of the bankrupt firm s competors (Lang and Stulz, 1992), or their customers and suppliers (Hertzel et al., 2008). Different from Lang and Stulz (1992) and Hertzel et al. (2008), Kale and Shahrur (2007) firstly examine the effects of suppliers and customers characteristics on 4 Kahn (1987) proposes a production counter-smoothing hypothesis to explain the stylized fact of inventory behavior. Blinder (1986) and West (1986) also have similar arguments. 5 Chen et al. (2013a) and Chen et al. (2013b) demonstrate that suppliers / customers trading-based information asymmetry and internal liquidy risk increase a firm s cred risk, respectively. In addion, Chen et al. (2014b) also demonstrate that suppliers / customers macroeconomic risks posively relate to firm cred risk. 3

5 corporate capal structure, 6 especially for the feature of bargaining power (e.g. industry concentration and R&D intensy). According to the Porter s (1979) five-force analytic framework, the bargaining powers of a firm s suppliers and customers affect s profabily, cash flow and hence asset value distribution. Tsai (2008) provides an insightful look at business counterparties' related cash flow risk and employs cash conversion cycle (or operating cycle) to describe the variations of a firm's product flow and cash flow. Therefore, the characteristics of a firm s suppliers and customers affect the firm's asset value distributions and s cred risk. Some recent studies document that suppliers / customers characteristics affect firm cred risk (bond yield spreads) from the perspectives of information asymmetry (Chen et al., 2013a), internal liquidy risk (Chen et al., 2013b), and macroeconomic sensivy volatily (Chen et al., 2014b). 7 However, few studies discuss how suppliers / customers PEU affects firm cred risk. This study therefore addresses the issue by examining the effects of a firm's business counterparties' PEUs on the firm bond yield spreads. Concerning the connection between a firm s production efficiency and the supply chain relationship, Lee et al. (1997a, 1997b) assert that the information transferred in the form of orders tends to be distorted and misguides suppliers in their inventory and production decisions. They also term the phenomenon as bullwhip effect 8 that the variance of the orders a firm placed is larger than that of s sales, and the distortion tends to increase as one moves upward from customers to suppliers, indicating that the production efficiency information plays a more important role in the information transmission than sales 6 Kale and Shahrur (2007) found that a firm s leverage is posively related to the concentration levels in s supplier and customer industries. In addion, firms that deal wh R&D-intensive suppliers (customers) and firms wh high intensies of strategic alliances and joint ventures wh suppliers (customers) have lower cred risk. 7 For the main findings of these three studies (Chen et al., 2013a; Chen et al., 2013b; Chen et al., 2014b), please refer to the footnote 5. 8 Lee et al. (1997a, 1997b) analyzed four sources of the bullwhip effect: demand signal processing, rationing game, order batching, and price variations. Actions that can be taken to migate the detrimental impact of this distortion are also discussed. 4

6 information in a supply chain relationship. In addion, Davis (1993) demonstrates that demand, manufacturing process, and supply uncertainties are three main sources of supply chain uncertainty. Moreover, manufacturing process uncertainty is affected by demand and supply uncertainty (Davis, 1993). Since production efficiency is an essence of manufacturing process, manufacturing process uncertainty could be viewed as production efficiency uncertainty (also called as technological uncertainty). Therefore, production efficiency uncertainty plays an important role in the supply chain uncertainty. Based on the above discussions, the PEUs of a firm's business counterparties seem to be posively related to s information flow risk, inventory flow risk (e.g. manufacturing process uncertainty), and therefore cash flow risk and firm asset value uncertainty (risk). Although suppliers / customers PEU seems to increase a firm s asset value uncertainty (risk) through the business relationship, Huang et al. (2014) show that the production efficiency uncertainty (technological uncertainty) enhances the posive effect of supply chain integration on supplier s performance. 9 Their findings suggest that suppliers PEU brings themselves high operating performance and benefs their business counterparties (e.g. the firm, one of the suppliers customers) by value enhancement or risk reduction in cash flows, inventory flows and information flows. In addion, Chiang and Feng (2007) find that supply uncertainty has significant impact on the value of information sharing for the upstream business counterparties, while the impact is relatively insignificant for the downstream ones. 10 The above results reveal that the uncertainty characteristics of suppliers (e.g. production efficiency uncertainty and supply uncertainty) may bring a firm (one of the suppliers customers) more benefs (value enhancement or risk reduction) because of 9 Huang et al. (2014) show that supply chain integration has a significant posive effect on the suppliers performance. In addion, Huang et al. (2014) also show that the demand uncertainty weakens the posive relationship between supply chain integration and supplier s performance. 10 Chiang and Feng (2007) find that information sharing is more beneficial for the manufacturer than for the retailers in the presence of supply uncertainty and demand volatily. The value of information sharing to the retailers is relatively insensive to the production yield uncertainty. 5

