The Impact of Macroeconomic Risk Factors, the Adoption of Financial Derivatives on Working Capital Management, and Firm Performance

Abstract

This study examines macroeconomic risk factors to investigate how they affect working capital management (WCM) and, ultimately, firm performance. Additionally, we examine the effect of credit default swaps (CDSs) as a countermeasure for WCM in the presence of volatile macroeconomic risk factors. In doing so, we use firm-level data from the United States, the United Kingdom, Germany, and China between 2006 and 2020. The two-step system generalized method of moments (GMM) estimation method is employed to analyze the study′s objectives. Results show that US, German, and Chinese firms are more conservative, while UK firms are more aggressive in maintaining WCM during economic policy uncertainty. Conversely, foreign exchange risks drive the USA, the UK, and Chinese firms to lengthen their cash conversion cycle level due to fear of value loss, while the opposite is true for German firms. Nevertheless, following CDS adoption, firms are more confident in working capital (WC) investment. CDSs eliminate the need for delayed receivables and payables and increased inventory as safety stock for US, UK, and Chinese firms. Finally, CDS interaction shows that USA, UK, and German firms may boost their profitability by increasing account receivable periods to create more sales, reducing account payable periods, and holding more inventories to expedite sales operations. Alternatively, CDSs suggest an optimal level of WC investment for Chinese firms. As a result, governments should consider CDS adoption in policy decisions when business performance sinks due to macroeconomic volatility.

1. Introduction

In recent years, businesses have paid enormous attention to working capital management (WCM), which concerns the firm′s overall financial efficiency and health [1,2]. The term “WCM” is used to describe the practice that ensures a firm has enough cash on hand to pay for its day-to-day operations and its short-term obligations. Finding adequate cash levels, issuing credit, managing inventory, converting accounts receivable and inventories into cash, paying accounts payable on time, ensuring access to bank credit lines, investing in highly liquid securities, determining the most effective means of raising cash, and renegotiating long-term liabilities are all part of WCM [3].

Effective working capital management typically includes the successful management of its core components, such as inventories, accounts receivable, and accounts payable. When inventories are not saleable, receivables are not collectible, and payables cannot be paid on time, it is catastrophic for businesses. When a firm fails to effectively manage all of these components, the cash conversion cycle (CCC) is adversely impacted. Hence, WCM decisions need a meticulous strategy to guarantee that inventories, accounts receivable, and accounts payable are all at their optimum levels. However, most companies struggle constantly with improving and maintaining the efficiency of their working capital management. The situation worsens when businesses must contend with a wide range of macroeconomic risk factors. As such, the primary purpose of the current research is to analyze how economic policy uncertainty (EPU) and foreign exchange risk (FX risk), two of the most significant macroeconomic risk factors, affect WCM, and thereby firm performance.

As for EPU, it represents uncertainty over economic policies such as monetary, fiscal, and regulatory policies [4,5]. Concerns about EPU have grown significantly as a result of a succession of events, including the emergence of COVID-19, the trade war between the United States of America (USA) and China, and the Brexit negotiations between the United Kingdom (UK) and European Union (EU) nations [6]. Nevertheless, incorporating new economic policies may drastically alter the business climate. Instability in government policy, for instance, might affect business decisions. EPU leads to recessions, which in turn limit investments, strain the financial sector, impact consumer demand for products and services, and wreak havoc on the supply chain and firms’ productivity [7].

Furthermore, as EPU increases, it induces economic uncertainty and opaqueness [8]. Borrowers who are already struggling financially may not be able to repay their debts. Banks suffer a significant liquidity risk since the banking industry is highly subject to economic policy decisions. As a result, banks tend to limit credit to borrowers and also increase loan prices to cover default risk costs [9]. Consequently, this scenario raises the cost of capital and reduces firms’ access to external finance, putting strain on their working capital. The result is an increase in the demand for accounts receivable from financially challenged customers. In addition, when EPU is high, forecasting operational expenditures, especially those related to inventory demand, becomes more difficult for businesses, resulting in a rise in daily inventory outstanding [10]. An increase in the inventory turnover cycle leads to an increased inventory backlog and storage costs, negatively affecting the company′s profitability.

In addition to EPU, the volatility of currency markets poses a threat to both local and international businesses in today′s interconnected global economy. It has a major impact on the value of a firm and its ability to generate cash flow, as well as its competitive position in the global market [11]. FX risk is the possibility that the value of a firm’s assets, liabilities, cash flows, or net profit would fluctuate as a result of unanticipated changes in foreign exchange rates. As the value of a company′s receivables is denominated in a foreign currency, it suffers as the value of that currency declines. Therefore, trade receivable cash flows may fall short of what is needed to satisfy short-term commitments and keep the company running smoothly. When a country′s currency declines in value, it raises the amount owed to foreign counterparts and drives up the cost of imported goods. Further, realized foreign currency gains or losses arise when there is a shift in the value of one currency relative to another. This may impact a firm′s net working capital (WC) and the composition of that capital.

However, the magnitude of the aforementioned EPU and FX risk volatility differs across every country. It is true that some countries have to deal with severe consequences, while others face moderate or lesser consequences. In terms of global trade volume, the USA, the UK, Germany, and China are the top four countries, and they are also the biggest trading partners of each other. Since these countries are strongly engaged in international trade, a rise in the volatility of macroeconomic risk factors affects other nations where their businesses operate. Importantly, when macroeconomic instability emerges in an area, the most important or most substantial ramifications are felt by the top trade partners [12]. It is postulated that EPU and FX risk have severe consequences for these countries′ firms′ WCM. If a firm′s WCM components are affected, it may have a negative impact on its profitability. This is because a firm′s profitability will fall if it takes too long to transform its working capital investment into cash from sales.

Nonetheless, businesses may use risk management strategies to guarantee consistent performance regardless of fluctuations in macroeconomic risk factors. This method has the potential to shield businesses from the unintended consequences of EPU and FX risk. Firms are more likely to employ financial derivatives, a risk management tool, to lessen their exposures to business risks when there is a higher degree of uncertainty in macroeconomic risk factors [13]. Of note, financial derivatives are a common strategy for hedging against fluctuations in cash flow. When markets are volatile, this kind of risk management may be very useful. Corporations operating in countries with high EPU might hedge against the risk of customer default by, for example, purchasing credit default swaps (CDSs) [14]. Under the terms of the CDS contract, the CDS seller will compensate the impacted firm if a party is determined to be in default. Thus, firms are not in a situation of credit risk, which assures a lower CCC of the firm. Firms with a low CCC convert sales into profits effectively, leading to strong financial health. As such, the ultimate objective of this research is to investigate the effect of EPU and FX risk on WCM and, eventually, on firm performance, before and after the interaction of financial derivatives with WCM.

