Abstract:

This paper looks at financial performance of non-financial sectors of Pakistan concerning capital structure. We gathered data from annual audited financial statements of 152 firms listed at PSX during 2010-2017. To analyze data gathered, we have employed descriptive, correlation and regression analyses techniques. The findings show substantial positive contribution of LTDA in EPS and ROA and significant negative role in NPM and ROE which implies prefer long term debt over short term debt because of less financing cost. STDTA has substantial negative contribution in firms’ financial performance among all sectors except sugar and communication & technology sectors. TDTA also has negative impact on financial performance of firms among all sectors except automobile sector, which implies that equity financing is preferable over debt financing. These findings validate pecking order theory and recommend preferring internal financing (retained earnings) over external financing

Key Words:

Capital Structure, Financial Performance, Non-Financial Sector

Introduction

In today’s dynamic era, firms have to smooth their cash flows by carefully taking operating, investing and financing decisions. Of these decisions, financing decisions are considered as pivotal for survival of every business. Financing decisions refer to sources from which any firm arrange its finance i.e., debt financing and equity financing. Capital structure (CS) of firms contains debt and equity which proportion varies from sector to sector. However, mixture of debt and equity (Akhtar et al., 2019) is considered as optimal CS (Kanwal et al., 2017). According to pecking order theory, internal financing (retained earnings) is preferred over external financing and debt is given preference over equity when external financing is needed (Zaheer et al., 2011). Thus, to decide percentage of debt and equity is considered as crucial decision which has strong influence over firms’ financial performance (FP) ( Akhtar et al., 2019)

As per pecking order theory, debt is preferred because it provides tax shield (Akhtar et al., 2019) as interest on debt is subtracted before tax calculation. However, excessively relaying on debt can create troublesome for firms as it can lead to bankruptcy (Basit & Hassan, 2017). On other side, every firm has limited authorized capital from which shares can be issued to raise equity financing. Excessively relying on equity financing can create liquidity issues in firms (Basit & Hassan, 2017). Therefore, choice of optimal CS is crucial because inappropriate mixture of debt and equity have negative effect on FP  (Akhtar et al., 2019). Thus, financial managers should carefully take decisions related to optimal CS as it can lead to maximizing shareholders’ wealth (Abbas et al., 2013). 

Generally, CS is defined as various sources through which firms arrange their finances i.e., debt and equity (Nawaz et al., 2011). Equity financing includes common stock, preferred stock, and reserve funds. Whereas, debt financing includes short term liabilities and long term liabilities (Chinaemerem & Anthony, 2012). Review of extant literature reveals that majority studies conducted to study role of CS on FP  are found in developed economies (Abdullah & Tursoy, 2019; Abeywardhana, 2016; Avc?, 2016; Berger & di Patti, 2006; Detthamrong et al., 2017; Margaritis & Psillaki, 2010; Salim & Yadav, 2012; Saputra et al., 2015). Limited empirical evidences are observed in developing economies (Abbas et al., 2013; Akhtar et al., 2019; Basit & Hassan, 2017; Fosu, 2013; Hossain et al., 2019; Kanwal et al., 2017; Nawaz et al., 2011). 

Majority of extant empirical evidence has focused on non-financial sector (Chinaemerem & Anthony, 2012; Dada & Ghazali, 2016; Fosu, 2013; Nenu et al., 2018; T. H. Nguyen & Nguyen, 2020a; Pandey & Sahu, 2017). However, some studies have concentrated on a single sector e.g., Information technology (Hossain et al., 2019), textile (Abbas et al., 2013; Akhtar et al., 2019; Ahmed & Siddiqui, 2019; Nawaz et al., 2011; Sachdeva, 2019; Sattar, 2020), engineering (Khan, 2012), sugar (Saeed & Badar, 2013). The findings of studies reveal negative influence of debt financing on firms’ FP  (Kanwal et al., 2017; Zaheer et al., 2011). Some studies have found positive consequences of debt financing on firms’ FP. Thus, extant literature reveals inconclusive findings. Regarding sectoral differences, FP with regard to CS of firms vary from sector to sector (Salim & Yadav 2012). To our knowledge, only Kanwal et al. (2017) gauged FP  of firms concerning CS in Pakistan. Due to dearth of literature on sectoral differences, a study is needed to cover this identified gap.