7 information sharing or supply chain integration. Moreover, for the customer side, demand uncertainty is commonly regarded as the most severe type of risks among the three sources of supply chain uncertainty, arising from demand volatily or inaccurate forecasts (Davis, 1993; van der Vaart et al., 1996; Gupta and Maranas, 2003). Different from the consequences of suppliers uncertainty characteristics, customers uncertainty characteristics may bring a firm (one of the customers suppliers) more costs (value reduction or risk increases) due to the cash flow variations caused by demand uncertainty. Summarizing the above discussions, this study hypothesizes that the PEUs of a firm s business counterparties affect the firm s cred risk (or bond yield spreads) based on the structural form cred models of Merton (1974) and Duffie and Lando (2001). Furthermore, the PEUs of customers are posively related to firm s cred risk while those of suppliers have uncertain effects because they are affected by the two oppose effects, the supply chain uncertainty (Davis, 1993) and the supply chain integration (Huang et al., 2014) or the value of information sharing (Chiang and Feng, 2007). This study empirically investigates the effects of the PEUs of a firm s suppliers and customers on s bond yield spreads when controlling for well-known variables affecting corporate cred risk, such as (operating) cash flow volatily, leverage, equy volatily, matury, coupon, issuance amount, cred rating, information asymmetry, R&D intensy, and a firm s industry concentration, by employing a preliminarily screened sample of 1,733 yearly bond observations wh supplier identifications and 559 ones wh customer identifications among manufacturing firms from the year 1997 through This study finds that both suppliers and customers PEUs play an important role in explaining a firm s bond yield spreads. When controlling for well-known variables, firm bond yield spread decreases bps while increases bps for per standard deviation increase in suppliers and customers PEU (estimated by the standard deviations of their previous six-year total factor 6

8 productivy (later denoted as TFP) data, respectively. The customer-side result reveals that an increase in customers PEU raises the supply chain uncertainty (demand uncertainty), a firm s sales (or cash inflow) uncertainty and therefore the firm s cred risk. This is consistent wh the hypothesis based on Davis (1993), van der Vaart et al. (1996), Gupta and Maranas (2003), Chen et al. (2014a) and Tsai (2008). However, the supplier-side result reveals that the benefs of supply chain integration (Huang et al., 2014) or the value of information sharing (Chiang and Feng, 2007) exceed the costs of supply chain uncertainty (Davis, 1993). That is, an increase in suppliers PEU raises suppliers operating performance (Huang et al., 2014), benefs a firm in terms of value enhancement or risk reduction, and therefore reduces the firm s cred risk. The current study also finds that the effects of suppliers / customers PEU on bond yield spreads are significantly affected by the information flow risk whin the supply chain. In addion, the effect of customers PEU on bond yield spreads becomes weaker during the cred crisis period, whereas that of suppliers is not affected. These empirical results are robust when controlling for potential simultaney, endogeney problems and employing an alternative sample which consists of the bond observations wh both supplier and customer identification information. The remainder of this paper is organized as follows. Section 2 introduces the measures of suppliers / customers PEUs. Section 3 presents the hypotheses. Section 4 summarizes other major variables used in the empirical examinations. Section 5 presents and analyzes empirical results. Finally, section 6 provides concluding remarks. 2. Measuring Suppliers / Customers Production Efficiency Uncertainties Following the method of Chen et al. (2014a), this study directly uses the volatily of total factor productivy (later denoted as TFPV) as the proxy for PEU. Later, this work follows the similar ways of Kale and Shahrur (2007) to develop the measures of a firm s suppliers and customers PEUs. 7