This research has theoretical, empirical, and practical significance as a result of its enormous contributions. To begin with, prior studies have shown a correlation between EPU [10,15,16], FX risk [17], and WCM. However, they have not explored potential countermeasures for managing the risks induced by EPU and FX risk in WCM. Instead, the present study will explore financial derivatives (CDS) as an interaction variable of WCM to see whether the interaction results in favorable changes when WCM is affected by EPU and FX risk. Notably, the interaction of CDS with firms’ WCM is nascent. Moreover, with the inclusion of CDS as an interaction variable of WCM, this research adds substantial empirical evidence to the existing body of literature. When firms are preoccupied with macroeconomic risk concerns, there may have been less attention paid to the interplay between CDS and WCM.

The remaining parts will be discussed in the following categories. In Section 2, we will discuss the literature review. The methodology of the study will be discussed in Section 3. The results and discussion will be presented in Section 4. This research paper will conclude with a summary in Section 5.

2. Literature Review

The importance of WCM in determining an organization′s financial success has been widely acknowledged in the literature on corporate finance [18,19,20]. Research also shows that the profitability of a firm might be jeopardized when WCM is strongly influenced by fluctuations in macroeconomic factors [21]. During the global financial crisis, Tsuruta [22] found that firms’ revenues decreased, inventories grew disproportionately, and payments for products and services were delayed. Therefore, a firm′s ability to successfully manage its WC is crucial to its survival in the face of uncertain macroeconomic risk factors.

2.1. Macroeconomic Risk Factors and WCM

2.1.1. EPU and WCM

The management of working capital is profoundly affected by EPU. When there is widespread uncertainty in economic policy, it might disrupt a firm’s WCM efforts. The financial accelerator theory suggests that firms′ access to financing might be severely constrained by an economic shock, affecting investment behavior [23]. Cheng [15] revealed that uncertainty about the direction of economic policy has a negative impact on sales and slows down the turnover of accounts receivable. In addition, when there is a lot of unpredictability in economic policy, it becomes even more important for businesses to accurately predict their inventory needs, which in turn leads to a faster inventory turnover cycle. As a consequence of the decline in the size of the financing market, businesses are finding it harder to get access to money, driving up the demand for credit sales.

While Cheng [15] analyzed Chinese firms, Dbouk et al. [10] found very identical outcomes for 6503 US industrial enterprises between 1990 and 2018. They note that EPU necessitates increased levels of inventories, trade credit, and payables. Their findings imply that businesses must retain more cash in their operations in order to prevent unanticipated catastrophes. On the other hand, Tandoh [24] argues that the presence of EPU leads businesses to have a more aggressive strategy of WCM. In addition, businesses during periods of high EPU intentionally try to stretch out their payables and speed up their receivables.

Furthermore, the relationship between EPU and WCM is implicitly observed in other investigations. Differences in WCM across nations were investigated by Matto and Niskanen [25], who focused on the impact of the legislative framework and financial market development. Their analysis shows that a country′s financial and legal institutions affect WC and trade credit. The degree of trade credit arrangements fluctuates in response to changes in the financial system. Trade credit is an integral part of WCM. It is a kind of selling where the vendor accepts payments at a later date. Increases in trade credit lead to a stagnation of WC [26]. Contrarily, strict credit regulations may reduce a company′s revenue.

Meanwhile, during EPU, firms are more likely to default on their loans [9], leading to a rise in non-performing loans. Nonperforming loans induce banks to accumulate liquidity during times of elevated EPU [27]. As a consequence, investment and cash flow considerations are obscured, hence aggravating firms′ investment decisions [28,29]. Thereby, when there is a lot of uncertainty in the market, businesses behave more cautiously, ultimately delaying investments [30].

2.1.2. FX Risk and WCM

Another key macroeconomic risk factor that generates a great deal of volatility for firms is foreign exchange risk [31]. The level of a firm′s exposure to exchange rate fluctuations is determined by the foreign-currency-denominated assets and liabilities. Domestic companies with no overseas transactions might also be impacted by currency rates as a result of competitiveness with other domestic enterprises [11].

Foreign exchange rate volatility generates uncertainty in firms′ revenues and cash flows and diminishes the firms′ lending capacity [32]. Eventually, it disrupts the firms’ management of WC. Cooper [33] is arguably one of the pioneers who demonstrate the link between FX risk and WCM. The author illustrates that FX risk negatively impacts firms′ current monetary assets and liabilities and non-monetary assets such as inventories. The value of current monetary assets denominated in a foreign currency will fall or rise, respectively, if that currency appreciates or depreciates relative to the domestic currency. For current monetary liabilities, the reverse relationship is true. Further, a translation loss is realized when the foreign currency against which inventories are valued declines in value, whereas a translation gain is realized when the foreign currency rises in value. Therefore, exchange rate fluctuations may have an effect on a company′s operational cash flows.

Consistent with the aforementioned works, Bhattacheryay [17] articulates that a WC crisis occurs when unexpected occurrences alter the expected cash flows of a company. The increased volatility of major currencies threatens the predictability of foreign revenue. Therefore, frequent exchange rate fluctuations impede the free flow of capital. In turn, this impacts the management of WC adversely. Moreover, as exchange rates vary, WC levels fluctuate as well. Consequently, a rise in the exchange rate reduces enterprises′ actual net WC [17]. Similarly, Tunc and Solakoglu [34] and Santillán-Salgado et al. [35] demonstrate that uncertainty in foreign currency rates has a direct impact on the value of multinational enterprises′ overseas investments, input costs, sales, and liabilities. Furthermore, when FX rate volatility impacts enterprises′ cash flows, it reduces company liquidity and leads to an increased cost of capital, financial hardship, and default. Overall, financial stability is jeopardized by foreign currency vulnerabilities.

H1. 

There is a significant relationship between macroeconomic risk factors (EPU and FX risk) and WCM.

2.2. Macroeconomic Risk Factors, WCM, and Financial Derivatives

Financial derivatives and WCM attract limited scholarly attention owing to their obscurity. The majority of finance research focuses on the potential long-term benefits of financial derivatives. Contrarily, this research looks into how financial derivatives influence the WCM when macroeconomic risk factors (EPU and FX risk) are volatile. The current study posits that financial derivatives, a kind of risk management technique [36], may help businesses handle their WCM during times of economic turmoil. In line with this, the hedging theory states that corporate hedging using financial derivatives mitigates the possibility of financial distress costs by reducing the volatility of the firm′s cash flow [11,37].