Non-financial sector is considered as backbone of Pakistan’s economy which contributes 13.6% in GDP (Economic Survey 2017-18). SBP reported growth in non-financial sector by 19.23% in terms of sales during 2018. Within non-financial sector, this study has considered six sectors namely; textile, sugar, steel, automobile, petroleum and Communication &Technology sectors. To examine performance of non-financial sector, present study aims to achieve two objectives (1) To examine role of CS in FP of firms and (2) to gauge any sectoral differences in FP of firms based on CS. 




Literature Review Theoretical Background

Modigliani and Miller (MM) took initiative to gauge nexus between CS and firms’ FP  but did not find any relationship between both of them (Kanwal et al., 2017). In 1963, MM applied CS theory to prove variation in CS as favorable for FP of firms due to tax shield associated with debt. Because of debt financing, firms usually pay less tax. Later on, various theories were developed and applied on the construct of CS i.e., pecking order theory, agency theory, and trade off theory. Agency Theory highlights misalignment of interest between shareholders and management which arises when less dividends are paid due to greater percentage of debt in CS (Basit & Hassan, 2017). Pecking order theory proposed that firms should rely on internal financing and prefer debt over equity when external financing is required due to lesser associated cost. 




Empirical Review

The pertinent literature confirms importance of CS for firms’ FP. Nassar (2016), explored CS influence on firm’s FP operating in Turkey and collected from 136 firms listed Istanbul stock exchange during 2005 to 2012 and to conduct analysis multiple regression models were applied. The findings show that debt ratio (DR) had a substantial as well as negative influence on firm’s EPS, ROE and ROA. Ashraf, Ameen and Shahzadi (2017) explored association among firm’s profitability and optimal CS by collecting data from 18 KSE listed firms during 2006-2015. The study showed results that short-term debt ratio has a substantial positive influence on ROE and ROA. However long-term debt ratio and debt ratio showed negative association with ROE and ROA. 

Vuong, Vu and Mitra (2017) analyzed influence of CS on United Kingdom firm’s FP. Panel data was collected from 739 firms scheduled at London stock exchange during 2006-2015. Their study’s results showed that Tobin’s Q, ROE and ROA had association with long-term liabilities but had no association found with firm short-term liabilities. Firm leverage (LTL and STL) had no significant influence on EPS. Miko and Para (2019) gauged the influence of various determinants of capital structure on Nigerian firms’ profitability. They collected data from audited annual reports of 39 manufacturing firms which were registered on Nigerian stock exchange from 2008 to 2017. The findings extracted through OLS technique show substantial influence of debt financing and equity financing on financial performance of manufacturing firms.  

Likewise, Nguyen and Nguyen (2020) studied the nexus between CS and FP of non-financial firms listed at Vietnam's stock market. They gathered data from 448 firms for the period of 2013-2018 and applied GLS technique. The findings show substantial negative influence of CS on FP which supports pecking order theory. Spitsin et al. (2020) analyzed the influence of CS on performance of high-tech firms of Russia. The data was gathered from 1826 firms during 2013-2017. The findings reveal that effective management of CS positively relates to firms’ profitability measured in terms of ROA.  Meah et al. (2020) also examined the FP of 39 family firms and 39 non-family firms registered at Dhaka Stock Exchange with reference to CS and collected data during 2013-2017. The finding extracted using pooled OLS technique reveals that family firms are substantially influenced by debt financing as compared to non-family firms which favors pecking order theory. 

Mujwahuzi and Mbogo (2020) studied the influence of CS on profitability of firms listed on Dar es Salaam Stock Exchange of Tanzania. Data for this study were extracted from annual reports of firms during 2009-2018. The results obtained through OLS technique show week and statistically insignificant nexus between CS and firms’ profitability. Nguyen (2020) explored the nexus between CS and FP of 48673 construction firms during 2016. The findings confirm that high proportion of debt in CS favorably influence ROA and ROE of firms. 

Basit and Hassan (2017)  identified elements of CS and their influence on firms’ FP. Data were gathered from 50 non-financial sector firms listed at KSE during 2010-2017. Findings reveal significant influence of Debt to equity ratio on ROA. Kanwal et al. (2017) examined FP of 213 non-financial sector firms regarding CS. Data were gathered during 1999-2015 from firms listed at KSE. Their findings reveal that short term and long-term debts adversely affect performance of firms. Rahman, Sarker and Uddin (2019) analyzed linkage between profitability of manufacturing firms and CS. Data was collected from 10 Dhaka stock exchange listed firms during 2013-2017. The findings revealed that debt to equity ratio had a substantial but negative association with EPS, ROE and ROA. The equity ratio and debt ratio had significant positive linkage with ROA and had a positive effect on ROE.