9 2.1. A firm s production information uncertainty This research follows Chen et al. (2014a) to employ the model specification of Schoar (2002) and the definions of factor input variables of Brynjolfsson and Ht (2003) to estimate a firm s TFP and uses the TFP volatily (TFPV) as the firm s production efficiency uncertainty (i.e. the PEU). In Eq. (1), Y ijt, L ijt, K represent the sales outputs, labor inputs and capal inputs for ijt firm i in industry j at year t. The coefficients of labor inputs and capal inputs vary by industry and year. The model specification allows for different factor loadings in different industries and years. The TFP measure for each individual firm is the estimated residual from these regressions. Y ijt a jt bjt ln Lijt c jt ln( Kijt) ijt ln (1) According to Brynjolfsson and Ht (2003), the definions of (Y, L, K) are as follows. The sales outputs are the total sales deflated by Consumer Price Index (later denoted as CPI). The labor inputs are the labor expenses deflated by Employment Cost Index (later denoted as ECI). Labor expenses are eher calculated as a sector-average labor cost per employee multiplied by total employees or directly taken from COMPUSTAT. 11 The data of sector-average labor cost per employee and employment cost index are both obtained from the Bureau of Labor Statistics (later denoted as BLS). The capal inputs are estimated by the gross value of property, plant and equipment (PPE) deflated by the Producer Price Index (later denoted as PPI). According to the previous discussions, the fluctuations in a firm's TFP represent the 11 Following Brynjolfsson and Ht (2003), this study also assumes 2040-hour work year for each employee. In addion, the average sector labor cost is computed using annual sector-level wage data (salary plus benefs) from the Bureau of Labor Statistics (BLS) from 1993 to

10 firm s technology variations and production efficiency uncertainty. The current study employs TFP volatily (later denoted as TFPV) to express the variation in production efficiency (i.e. the PEU), including the fluctuations in inventory and productivy and inconsistence in production qualy. The firm s TFP volatily is calculated for the six years prior to the end of each year Definions and measures of suppliers / customers production information uncertainty To estimate suppliers / customers TFPVs in firm level, we firstly follow Kale and Shahrur (2007) to classify a firm s suppliers and customers by using their firm-level identification method that identifies a firm s customers and suppliers by employing the COMPUSTAT industry segment files database. We construct firm-specific measures of business counterparties PEUs after completing the identifications of a firm s customers and suppliers. 12 For the identification procedure of a firm s business counterparties, we use a manual procedure to identify the supplier and customer firms of the sample firms because the COMPUSTAT s industry segment files only report the names of the customers and sometimes only the abbreviated versions of the names. In addion, we exclude the firms that are headquartered in foreign countries, financial and utily firms, firms in the retail and wholesale industries, and firms whose main supplier/ customer is a retailer or a wholesaler from the sample. The screening creria and the identification procedure of a firm s business counterparties are similar to that used in Fee and Thomas (2004), Kale and Shahrur (2007), and Chen et al. (2013a; 2013b). In the following, we present the ways to construct the PEUs of a firm s business counterparties. We construct a weighted average of the PEUs of a firm s customers to represent customers PEU (C_TFPV, shown as Eq. (2)) because a firm may have multiple customers in 12 In practices, although public firms have to disclose the identy of any customer that contributes at least 10% to the firm s revenues due to the requirement of the Statement of Financial Accounting Standards (SFAS) No. 14 and 131, some firms also choose to report customers that contribute less than 10% in fact. 9

11 a given year. In Eq. (2), sales to the j th customer, C _ PS j (Customer Percentage Sold j ) is the percentage of the firm s C _ TFPV j (Customer TFPV j ) is the PEU level of the j th customer, and m is the number of customer firms. m C _ TFPV C _ TFPV C _ (2) j 1 j PS j Similarly, a firm may have multiple suppliers in a given year. Hence, we employ Eq. (3) to compute the PEUs of a firm s suppliers (S_TFPV) for each firm wh multiple suppliers. In Eq. (3), C _ ICj (Customer Input Coefficient j ) is the ratio of the firm s purchases from the j th supplier to the firm s total sales, S _ TFPVj (Supplier TFPV j ) is the PEU level of j th supplier, and n is the number of suppliers. n S _ TFPV S _ TFPV C _ (3) j 1 j IC j 3. Hypotheses Developments This section proposes the hypotheses that state the relationships between a firm's suppliers / customers PEUs and s cred risk. The hypotheses base upon the following five arguments: Davis s (1993) model of supply chain uncertainty (demand uncertainty, manufacturing process uncertainty, and supply uncertainty), benefs of suppliers PEU because of information sharing or supply chain integration (Chiang and Feng, 2007; Huang et al., 2014), Chen et al.'s (2014a) contention on the effects of PEU on firm cred risk, Tsai's (2008) theory of cash flow risk among business counterparties, and structural form cred models of Merton (1974) and Duffie and Lando (2001). 13 We develop the hypotheses as the following: 13 The fact that production information is less disclosed in financial statements, and the stylized fact that the variance of production exceeds the variance of sales wh demand uncertainty (Kahn, 1987). 10