This study employs CDSs as a financial derivative tool to investigate their interactions with WCM. At the height of the financial crisis, CDSs have become the most widely utilized credit derivative instrument [38]. According to Dodd et al. [39], global market risk factors were the most significant driver of CDS spreads in the financial markets during the GFC. After the Great Recession, the research finds, CDS spreads for corporations skyrocketed due to elevated debt. Liu et al. [40] showed that COVID-19 increased enterprises′ liquidity risk, which stemmed from their inability to satisfy short-term commitments. Firms with maturing debt but no liquidity faced a substantial debt rollover risk. The crisis raised CDS spreads by 349–880 basis points for enterprises in the highest debt-rollover-risk quartile. The shorter the CDS contract maturity for enterprises with significant debt rollover risk, the higher the CDS spread, and signaling investors are more worried about short-term default risk than long-term risk. The crisis′ influence on CDS spreads for high-rollover-risk enterprises became more obvious as the US became the most COVID-19-affected nation.

Markedly, companies that engage in CDS trading, for instance, have been shown by Fuller et al. [41] to have a quicker payment of accounts payable and a faster collection of accounts receivable. They further explain that if CDS trading reduces lenders′ exposure to borrowers′ credit risk, companies would have easier access to credit markets. As a consequence, borrowing companies have less need for trade credit as a result of improved access to cash. In addition, Li and Tang [42] note that the impact of customers′ CDSs on suppliers′ leverage increases as suppliers provide more trade credit to customers. Further, the leverage of a company decreases when a greater proportion of its revenue comes from CDS-referenced clients. CDSs further strengthen the bond between lenders and borrowers by guaranteeing the repayment of debts. This holds true even in the face of a general scarcity of credit. As a result, CDSs have a beneficial effect on company financing [43].

Conclusively, it is postulated that CDSs reduce firms’ WC risk from trade credit and external financial issues caused by macroeconomic risk factors. CDSs might help businesses deal with challenges in their day-to-day operations if they are integrated with WCM. Despite this, the literature on CDS and WCM interactions is sparse. This research aims to explore the connections.

H2. 

There is a change in the relationship between macroeconomic risk factors (EPU and FX risk) and WCM after the interaction of financial derivatives with WCM.

2.3. WCM, Firm Performance, and Financial Derivatives

The significance of the effective management of WC to the overall functioning of a firm is widely recognized by scholars. Poor WC management impairs a firm′s performance. Functional WCM, on the other hand, improves business performance, which boosts the firm′s market value both in absolute and relative terms [20]. However, WCM′s components have a significant role in shaping a firm’s performance. Fernández-López et al. [44] and Sawarni et al. [45] demonstrated that the daily sales outstanding (DSO), daily inventory outstanding (DIO), daily payable outstanding (DPO), and the CCC have a negative relationship with the profitability of businesses. When firms wait longer to turn their investments in WC into cash or repay their debt, this results in substantial financial costs. The profitability of businesses is hampered by excessive finance costs.

Contrarily, Braimah et al. [18] revealed a positive link between the payable period and profitability. The research suggests that a company′s cash flow would be better served by a longer payables term than collections time. Likewise, in Nwude et al.′s [46] research, return on assets (ROA) was shown to be higher when payable and inventory conversion periods are shorter and receivables collection periods are longer. The research by Amponsah-Kwatiah and Asiamah [47] demonstrates that not only ROA but also return on equity (ROE) is positively impacted by account receivables, account payables, the cash conversion cycle, inventory management, current assets, and current ratios. In a different study, Magni and Marchioni [1] illustrate the links between accounts receivable and revenues, accounts payable and operating expenditures, and inventory and sales. Disruptions in any of the components have a detrimental effect on the operation of the business.

Researchers have also found that a lack of liquidity makes it difficult for a firm to function on a day-to-day basis. That is why the trade-off theory suggests that financial managers must strike a balance between liquidity and profitability [48,49]. Without available cash on hand, Afrifa and Tingbani′s [50] research found a negative correlation between WCM and firm performance. Bian et al. [51] emphasize the need for managing WC requirements to reduce bankruptcy risk and maintain operational liquidity. Subsequently, a number of studies [52,53] recommend a shorter CCC to mitigate the negative effects of financial constraints and boost corporate performance.

Nonetheless, firms′ payment terms and initial investment are directly influenced by macroeconomic risk factors. In a number of studies, researchers found that WCM practices were influenced by the economic climate of the nation in which the company was located [3,21,22,54]. As the present research hypothesizes that EPU and FX risk may hinder the firms’ management of WC and profitability, financial derivatives as a risk management instrument may be effective in improving the situation. Multiple scholars have shown a strong connection between financial derivatives and business success [36,55,56,57]. Accordingly, Danis and Gamba [58] indicate that CDSs reduce bankruptcy risk and increase firms’ value.

Therefore, the present research will investigate the following hypotheses based on the preceding discussions.

H3. 

There is a significant relationship between WCM and firm performance before the interaction of financial derivatives with WCM.

H4. 

There is a different impact on the relationship between WCM and firm performance after the interaction of financial derivatives with WCM.

3. Methodology

3.1. Population and Sample Selection

The research sample comprises listed non-financial firms from the USA, the UK, Germany, and China from 2006 to 2020. Non-financial firms are the primary focus of the research since they are more likely to actually utilize accounts receivable, accounts payable, and inventory. Furthermore, financial firms vary from non-financial firms in their financial and accounting practices. The sample size includes 7321 US firms, 1063 UK firms, 635 German firms, and 4513 Chinese firms based on their country of headquarters and exchange. Nonetheless, the main motivation for selecting the USA, UK, Germany, and China is that they are mutually connected on the basis of trading partners; the US–China trade war; Brexit issues between the UK and EU; and the COVID-19 pandemic.

3.2. Data Collection

The research began its data collection by obtaining data on macroeconomic risk factors (EPU and FX risk). Data on the EPU index were collected from the website www.policyuncertainty.com (accessed on 1 September 2022). This website contains the data for Baker et al.′s [4] news-based EPU index. However, data on FX risk were obtained from the International Monetary Fund′s (IMF) International Financial Statistics (IFS) database. The research then collected all of the financial data of the companies in the sample from DataStream (Thomson Reuters Refinitiv Eikon). This study also utilizes DataStream to obtain monthly closing CDS mid-spread data. To clarify, TR CDS indices do not contain CDSs with a restructuring clause.