The review of extant literature reveals inconclusive relationship between CS and FP. As effect of CS on FP varies from sector to sector. But only Kanwal et al. (2017) explored sectoral differences. Thus, there is pressing need to assess FP concerning CS and sectoral differences as well. 




Conceptual Framework

Based on extensive review of pertinent literature, this study has designed the framework depicted below: 



Research Methods and Data Collection

The population of current study is consisted of non-financial sector firms scheduled at PSX the choice of listed firms is made owing to their developed and regulated structure. Sample is comprised of 152 firms belonging to six sectors namely: textile sector, steel mills, food and personal care sector, vehicle manufacturing sector, sugar mills, petroleum and chemical sector. Researchers have collected data based on following criteria:

? Only those firms are selected which are registered at KSE

? Out of registered firms, their data should be available from 2010 to 2017

Data for this study has been gathered from audited annual reports of selected firms.

Table 1. Operational Definition of Variables

Construct	Proxies	Formula	References
Capital Structure	STDTA		Umar (2012) and Ebaid (2009 )
	LTDTA		Abore (2005), Zeitun (2007), Umar, (2012) and Ebaid (2009)
	TDTA		Abore (2005), Zeitun (2007), Umar (2012)
Firms' Specific	CACL		Feidakis and Rovolis (2007), Sbeiti (2010), Nor et al. (2011)
	Log TA	= LOG (Total assets)	Salim (2012), and Delcoure (2007)
	Tangibility		Kayo and Kimura (2011), Delcoure (2007), Frank and Goyal (2003)
Financial Performance	ROA		Ebaid (2009), Salim (2012), and Delcoure (2007 )
	ROE		Salim (2012) and Delcoure (2007)
	EPS		Umar (2012) and Ebaid (2009)
	NPM		Booth et al. (2001) and Pandey (2001)

Proposed Regression Model

?FP?_it=?_°+?_1 ?STDTA?_it+?_2 ?;LTDTA?_it+?_3 ?TDTA?_it+?_4 ?IC?_it+?_it

In above equation, FP refers to financial performance measured in terms of ROA, ROE, EPS, and NPM. STDTA refers to short term debt to total assets ratio. LTDTA denotes long-term liabilities to total assets and TDTA refers to total debt to total asset ratio. IC refers to internal characteristics of firms such as firms’ size, current ratio and tangibility. ? refers to error term; i denotes firms and t denotes time.




Findings-Multiple Regression Analysis

This section covers the results of multiple regression analysis and we have applied pooled OLS technique and fixed effect model in accordance with extant literature (Meah et al., 2020; Miko & Para, 2019; Mujwahuzi & Mbogo, 2020).  The findings initially represent the FP of all the sectors (overall) with reference to CS. Then, we have examined sector-wise performance of firms with reference to CS. 

Table 3. Results of Multiple Regression Analysis of Overall Sample

Dep_var		Balanced OLS	Balanced FE
	Independent Variables	Coefficient	Coefficient
EPS	Constant	-3.325*	-52.8535***
	LTDTA	0.4965**	0.7088**
	STDTA	-2.1244*	-0.5374**
	TDTA	0.7756**	-0.8291**
	CACL	-0.02892	-0.0289
	Size	2.2324***	7.3019***
	Tangibility	-6.6422***	-0.6365
	Adj. R Square	0.52	0.84
	F Statistics	0.00025	0.0000
NPM	Constant	-1.9917	1.8619
	LTDTA	0.0625**	-0.3753**
	STDTA	-0.5087*	-0.4428**
	TDTA	0.3424**	-0.1982**
	CACL	-0.2327	-0.2583
	Size	0.4169	0.0397
	Tangibility	-0.5307	-0.5006*
	Adj. R Square	0.46	0.42
	F Statistics	0.0605	0.0165
ROA	Constant	-0.2012	2.8222
	LTDTA	0.1462**	0.5628**
	STDTA	0.1254	0.0305
	TDTA	-0.6152**	0.6221**
	CACL	-0.031	-0.0308
	Size	0.0089	-0.2746*
	Tangibility	0.6089***	-0.2964**
	Adj. R Square	0.53	0.63
	F Statistics	0.0046	0.0001
ROE	Constant	2.1505*	9.9562**
	LTDTA	0.1766*	-0.3618***
	STDTA	0.0276	-0.2456
	TDTA	-0.6221**	0.6823**
	CACL	-0.1742**	-0.2219**
	Size	-0.1994*	-1.0034*
	Tangibility	1.0905***	0.5446*
	Adj. R Square	0.48	0.59
	F Statistics	0.0135	0.0001

***, **, and * refer to significance at 1%, 5% and 10% respectively.

 

Findings of polled OLS reveal that all proxies of CS have significant contribution to EPS. The results of FE model highlight that all dimensions of CS except LTDTA has negative contribution to firms’ EPS. The negative coefficient values imply that excess reliance on short term debt financing increase cost of capital, which reduce firms’ EPS. These results are consonant study of Khan (2012) and outcomes confirm expectations of pecking order theory.