12 Main Hypothesis: Given a firm s production efficiency uncertainty, s suppliers / customers production efficiency uncertainties affect the firm s cred risk (bond yield spreads). Production efficiency uncertainty, the uncertainty in a firm s manufacturing process (e.g. inputs and outputs), is one of main sources of supply chain uncertainty. Based on the model of Davis (1993), PEU (e.g. manufacturing process uncertainty) is affected by supply chain uncertainty, including demand and supply uncertainty. In addion, Kahn (1987) demonstrates that the variance of production exceeds the variance of sales wh demand uncertainty and Lee et al. (1997a, 1997b) contend that the information transferred by supply chains tends to be distorted and may misguide upstream counterparties in their inventory and production decisions. Based on the above discussions, PEU therefore plays an important role in the supply chain uncertainty. Since the uncertainties (such as the risks of information flow, inventory flow, and cash flow) in the supply chain would be transmted from one business counterparty to others (Davis, 1993; Lee et al., 1997a, 1997b; Tsai, 2008), suppliers / customers PEUs seem to affect a firm s asset value distribution (e.g. asset value and asset volatily), especially in the increase of the firm s asset volatily. In addion, Chen et al. (2014a) also demonstrate that PEU is posively related to firm asset volatily and therefore corporate cred risk (bond yield spreads) from structural form cred model perspectives. Therefore, we conjecture that suppliers / customers PEUs affect a firm s bond yield spreads based on structural form cred models of Merton (1974) and Duffie and Lando (2001). Sub-Hypothesis 1: Given a firm s production efficiency uncertainty, s customers production efficiency uncertainties are posively related to the firm s cred risk (bond yield spreads). Davis (1993) shows that demand uncertainty is commonly regarded as the most severe type of the three sources of supply chain uncertainty, arising from volatile demand or inaccurate forecasts. The similar opinions are also seen in van der Vaart et al. (1996) and 11

13 Gupta and Maranas (2003). Accordingly a firm's customers uncertainty characteristics may be harmful for the firm in terms of value reduction or risk increase, which mainly result from cash inflow uncertainty. Therefore, demand uncertainty acts as the main channel for the effects of customers uncertainties on a firm s value and cred risk. Based on the above discussions, this study therefore conjectures that customers PEU is posively related to a firm s cred risk (bond yield spreads). Sub-Hypothesis 2: Given a firm s production efficiency uncertainty, the effects of s suppliers production efficiency uncertainties on the firm s cred risk (bond yield spreads) are uncertain. According to the main hypothesis, suppliers PEUs seem to increase a firm s asset value uncertainty (risk) through the supply chain relationship. However, some studies document that the supplier-side PEU (technological uncertainty) have posive effects on suppliers values or performance via information sharing (Chiang and Feng, 2007) or supply chain integration (Huang et al., 2014), suggesting that a firm's suppliers PEU may bring them high operating performance and further benefs the firm by value enhancement or risk reduction in s cash flows, inventory flows and information flows. Based on the above discussions, this study hypothesizes that a firm's suppliers PEUs have uncertain effects on the firm cred qualy due to the two oppose effects, the negative one of supply chain uncertainty (Davis, 1993) and the posive one resulting from supply chain integration (Huang et al., 2014) or information sharing (Chiang and Feng, 2007) 4. Data and Methodology The data for estimating PEU (TFP volatily) is obtained from COMPUSTAT and BLS databases. The data of corporate bond characteristics, including yield spreads, issued amount, coupon rate, issue date, and Moody s bond rating of each bond, are obtained from Datastream. 12

14 The major difference of this study from previous studies (Yu, 2005; Chen et al., 2011) in the selection creria of corporate bond samples is that we restrict the sample bonds to those issued by manufacturing firms (two-dig SIC code ranges from 20 to 39) because in general their production function plays a more important role in the firm value creation than that of other industry firms. In addion, bonds wh the following characteristics are removed: wh embedded options (such as convertible or callable bonds), floating rate coupons, secured, government guaranteed, and wh special clauses. In sum, the sample observations only include U.S. manufacturing firms' straight bonds wh fixed coupon payment and collateralized by firm assets. Moreover, the screened bond sample observations must have suppliers or customers identifications from the original bond sample. In addion, this study controls for other well-known spread determinant variables in the empirical investigations. Most of the data sources for these variables are from COMPUSTAT (e.g. financial data), CRSP (e.g. equy market data), and TAQ (e.g. intraday trading data) databases. After deleting samples wh invalid and missing data, the sample includes 1,733 annual bond observations having supplier identifications and 559 annual bond observations having customer identifications during the sample period from the year 1997 to Approximately 83% and 77% are investment grade bonds for those having suppliers and customers identifications, respectively. Table 1 shows the distribution of the sample observations. The sample size increases gradually each year. [Insert Table 1 here] 4.1. Measures of production efficiency uncertainty effects of suppliers and customers This study employs C_TFPV and S_TFPV shown in Eq. (2) and (3) as the measures for customers and suppliers PEUs. We employ the standard deviations of previous six-year total factor productivy data of customers and suppliers as the proxies for C_TFPV and S_TFPV, respectively. 13