3.3. Research Variables

3.3.1. Macroeconomic Risk Factors

In this study, we use EPU and FX as the macroeconomic risk factors, which are independent variables of the study. EPU is measured through Baker et al.′s [4] news-based EPU index [9,10]. Meanwhile, the standard deviation of the real effective exchange rate (based on the consumer price index) is used to calculate the FX risk. FX risk is estimated using standard deviation by a number of researchers [59,60,61].

3.3.2. Working Capital Management

Since the CCC is a common indication of WCM, it serves as the measurement standard for this research. The CCC is the time it takes to convert inventory and accounts receivable into cash, less the time it takes to pay suppliers [20,26,46].

CCC = DSO + DIO − DPO

CCC = ($\frac{AccountReceivables}{Sales}\times 365$) + ($\frac{Inventory}{COGS}\times 365$) − ($\frac{AccountPayables}{COGS}\times 365$)

The term “days sales outstanding” (DSO) refers to the number of days it takes to receive payment for goods and services sold. DSO includes accounts receivable as one of its components. Secondly, the average number of days a firm keeps goods in stock before selling them is called days inventory outstanding (DIO). The acronym “DPO” refers to days payable outstanding. DPO represents the average time (in days) a firm takes to pay its trade creditors, such as suppliers, vendors, or financiers.

3.3.3. Firm Performance

Profitability is a frequently used indicator for analyzing a company′s performance since it determines the success or failure of a business. We employ ROA to measure the firms′ profitability. ROA is calculated by dividing net income by total assets [62,63,64].

Return on assets (ROA) = $\frac{NetIncome}{TotalAssets}$.

3.3.4. Financial Derivatives

CDS is used as a financial derivative instrument in this study. The rationale for choosing CDS is that it is connected to the financing decisions of firms [41]. To measure CDSs, the study employs monthly last-day closing CDSs mid-spread as a proxy following the study by [38]. The monthly last-day closing mid-spreads are then converted into yearly average CDS mid-spreads, as shown by Chang et al. [65]. However, we have used CDSs’ interaction with lagged WCM in model 2. Ahangar [66] similarly used the interaction of DFC (degree of financial constraints) with a lagged CCC variable, which enabled them to analyze the influence of financial constraints on the pace of WC adjustment. In model 4, we have used CDSs’ interaction with the independent variable WCM.

3.3.5. Control Variables

The financial control variables employed in this study are sales growth (SG), firm size (SIZE), and leverage (LEV). Sales growth (SG) is the percentage increase or decrease in sales that occurs over a certain time period [10].

Sales growth = $100\times \frac{\left(Sal{e}_{it}-Sal{e}_{it-1}\right)}{Sal{e}_{it-1}}$.

Leverage (LEV) is the ratio of total debt over total assets [44].

Leverage = $100\times \frac{Short-TermDebt+Long-TermDebt}{TotalAssets}$.

The firm size (SIZE) equals the natural logarithm of a company′s total assets [44].

3.3.6. Dummy Variable

The impact of the Global Financial Crisis (GFC) on the study′s findings is controlled by the inclusion of a dummy variable. The GFC is the dummy variable that takes the value of 1 between 2007 and 2009 and 0 in any other years.

3.4. Model and Estimation Technique

Our study employs quantitative data analysis based on the dynamic panel data methodology in STATA 15. Dynamic panel models outperform fixed and random effect models where endogeneity is driven by reverse causality and bias owing to omitted variables is a major issue [67]. This research employs Blundell and Bond′s [68] two-step GMM estimation technique, which has been shown to be more efficient and robust. Importantly, a two-step GMM estimator minimizes unobservable individual effects through first-order differencing while simultaneously using lagged instrumental variables to control the association between the dependent variable difference and the error term [68].

The dynamic panel model is shown below.

Y_i,t = β₀ + β₁ Y_i,t−1 + β2 X_i,t + α_i + Ɛ_i,t

(1)

Here, Y_i,t indicates the dependent variable for firm i at the time t; Y_i,t−1 indicates the lag term of the dependent variable; β represents the estimated coefficient; X represents all the independent variables for firm 𝑖 at the time t; αi represents time-invariant unobserved heterogeneity; and Ɛ_i,t represents idiosyncratic error.

Empirical Models

The study′s objectives and hypotheses serve as the basis for the development of the research models. With the use of the two-step GMM estimate technique developed by Blundell and Bond [68], this study creates the following models.

Model for H1:

CCC_i,t = β₀ + β₁ CCC_i,t−1 + β₂ EPU + β₃ FX Risk + β₄ SG + β₅ LEV + β₆ SIZE + β₇ GFC + α_i + Ɛ_i,t

(2)

Model for H2:

CCC_i,t = β₀ + β₁ CCC_i,t−1×CDS + β₂ EPU + β₃ FX Risk + β₄ SG + β₅ LEV + β₆ SIZE + β₇ GFC + α_i + Ɛ_i,t

(3)

Model for H3:

ROA_i,t = β₀ + β₁ ROA_i,t−1 + β₂ CCC + β₃ SG + β₄ LEV + β₅ SIZE + β₇ GFC + α_i + Ɛ_i,t

(4)

Model for H4:

ROA_i,t = β₀ + β₁ ROA_i,t−1 + β₂ CCC×CDS + β₃ SG + β₄ LEV + β₅ SIZE + β₇ GFC + α_i + Ɛ_i,t

(5)

4. Results and Discussions

This study aims to examine the impact of macroeconomic risk factors on WCM and, eventually, firm performance. Afterwards, we examine CDSs to evaluate their impact on the firm′s WCM and return on assets. In doing so, this study conducts several statistical tests, including descriptive statistics, correlation matrix, and regression analysis using the two-step system GMM estimation technique.

4.1. Descriptive Statistics

Country-wise descriptive statistics are shown in

Table 1. Descriptive statistics.