15 4.2. Bond yield spreads and other well-known control variables We employ bond yield spread as the dependent variable and use several control variables to investigate whether or not a firm's suppliers' and customers' PEUs addionally explain the firm's bond yield spread variations. Following Yu (2005), we define bond yield spreads (YS) as the yield difference between the corporate bond yield and the yield of an equivalent matury Treasury bond. The YS data used in this study are obtained from Datastream. The following lists other well-known determinant variables for cross-sectional corporate bond yield spread variations in the lerature, including firm characteristics and bond features. The firm characteristic variables mainly include firm leverage, equy volatily, information asymmetry and cash flow volatily. The variable of firm leverage (LEV) is the ratio of book value of debt to the sum of book value of debt and market value of equy. Collin-Dufresne et al. (2001) show that the leverage ratio variable is posively associated wh bond yield spreads. For the equy volatily (VOL) variable, Campbell and Taksler (2003) document that is posively related to bond yield spreads. The VOL variable is the annualized standard deviation of daily stock returns over the previous 150 days. For the information asymmetry measure, we employ the ADJPIN variable (Duarte and Young, 2009) as the main proxy which is calibrated from a stock's bid and ask trading prices. 14 A higher value of ADJPIN variable represents that the level of incomplete information of a firm is higher. Duffie and Lando, (2001), Yu (2005), and Lu et al. (2010) also document that ADJPIN has a significant and posive impact on firm cred risk. Other control variables of firm characteristics include a firm s operating cash flow risk (later denoted as CFV), R&D intensy (RD), and industry concentration level (HHI), defined as the standard deviation of the operating cash flow calculated over previous twelve quarters, 15 the ratio of total research and development 14 For the estimation of the ADJPIN variable, this work follows the EM-algorhm first developed by Chen and Chung (2007) (later followed by Lu et. al., 2010). 15 The operating cash flow in this study is defined as the ratio of the sum of operating cash flow in financial report and interest expense to total asset market value. 14

16 expendures to total assets, and sales-based Herfindahl Index of a firm s primary industry, respectively. Bond feature variables include coupon rate (Coupon), life to final date (LFFL), bond age (Bage), amount issued (Lnamt), and cred rating (RAT). The variables of Coupon, LFFL, Bage, Lnamt, and RAT are the annual payable rate on a bond in percentage, the remaining years from time t to the bond matury date, the difference between the settlement date and the issuing date, the logarhm of the dollar amount originally issued, and Moody s issuer rating for each bond, 16 respectively. Previous studies show that the former three variables are posively related to bond yield spreads while the fourth one has an oppose effect. The summary statistics of the above variables of the sample bond observations wh issuing firms' supplier identifications and customer identifications are shown in panel A and B of Table 2, respectively. [Insert Table 2 here] 5. Empirical Analysis The current study performs the following empirical analyses. First, we employ panel data regressions wh different model settings to scrutinize a firm s suppliers / customers PEU effects on s bond yield spreads. Second, to provide more robust evidences, we investigate whether the PEUs of a firm s suppliers and customers affect s bond yield spreads when controlling for other determinant variables well known in the lerature. Finally, we conduct the robustness examinations wh a different sample that simultaneously has both suppliers and customers identifications Preliminary examinations on the relations between bond yield spreads and the production information uncertainties of a firm s suppliers and customers. 16 For the RAT variable, s value is set to 1 for bonds wh Aaa rating, 2 for Aa1, 3 for Aa2, and so on. 15

17 This section examines the PEUs of a firm s suppliers and customers on the firm s corporate bond yield spreads by estimating the panel data regressions as shown in Eq. (4) and (5), controlling for firm and year fixed effects. YS 1 S _ TFPV 2TFPV (4) YS 1 C _ TFPV 2TFPV (5) Table 3 provides the results of Eq. (4) and Eq. (5) for the entire sample period. This table exhibs the results of four different regressions wh the bond yield spread (YS) as the dependent variable against various combinations of explanatory variables. These regressions cover 1,733 and 559 observations for suppliers and customers, respectively, from the year 1997 to Panel A show that suppliers PEU (S_TFPV) is significantly and negatively related to bond yield spreads. Panel B shows that customers PEU (C_TFPV) is significantly and posively related to bond yield spreads. The customer-side result reveals that an increase in customers PEU raises the supply chain uncertainty (demand uncertainty), a firm s sales (or cash inflow) uncertainty and therefore the firm s cred risk. This is consistent wh the hypothesis based on Davis (1993), van der Vaart et al. (1996), Gupta and Maranas (2003), Chen et al. (2014a) and Tsai (2008). The effect of customers PEU on firm cred risk (bond yield spreads) is the same as those of customers information asymmetry (Chen et al., 2013a) and internal liquidy risk (Chen et al., 2013b) on firm cred risk. However, the supplier-side result reveals that the benefs of supply chain integration (Huang et al., 2014) or the value of information sharing (Chiang and Feng, 2007) exceed the costs of supply chain uncertainty (Davis, 1993). That is, an increase in suppliers PEU raises suppliers operating performance (Huang et al., 2014), benefs a firm in value enhancement or risk reduction, and therefore reduces the firm s cred risk. 16

18 [Insert Table 3 here] 5.2. Further examinations on the relations between bond yield spreads and the production efficiency uncertainties of a firm s suppliers and customers. To examine the robustness of previous results, we addionally consider other well-known control variables introduced in previous section to reexamine the PEU effects of a firm s suppliers and customers on s bond yield spreads, shown as Eq. (6) and (7): YS LEV RAT HHI 7 TFPV 13 1 VOL 8 S 14 2 Coupon LFFL 3 RD 9 _ TFP 15 ADJPIN 10 S _ TFPV 4 Bage Lnamt CFV 11 5 TFP 12 6 (6) YS LEV RAT HHI 7 TFPV 13 1 VOL 8 C 14 2 Coupon LFFL 3 RD 9 _ TFP 15 ADJPIN 10 4 C _ TFPV Bage Lnamt CFV 11 5 TFP 12 6 (7) The results of model (2) and (4) of Table 4 reveal that the PEU of a firm s suppliers is negatively related to the firm s bond yield spreads when controlling for the firm s PEU and other well-known variables. The result of model (2) shows that firm bond yield spreads decrease bps ( ) per standard deviation increase in suppliers PEU. The results of model (2) and (4) of Table 5 reveal that the PEU of a firm s customers is posively related to the firm s bond yield spreads when controlling for the firm s PEU and other well-known variables. The result of model (2) shows that firm bond yield spreads increase bps ( ) per standard deviation increase in customers PEU. These results are similar to those of previous investigations. [Insert Table 4 here] [Insert Table 5 here] 17

19 5.3. Robustness check The results using the sample of bond observations wh both supplier and customer identification information To examine the robustness of these results, we employ the sample of bond observations wh both supplier and customer identification information to further examine how PEUs of suppliers and customers simultaneously affect a firm s cred risks, shown as Eq. (8) YS LEV RAT HHI 7 TFPV 13 1 VOL 8 S 14 2 Coupon LFFL 3 RD ADJPIN _ TFP 15S _ TFPV 16C _ TFP 17 4 Bage Lnamt 5 CFV 6 TFP C _ TFPV (8) The results of models (1) to (3) of Table 6 reveal that customers PEU is posively related to firms bond yield spreads whereas suppliers has an oppose effect. The results are similar to those of previous investigations and again reveal that suppliers / customers PEU is important in explaining firm bond yield spreads. [Insert Table 6 here] Discussions on the endogeney of the relation between suppliers / customers production efficiency uncertainty and firm cred risk (bond yield spreads) In the previous section, we explore how suppliers / customers PEU affects firm cred risk (bond yield spreads). However, could be challenged that firm cred risk may determine suppliers / customers PEU. In other words, suppliers / customers PEU and firm bond yield spreads may be determined simultaneously. In this case, the estimated regression coefficients of the regressions wh the firm s bond yield spreads regressed against suppliers / customers PEU variables may be biased because the error terms are correlated wh the independent variables. We apply the simultaneous equations model to address this issue as Eq. (9). We 18