Country	Variable	Obs.	Mean	SD	Min	Max	Country	Variable	Obs.	Mean	SD	Min	Max
USA	CCC	109,744	4.331	0.840	0.382	5.221	Germany	CCC	9503	4.360	1.055	−0.914	5.736
	ROA	109,765	3.689	0.631	−0.529	4.069		ROA	9503	4.309	0.461	−2.158	4.636
	EPU	109,815	4.910	0.271	4.507	5.353		EPU	9503	5.042	0.277	4.593	5.416
	FX Risk	109,815	2.415	0.629	1.220	2.998		FX Risk	9503	4.568	0.191	4.218	4.802
	CDS	109,815	12.888	0.574	11.757	13.314		CDS	9503	8.230	1.330	5.696	9.545
	SG	109,734	3.685	0.594	−0.018	4.739		SG	9495	3.802	0.624	−0.141	5.032
	LEV	109,693	3.549	0.602	2.303	4.831		LEV	9487	3.614	0.393	3.011	4.735
	SIZE	109,778	18.002	2.358	12.423	22.541		SIZE	9503	18.977	1.873	15.725	22.589
UK	CCC	15,921	3.900	1.290	−0.857	5.240	China	CCC	67,625	4.604	1.176	−0.699	6.191
	ROA	15,915	4.103	0.757	−0.244	4.559		ROA	67,651	3.540	0.308	−0.793	4.097
	EPU	15,933	4.915	0.294	4.473	5.285		EPU	67,695	4.882	0.529	4.210	5.830
	FX Risk	15,933	2.400	0.880	1.096	3.782		FX Risk	67,695	2.207	0.416	1.648	2.779
	CDS	15,933	9.639	0.808	8.052	10.271		CDS	67,695	8.414	0.647	7.218	9.037
	SG	15,933	3.353	0.654	−0.019	4.451		SG	67,679	3.827	0.681	−1.109	5.095
	LEV	15,900	2.744	0.696	1.609	3.871		LEV	67,596	3.471	0.587	2.303	5.111
	SIZE	15,918	18.522	1.856	15.326	22.058		SIZE	67,679	19.726	1.207	11.728	26.653

. The present study winsorizes the data to remove outliers and then applies natural logarithms to the variables before obtaining descriptive statistics. The whole study period is from 2006 to 2020, and we divide it into five periods by averaging three years for the best GMM outcome. However, Table 1 shows that, with the exception of EPU, FX risk, and CDS, each variable has a unique set of observations due to missing data in certain years. Table 1 also reveals that the standard deviations (SDs) for most variables are substantially less than the mean. Thus, the low standard deviation is indicative of data that tend to cluster close to the mean. Further, all countries have firms from different industries, so minimum and maximum CCC values vary considerably. Some firms are conservative, while others are aggressive in WCM. Additionally, ROA has a negative minimum value for all nations across the research periods, whereas minimum and maximum values of EPU, FX risk, and CDS values are nearly close, owing to the influence of the natural logarithm.

4.2. Correlation Matrix

The results of the correlation analysis of the four countries (USA, UK, Germany, and China) are tabulated in

Table 2. Correlation matrix.

Country	Variables	CCC	ROA	EPU	FX Risk	CDS	SG	LEV	SIZE
USA	CCC	1.000
	ROA	0.236	1.000
	EPU	−0.094	−0.109	1.000
	FX Risk	0.021	0.002	−0.184	1.000
	CDS	−0.069	−0.063	0.827	−0.442	1.000
	SG	0.276	0.138	−0.129	0.016	−0.100	1.000
	LEV	−0.056	0.158	−0.014	0.014	−0.017	0.023	1.000
	SIZE	0.248	0.135	−0.035	0.011	−0.033	0.175	−0.056	1.000
UK	Variables	CCC	ROA	EPU	FX Risk	CDS	SG	LEV	SIZE
	CCC	1.000
	ROA	0.160	1.000
	EPU	−0.089	−0.066	1.000
	FX Risk	0.083	0.039	−0.418	1.000
	CDS	−0.073	−0.033	0.643	−0.792	1.000
	SG	0.149	0.189	−0.050	0.033	−0.004	1.000
	LEV	−0.006	0.190	−0.054	0.045	−0.061	0.064	1.000
	SIZE	0.053	0.463	0.008	−0.009	−0.003	0.091	0.445	1.000
Germany	CCC	1.000
	ROA	0.257	1.000
	EPU	−0.060	−0.030	1.000
	FX Risk	0.063	−0.001	−0.634	1.000
	CDS	−0.011	−0.032	0.627	−0.002	1.000
	SG	0.196	0.148	−0.133	0.059	−0.136	1.000
	LEV	0.074	0.023	−0.018	0.019	0.002	0.082	1.000
	SIZE	0.106	0.207	0.018	−0.022	0.012	0.214	0.301	1.000
China	Variables	CCC	ROA	EPU	FX Risk	CDS	SG	LEV	SIZE
	CCC	1.000
	ROA	0.079	1.000
	EPU	0.054	0.092	1.000
	FX Risk	−0.045	−0.034	−0.675	1.000
	CDS	−0.024	0.021	−0.707	0.515	1.000
	SG	0.202	0.352	0.177	−0.177	−0.042	1.000
	LEV	0.008	−0.208	0.171	−0.171	−0.092	−0.051	1.000
	SIZE	−0.159	−0.056	−0.174	0.145	0.070	−0.024	0.111	1.000

. All countries in the table have a positive link between ROA and the CCC, as seen in the table. All else being equal, it suggests that a rise in firms’ CCC will lead to a higher ROA for those businesses. In contrast to China, where the association between the CCC and EPU is positive, it is negative in the USA, the UK, and Germany. The negative association suggests that when there is more uncertainty in economic policy, businesses are more likely to play it safe and keep their CCC levels low. Conversely, as EPU rises, Chinese companies are more likely to invest in WCM to ensure that their operations run smoothly and that they do not lose any customers. Furthermore, the correlation between the CCC and FX risk is positive for all countries except China. As FX risk rises, businesses tend to increase their CCC, and vice versa. Table 2 also shows that there is a negative correlation between CDSs and the CCC. Therefore, with the widespread use of CDSs, WCM will become more effective and the CCC may be minimized.

4.3. Regression Analysis and Discussions

To test the current study′s hypotheses, we employ dynamic panel data regression analysis using a two-step system GMM estimation. The research includes first-order AR(1) correlations, second-order AR(2) correlations, and the Hansen test to validate the two-step system GMM estimation method. The Hansen test of over-identifying restrictions measures the validity of instruments and determines whether the models have been accurately defined. First- and second-order serial correlations are measured by AR(1) and AR(2), respectively.

However,

Table 3. Regression analysis for model 1.

MODEL 1	DV: Cash Conversion Cycle (CCC)
	USA	UK	Germany	China
L.CCC	0.768 *** (0.054)	0.953 *** (0.164)	0.338 ** (0.137)	0.917 *** (0.282)
EPU	−0.087 *** (0.031)	0.264 ** (0.132)	−0.406 *** (0.153)	−0.222 ** (0.089)
FX risk	0.015 ** (0.007)	0.061 * (0.036)	−0.239 ** (0.105)	0.207 *** (0.056)
SG	0.128 *** (0.019)	−0.046 (0.064)	−0.034 (0.061)	0.198 *** (0.037)
LEV	−0.018 (0.022)	−0.168 * (0.088)	0.138 (0.128)	−0.028 (0.026)
SIZE	0.113 *** (0.008)	0.135 *** (0.050)	0.009 (0.057)	0.033(0.034)
Constant	−1.084 *** (0.380)	−3.264 ** (1.453)	5.340 *** (1.764)	−0.475 (1.400)
AR(1) p-value	0.000	0.000	0.003	0.004
AR(2) p-value	0.530	0.340	0.245	0.161
Hansen test p-value	0.742	0.850	0.119	0.571

Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

tabulates the study’s first hypothesis and model that there is a significant relationship between macroeconomic risk factors (EPU and FX risk) and WCM. The CCC as the measurement of WCM is the dependent variable in this model, whereas EPU and FX risk are the independent variables.

To begin with, Table 3 shows that EPU has a significantly negative relationship with the CCC for firms in the USA, Germany, and China. This indicates that as EPU rises, firms lower the number of days required to turn WC into cash. This is because, as Zhang et al. [69] show, the leverage ratios of firms are negatively correlated with EPU. In periods of high EPU, the external finance environment becomes more restrictive because of a supply impact. Further, a “demand effect” could make the supply effect stronger. This means that firms might choose to borrow less when EPU is high, which conforms with the idea that firms are more cautious when the economy is uncertain [7]. As such, firms may be prompted to meet their WC requirements by selling their inventory rapidly and collecting their receivables promptly while paying their payables late.

In contrast, for the UK firms, the analysis reveals a positive and statistically significant link between EPU and the CCC. As EPU increases, the CCC rises, showing deterioration in the management of firms’ WC. According to Dbouk et al. [10], customers are less likely to make timely payments when EPU is high. This increases the DSO. Further, some firms increase accounts receivable amid financial difficulties to gain market share and sales [70]. On the contrary, a trade credit extension to customers would be more difficult for financially constrained suppliers at times of high EPU [16]. According to Garcia-Appendini and Montoriol-Garriga [71], firms that were financially constrained during the 2007–2008 crisis curtailed trade credit supply. This compels businesses to expedite their account payables payments. Furthermore, a higher EPU causes firms to raise inventory as a safety strategy, and they may not anticipate inventory demand properly.

The study′s second independent variable is FX risk, which has a significantly positive effect on the CCC for firms in the USA, the UK, and China, but a significantly negative effect on the CCC for German firms. Research demonstrates that a firm′s exposure to FX rate fluctuations is determined by its foreign-currency-denominated assets and liabilities [11]. As the FX rate fluctuates, it reduces company liquidity and leads to increased costs of capital, financial hardship, and default [35,72]. Firms may also postpone collecting strong currency receivables or increase the number of such receivables when the local currency depreciates [33]. If this happens, the obligations of the main company and its subsidiaries may be repaid more swiftly. Due to the reduced cost of imports, some businesses may decide to expand their inventory of imported goods and materials. Thereby, firms increase their CCC level. The opposite is true when the relationship is negative for German firms.

Table 4. Regression analysis for model 2.

MODEL 2	DV: Cash Conversion Cycle (CCC)
	USA	UK	Germany	China
L.CCC*CDS	0.044 *** (0.002)	0.067 *** (0.004)	0.048 *** (0.010)	0.098 *** (0.004)
EPU	−0.244 *** (0.022)	0.204 *** (0.067)	0.798 *** (0.256)	−0.255 *** (0.015)
FX risk	0.024 *** (0.006)	0.054 ** (0.024)	0.536 *** (0.139)	0.142 *** (0.015)
SG	0.193 *** (0.016)	0.011 (0.034)	0.009 (0.044)	0.135 *** (0.011)
LEV	−0.033 *** (0.012)	−0.084 *** (0.030)	0.120(0.100)	0.002 (0.005)
SIZE	0.058 *** (0.003)	0.031 *** (0.010)	0.055 (0.039)	−0.061 *** (0.004)
Constant	1.281 *** (0.150)	−0.300 (0.431)	−5.513 ** (2.369)	2.406 *** (0.222)
AR(1) p−Value	0.000	0.000	0.000	0.000
AR(2) p−Value	0.107	0.374	0.403	0.187
Hansen test p−Value	0.122	0.510	0.356	0.223

Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the findings of the study′s second hypothesis and model′s analysis. Hypothesis 2 of the study posits that there is a change in the relationship between macroeconomic risk factors (EPU and FX risk) and WCM after the interaction of financial derivatives with WCM. In pursuit of this, we employ CDSs’ interactions with a lagged CCC in this model because we believe that the present level of the CCC is highly reliant on its previous level. However, Table 4 demonstrates that, at a significance level of 1%, the interaction of the lagged variable (L.CCC*CDS) has a positive connection with the CCC for all countries′ firms. As the firms are secured by CDS derivatives, this proves that growth in CDS adoption drives firms to spend more in WC by assuring a higher level of inventory and extending the period of account receivables and payables. According to Li and Tang [42], when a supplier extends more trade credit to customers, they discover that customers′ CDSs have a stronger influence on supplier leverage. As Campello and Matta [43] discover, CDSs positively influence corporate funding, which in turn prompts firms to borrow more to maintain liquidity. CDSs, according to Arping [73], shield lenders against losses resulting from a borrower′s default. This raises creditworthiness for debtors since they increase the credibility of foreclosure risks. Moreover, CDSs have been shown to considerably cut the weighted average cost of capital for US public firms between 2001 and 2018, according to the study conducted by Bhabra et al. [74]. Consequently, the availability of external financing encourages firms to extend the CCC to gain competitive advantages in the global market. Liu et al. [40] and Dodd et al. [39] report that increased leverage boosts CDS adoption, and businesses depend more on CDS amid high default risk.