20 simultaneously estimate a system of two simultaneous equations for suppliers / customers PEU variables, including the firm s bond yield spread regressions and another regression in which the suppliers / customers PEU proxy is the dependent variable while the firm s bond yield spreads and other control variables served as independent variables. YS a 0 1S _ TFPV( C _ TFPV) LFFL Lnamt RAT ADJPIN RD HHI 6 CFV TFPV i LEV VOL FIRM YEAR 2 9 t 3 Coupon Bage S _ TFPV ( C _ TFPV ) c c YS c RD c HHI c TFPV FIRM YEAR 8 1 c LEV c VOL 2 i 3 t c ADJPIN c CFV 4 5 (9) Where YS is firm bond yield spreads; TFPV (S_TFPV; C_TFPV) stands for a firm s (suppliers / customers ) PEU. LEV, VOL, ADJPIN, RD, HHI, and CFV are the leverage ratio, equy volatily, estimated probabily of information trading (Duarte and Young, 2009), R&D intensy, industry concentration level, and cash flow volatily. LFFL, Coupon, Bage, Lnamt, and RAT stand for time to matury, annualized coupon rate, bond age, the natural log of amount issued, and bond rating, respectively. FIRM is a firm fixed effect while YEAR is a time fixed effect. 17 Table 7 shows the results of the simultaneous regressions for the suppliers / customers PEU proxies, S_TFPV and C_TFPV, respectively. The results of models (1) to (8) in Table 7 show that customers PEU variables are significantly and posively related to corporate cred risk (bond yield spreads) while suppliers ones have an oppose effect. These results provide further support for our findings that suppliers PEU is significantly and negatively related to corporate cred risk (bond yield spreads) while customers PEU is significantly and 17 This study considers the possible econometric problem that the error terms in the system regressions could have cross-equation contemporaneous correlations by employing the seeming unrelated regressions to estimate the variance-covariance matrix. 19

21 posively related to. [Insert Table 7 here] In addion, the correlation between the independent variables (suppliers / customers PEU proxies) and the error terms of bond yield spreads could be due to the omted variable problem or measurement errors. To address this econometric issue, we apply a two-stage regression methodology to examine the issue. In the first stage, we regress suppliers / customers PEU proxy against the explanatory variable (including the instrument variables) and estimated the predicted values of suppliers / customers PEU proxy. In the second stage, we regress bond yield spreads against the predicted values of the first stage and other control variables. This study uses the previous one-year suppliers industry concentration level (S_HHI) and customers TFPV (C_TFPV) as the instrument variables for the PEU variables of suppliers and customers, respectively. 18 Table 8 presents the results of the two-stage regressions. The coefficients of suppliers / customers PEU variables are significant and negative/ posive in the regressions wh bond yield spreads (YS) as dependent variable. The results of our two-stage regression estimations are broadly consistent wh those of the simultaneous equations. Based on the above results, after controlling for potential simultaney and endogeney problems, we find that our previous results are robust. [Insert Table 8 here] Employing alternative estimation period (from past six years to seven years) for 18 The lagged variable is commonly employed as one candidate of instrument variables (Sovey and Green, 2010). In this study, the lagged variable has a relatively low correlation wh the error term and a greater correlation wh current PEU proxies of suppliers and customers. In addion, due to the past one-year S_TFPV as a weak instrument variable for suppliers PEU proxy, this study employs the past one-year S_HHI variable as another appropriate instrument variable for suppliers PEU proxy. 20

22 calculating business counterparties PEUs by other estimation period To provide more robustness evidences for the significance of the PEUs of a firm s suppliers and customers on s bond yield spreads, this section presents the empirical examinations wh an alternative estimation periods for calculating a firm s PEU, changing from past six years to seven years. The results of model (1) and (2) in Table A1 show that customers PEU is still significantly and posively related to the firm s bond yield spreads when controlling for other well-known variables, whereas suppliers PEU still has the oppose effect. The above results again provide robust evidences for the importance of business counterparties PEUs in explaining a firm's bond yield spreads. Because the results of different estimation periods are similar, this study only shows the results wh PEU estimated by past seven years due to the limed space Addional Findings Further discussions on the effects of suppliers / customers production efficiency uncertainty on bond yield spreads: The cred crisis perspective This section examines how cred crisis condion (the dummy variable of year 2008, later denoted as D_CS) affects the effects of suppliers / customers PEU on bond yield spreads in the sample period by the panel data regressions, controlling for firm- and year fixed effects, shown as Eq.(10) and (11) : YS LEV RAT HHI 7 TFPV S 13 1 VOL Coupon LFFL Bage Lnamt _ TFPV 15D _ CSt 16 RD ADJPIN CFV S _ TFPV D _ CS TFP 12 6 t (10) 21