Nonetheless, the changes in the association between EPU, FX risk, and the CCC following CDS interaction are clearly evident in terms of the coefficient and significance level in Table 4. However, the changes are varied across countries. Keeping the negative correlation, the coefficient findings reveal that in the United States, a one-unit rise in EPU causes a −24.4% drop in the CCC at a 1% significance level after the implementation of CDSs, but it was a −8.7% drop in the CCC before the implementation of CDSs at the same significance level. This is congruent with Chinese firms, where EPU′s and the CCC′s coefficient is -25.5% at a 1% significance level, compared to the previous −22.2% coefficient at a 5% significance level. According to the findings, it seems that CDS adoption improves the creditworthiness of both buyers and suppliers for external financing. They are able to meet their WC requirements through external financing; therefore, the need for prolonged account receivables and payables is eliminated. Further, firms do not have to keep higher levels of inventory on hand as a safety stock since they can buy and hold inventory using external financing. This is due to the fact that CDS makes it easier for external finance to be obtained [43,75]. All the factors contribute to lowering the CCC level to a greater extent. Therefore, firms′ working capital management is improved after the adoption of CDSs. Further, the identical coefficient results for the USA and China may be attributable to the fact that both nations′ firms are more susceptible to EPU owing to their dominant position in international trade [10,15]. Second, the study′s sample contains a higher number of US and Chinese manufacturing firms with a stronger connection to WCM components.

In contrast, our sample contains a comparatively lower number of firms from the UK and Germany, which are dominated by both manufacturing and service sectors. Nevertheless, after the interaction of CDSs in UK firms, we find that the positive association persists but the significance level and coefficient shift. An increase in EPU leads to a 20.4% increase in the CCC at a 1% significance level, compared to a 26.4% increase previously at a 5% significance level. The potential availability of external financing as a result of CDS adoption may induce UK firms to cut the pace of CCC increase amid EPU. Lastly, for German firms, the connection between EPU and the CCC changes from negative to positive at 1% significance following the implementation of CDSs. Firms in Germany may be motivated to keep more inventories, prolong receivables, and shorten payable days in order to take advantage of the competitive benefits that CDS adoption provides. This is due to the fact that CDSs help to reduce the likelihood of a default [40].

In terms of the link between FX risk and the CCC following the adoption of CDSs, we find that businesses in the USA, the UK, and China retain the positive association but with higher coefficients for the USA and lower coefficients for the UK and China. On the other hand, firms in Germany move from a negative to a positive correlation. We further observe that the US firms’ coefficient increases to 2.4% from 1.5% at a 1% significance level from 5%. According to Feng et al. [76], the United States’ CDSs are quite predictable. Therefore, this may encourage businesses to spend more on their working capital to enhance sales. This may result in a rise in the CCC level after the adoption of CDSs.

Contrarily, the UK firms′ coefficient drops to 5.4% from 6.1% at 5% from 10% significance. Chinese businesses′ coefficient drops from 20.7% to 14.2% at the same significance level of 1%. The decline in the coefficient between FX risk and the CCC suggests that when increasing FX risk mismatches current asset and liability values, CDSs may protect firms from default risk due to value loss. In addition, businesses that trade CDSs on their debt are able to speed up the payment of their accounts payable and the collection of their accounts receivable [41]. CDS trading, they argue, makes credit markets more accessible for borrowing firms since it lowers lenders′ risk exposure to borrowers. As a consequence, the requirement for trade credit decreases among borrowing firms because of better access to capital. Thereby, it encourages firms to cut down the time it takes to turn WC into cash. Conversely, the change from a negative to a positive association for German firms may be because CDSs reduce the default risk of value loss in FX risk, which may encourage firms to keep their CCC high to gain a competitive advantage. In the presence of CDSs, German firms may be ready to examine if the variation in the FX rate increases the value of their inventory and accounts receivables while decreasing the value of their accounts payable.

Table 5. Regression analysis for model 3.

MODEL 3	DV: Return on Assets (ROA)
	USA	UK	Germany	China
L.ROA	0.704 *** (0.064)	0.380 *** (0.123)	0.250 *** (0.076)	2.557 *** (0.331)
CCC	0.038 ** (0.016)	0.047 *** (0.014)	0.033 * (0.018)	−0.064 *** (0.018)
SG	0.034 ** (0.014)	0.117 *** (0.020)	0.059 ** (0.028)	0.075 *** (0.016)
LEV	0.005 (0.021)	−0.041 * (0.022)	−0.031 (0.027)	0.051 ** (0.022)
SIZE	−0.042 *** (0.012)	0.105 *** (0.018)	0.029 *** (0.006)	−0.101 *** (0.012)
Constant	1.514 *** (0.378)	0.144 (0.250)	2.428 *** (0.321)	−3.751 *** (1.139)
AR(1) p−Value	0.000	0.000	0.001	0.000
AR(2) p−Value	0.210	0.592	0.556	0.331
Hansen test p−Value	0.592	0.820	0.700	0.199

Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

corroborates the third hypothesis of the current research by demonstrating a significant relationship between the CCC and ROA at different levels of significance for firms in the USA, the UK, Germany, and China. We discover that the association between the CCC and ROA is positive for firms in the USA, UK, and Germany. The positive correlation indicates that a higher CCC corresponds to greater investment in WC, which results in a higher ROA. Afrifa and Tingbani [50] support identical findings, stating that a conservative approach may increase a company′s performance by boosting investment in working capital. Firms′ sales are improved by a rise in inventory and trade receivables as a result of the conservative strategy. Investing in inventories may avoid production delays, lower the danger of running out of inventory, and lower supply costs and price volatility [77]. Concurrently, investing in accounts receivable may enhance firms′ performance because it gives customers more time to pay and reduces the information gap between buyer and seller. It can also be used as a product differentiation strategy, as a price cut, to reduce transaction costs, and to get customers to buy goods when demand is low [78].

For Chinese firms, we find a negative relationship between the CCC and ROA at the 1% significance level (Table 5). The result implies that when Chinese firms′ CCC is lower, it increases the profitability of the firms. A business with a lower CCC time lag is more efficient because it rotates its working capital more times each year. As a result, it makes more sales and profit from its working capital. In light of this, firms should reduce inventory investment and reduce the day’s payable outstanding because of the negative correlation [44]. Additionally, customer financing has opportunity costs that must be considered when determining the optimal level of receivable to maintain a healthy balance. Furthermore, during times of economic volatility, businesses may find their day-to-day operations hampered by a lack of available cash flow. Thus, according to the trade-off theory, a financial manager must balance profitability and liquidity in order to be successful [48].

The present study′s fourth hypothesis posits a different impact on the relationship between WCM and firm performance after the interaction of financial derivatives on WCM. According to

Table 6. Regression analysis for model 4.