23 YS LEV RAT HHI 7 TFPV C 13 1 VOL Coupon LFFL Bage Lnamt _ TFPV 15D _ CSt 16 RD ADJPIN CFV C _ TFPV D _ CS TFP 12 6 t (11) The results of model (3) and (4) in Table 9 show that the interaction term of customers PEU (C_TFPV) and cred crisis condion (D_CS) is significantly and negatively related to corporate bond yield spreads. The result reveals that the effect of customers PEU on bond yield spreads becomes weaker when cred crisis condion occurs. The results of model (1) and (2) in Table 9 show that the interaction term of suppliers PEU (S_TFPV) and cred crisis condion (D_CS) is insignificantly related to corporate bond yield spreads. The results reveal that the effect of suppliers PEU on bond yield spreads is insignificantly affected by the cred crisis condions. The above results show that distressed economic condions play a more important role on a firm's customer-side PEU effect than s supply-side one. [Insert Table 9 here] The discussions on the effects of suppliers / customers production efficiency uncertainty on bond yield spreads: The information flow risk perspective Chen et al. (2013a) demonstrate that suppliers and customers degrees of information asymmetry are significantly and posively related to firm bond yield spreads. The finding reveals that the information flow risk along the business counterparties plays an important role in determining corporate cred risk. Therefore, we investigate whether the information flow risk (measured by the suppliers / customers ADJPIN variables defined in Chen et al. (2013a)) affects the effects of the suppliers / customers PEU on bond yield spreads by the panel data regressions shown as Eq.(12) and (13), controlling for firm- and year fixed effects: 22

24 YS LEV RAT HHI 7 TFPV S 13 1 VOL Coupon LFFL Bage Lnamt 3 RD 9 10 _ TFPV 15S _ IA 16 4 ADJPIN CFV 11 5 S _ TFPV S _ IA TFP 12 6 (12) YS LEV RAT HHI 7 TFPV C 13 1 VOL Coupon LFFL Bage Lnamt 3 RD 9 10 _ TFPV 15C _ IA 16 4 ADJPIN CFV 11 5 C _ TFPV C _ IA TFP 12 6 (13) The results of models (1) and (2) in Table 10 show that the interaction term of suppliers PEU and suppliers information asymmetry is significantly and negatively related to corporate bond yield spreads. The result reveals that suppliers information asymmetry enhances the negative effect of the suppliers PEU on bond yield spreads. The possible explanation is that the benefs of the suppliers PEUs on their own performances and their customers (the firm is one of the suppliers customers) firm values which result from supply chain integration (Huang et al., 2014) and information sharing (Chiang and Feng, 2007) are higher when suppliers' information asymmetry is higher. However, the results of models (3) and (4) in Table 10 show that the interaction term of customers PEU and customers information asymmetry is significantly and posively related to bond yield spreads. The result reveals that customers information asymmetry augments the posive effect of the customers PEU. The possible explanation is their information asymmetry increases the firm's demand uncertainty according to Davis s (1993) model. The above results reveal that information flow risk whin the supply chain significantly affects the effect of suppliers / customers PEU on a firm's bond yield spreads. [Insert Table 10 here] 6. Concluding Remarks The recent financial tsunami aggravated by the contagion effects among business related 23

25 firms reveals that individual firm s risk is transmted through the business relationship along a supply chain which connects the variations of inventory flows, cash flows and information flows among business counterparties. This is the first study to explore the PEU effects of a firm s suppliers and customers on s bond yield spreads by using American data from the year 1997 to Empirical results of this study show that customers PEU is posively related to firms bond yield spreads whereas suppliers has an oppose effect. The customer-side result reveals that an increase in a firm's customers PEU raises the supply chain uncertainty (demand uncertainty), the firm s sales (or cash inflow) uncertainty and therefore s cred risk. However, the supplier-side result reveals that the benefs of supply chain integration (Huang et al., 2014) or the value of information sharing (Chiang and Feng, 2007) exceed the costs of supply chain uncertainty (Davis, 1993). In addion, the effects of suppliers / customers PEU on bond yield spreads are significantly affected by the information flow risk whin the supply chain. Furthermore, the effect of customers PEU on bond yield spreads becomes weaker during the cred crisis condion, whereas that of suppliers is insignificantly affected. The above results are robust when addionally controlling for cred ratings and potential simultaney and endogeney problems. We conclude that business counterparties' PEU helps tradional structural models explain corporate cred risk (and also corporate bond yield spreads). 24

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