MODEL 4	DV: Return on Assets (ROA)
	USA	UK	Germany	China
L.ROA	1.476 *** (0.103)	0.433 *** (0.056)	0.234 *** (0.088)	0.693 ** (0.274)
CCC*CDS	0.008 *** (0.002)	0.005 *** (0.001)	0.006 ** (0.003)	−0.012 *** (0.001)
SG	0.059 ** (0.028)	0.112 *** (0.020)	0.014 (0.022)	0.058 * (0.033)
LEV	0.088 *** (0.022)	−0.039 * (0.023)	−0.020 (0.045)	−0.055 (0.111)
SIZE	−0.042 *** (0.007)	0.099 *** (0.011)	0.041 *** (0.009)	−0.177 *** (0.025)
Constant	−2.068 *** (0.366)	0.052 (0.200)	2.321 *** (0.346)	4.393 *** (1.184)
AR(1) p−Value	0.000	0.000	0.001	0.004
AR(2) p−Value	0.328	0.600	0.515	0.135
Hansen test p−Value	0.340	0.979	0.541	0.245

Standard errors in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.1.

, we observe a significant positive correlation between CCC*CDS and ROA for the USA, UK, and Germany but a negative correlation for China. Prior to CDS interaction, we discover equivalent associations between the CCC and ROA in hypothesis 3. However, the changes are reflected in Table 6′s coefficient. As such, following CDS adoption, the coefficient between CCC*CDS and ROA is smaller than it was in the prior model. For example, an increase in CCC*CDS at the 1% significance level increases USA firms′ ROA to 0.8% (Table 6), whereas an increase in the CCC alone increases ROA to 3.8% at the 5% significance level (Table 5). For UK firms, this coefficient is 0.5% at 1% significance compared to 4.7% in the prior model. Furthermore, for German firms, the coefficient is 0.6% at a higher significance level of 1% compared to its preceding model′s coefficient of 3.3% at a 10% significance level.

The positive and highly significant results but lower coefficient indicate the improvement of the relationship between the CCC and ROA. This is because a negative relationship between the CCC and ROA is imperative. Since the coefficient of the positive relationship between CCC*CDS and ROA drops, it suggests that the CCC rises at a lower level. This helps firms enhance their profitability by increasing sales. The decrease in coefficient may be due to the fact that CDSs reduce trade credit risk by guaranteeing payment to CDS firms in the event of a customer′s default [40]. In addition, CDSs prompt firms to maintain an optimal level of accounts receivables and payables [41] and inventory by ensuring liquidity in the business. Thus, the WCM′s efficiency is improved, and the WC investment is turned into cash more efficiently. Production may operate more smoothly, resulting in higher sales for businesses with enough cash on hand. As a result, the firms are able to increase their profitability.

For Chinese firms, nevertheless, the strength of the negative correlation between the CCC and ROA in Model 3 is weakened after the interaction of CDSs with the CCC in Model 4. Notably, the correlation between CCC*CDS and ROA is −1.2% at a significance threshold of 1%, while it was −6.4% before the interaction. This suggests that following CDS interaction, firms may be able to reduce their CCC level at a lower rate than they could before CDS interaction. When companies struggle to maintain their CCC at the lower level, their working capital management efficiency worsens. Consequently, the firm′s sales decline, affecting its profitability. Nevertheless, this may be related to the fact that CDSs provide a better reflection of corporate credit risk during times when the economy is unstable, but during other times, they may be subject to more noise [79]. Under normal economic conditions, the use of CDSs is anticipated to raise the cost of hedging without significantly improving the situation. Likewise, this study spans the years 2006 through 2020, encompassing both unstable and stable economic periods. As CDS influence is more prominent during volatile times, the inclusion of both unstable and stable economic periods may account for the lower negative coefficients. Overall, the conjecture of different impacts in the relationship between WCM and ROA is evident after the interaction of CDSs. Subsequently, as can be seen from every model, the two-step system GMM estimation supports all the research hypotheses.

5. Conclusions

WCM is postulated to be difficult during periods of uncertainty in macroeconomic risk factors. Additionally, the current research recognizes the necessity of cross-country assessment since domestic and international firms involved in international trade may benefit from other countries′ firms′ WCM practices during macroeconomic turbulence. In view of this, this study first investigates EPU and FX risk, as the core macroeconomic risk factors, on WCM for firms in the USA, UK, Germany, and China. Secondly, it examines the effect of credit default swaps (CDSs) on WCM in the presence of volatile macroeconomic risk factors. Empirical studies have shown that CDSs facilitate external financing and lower the risk of default amid macroeconomic volatility. As a result, we established a positive correlation between CDS interaction and the CCC, which indicates that when firms adopt CDSs in WCM, their confidence in WC investments increases despite rising EPU and FX risk. Considering the EPU, FX risk, and CCC connection following the implementation of CDSs, we can argue that CDSs eliminate the requirement for delayed receivables and payables and higher levels of inventory as safety stock for firms in the USA, the UK, and China. Firms in Germany, on the other hand, may be driven by the competitive advantages that CDS adoption allows: maintaining more inventories, extending receivables, and reducing payable days amid macroeconomic volatility. The current study further discovered that firms in the USA, the UK, and Germany are more profitable after adopting CDSs due to the enhanced efficiency of their working capital management. This study recommends adopting CDSs during volatile periods for Chinese firms. Future studies should also concentrate on volatile periods to evaluate CDSs on working capital management.

6. Implications

• Theoretical Implications

According to the financial accelerator theory, macroeconomic shocks result in a rise in interest rates, external financing limitations, a decline in cash flows, and a consequent impact on companies′ investment behavior [23]. The current study similarly shows that any shock in the macroeconomy may affect firms′ working capital behavior.

(i)

Managerial Implications/Policy Implications.

This study has important ramifications for firms′ adoption of CDSs at the optimal period to maximize their benefits. This is because investors may be tempted to invest in a firm that effectively uses CDSs to streamline profitability. In addition, multinational corporations might utilize the study′s results for cross-country evaluation to identify the best place for their overseas investment. Those nations that are more adept at controlling their firms′ risk levels and maximizing their profitability may be able to capture international investment. Lastly, because EPU and FX risk volatilities are pervasive and impede the operation of firms in an economy, policymakers may require firms to use CDSs in WCM to make sure businesses can run smoothly.

7. Limitations and Scope for Future Studies

The conclusions of this research are subject to several limitations. First, this study is limited to just four nations. Depending on the data availability, future research should involve a larger sample of countries. Second, the current study focuses on non-financial enterprises but future research should focus on financial and non-financial firms. Thirdly, we chose only two macroeconomic risk factors. These criteria may not always be sufficient to evaluate the impact of global macroeconomic risk factors on working capital management. Future studies may incorporate more variables to maintain the present ones. Fourthly, our study suggests that Chinese companies may gain from adopting CDSs during times of crisis, although this is not always the case. An alternative study may reveal additional strategies of risk management that firms may find valuable in both stable and turbulent periods.