The findings of NPM under FE model show significant negative impact of LTDTA, STDTA and TDTA on NPM of firms; consistent with Chiang, Chang and Hui (2002). Excess use of debt financing either with short term or long-term debt is not favorable for firms’ NPM. ROA is positively affected by LTDTA and TDTA, which implies that financing from long-term sources e.g., bonds and debentures involves less cost than short term sources of debt. Larger firms benefit from long term financing (e.g., Ramaswamy, 2001). The findings of study suggest that STDTA has found no influence on ROE and is in accordance with Mathur and Mathur (2000).

The results of ROE under balanced FE model show significant negative impact of LTDTA and TDTA on ROE of firms; which imply that long term debt is in compliance with signaling and agency theories and consistent with extant literature (Margaritis and Psillaki, 2010). However, STDTA has no influence on ROE of firms, which supports Mathur and Mathur (2000) and in compliance with pecking order theory Majority of previous studies find negative relationship between CS and FP (Baker & Wurgler, 2002; Fama & French, 2002; Rajan & Zingales, 1995). Table 4 to 10 reports sector wise findings of multiple regression analysis.

 

Sector Wise Analysis

This section examines sector-wise FP of non-financial firms with reference to CS. The interpretation of findings is given after results of all the sectors. 

Table 4. Results of Multiple Regression Analysis of Petroleum Sector

Table 5. Results of Multiple Regression Analysis of Textile Sector

Dep_var	Independent Variables	Balanced OLS	Balanced FE
		Coefficient	Coefficient
EPS	Constant	-4.9688	-12.6639
	LTDTA	-1.2968*	2.421**
	STDTA	-1.7538*	0.2948
	TDTA	2.1885**	-3.0135**
	CACL	1.3604*	0.6552
	Size	2.2532**	2.9409
	Tangibility	-3.5633	-1.8834
	Adj. R Square	0.42	0.73
	F Statistics	0.0358	0.0007
NPM	Constant	-1.8161**	-11.5441***
	LTDTA	-0.0185	0.045
	STDTA	-0.3697***	-0.1191**
	TDTA	0.2287***	0.1498***
	CACL	-0.1046*	-0.3222***
	Size	0.2115**	1.3438***
	Tangibility	0.105	-0.2867
	Adj. R Square	0.26
	F Statistics	0.00003
ROA	Constant	0.609***	3.4349***
	LTDTA	0.4213**	0.304**
	STDTA	0.0556*	0.145
	TDTA	0.0603*	0.112*
	CACL	0.0001	-0.006
	Size	-0.0599*	-0.37***
	Tangibility	-0.0224	-0.0529
	Adj. R Square	0.39	0.19
	F Statistics	0.04981	0.0358
ROE	Constant	1.4606**	18.9066***
	LTDTA	0.1271*	0.1375*
	STDTA	-0.1941*	-0.5365***
	TDTA	0.0052	0.1432*
	CACL	-0.0632	-0.1879***
	Size	-0.142*	-2.0603***
	Tangibility	0.5697***	0.8259***
	Adj. R Square	0.5	0.46
	F Statistics	0.0132	0.0002

Table 6. Results of Multiple Regression Analysis of Sugar Sector

Dep_var	Independent Variables	Balanced OLS	Balanced FE
		Coefficient	Coefficient
EPS	Constant	-59.0385	31.1316
	LTDTA	5.4667	2.3282
	STDTA	10.7904**	1.3714**
	TDTA	-3.9233**	-1.8353*
	CACL	1.9685	3.15
	Size	7.5186*	-2.2746
	Tangibility	-25.3205**	-14.694
	Adj. R Square	0.38	0.76
	F Statistics	0.02564	0.000008
NPM	Constant	-0.5848	1.8634
	LTDTA	-0.1354	0.5844**
	STDTA	0.5386***	0.8598***
	TDTA	0.004	0.6106**
	CACL	0.1003**	0.07543*
	Size	0.0281	-0.2689
	Tangibility	-0.0106	0.0594
	Adj. R Square	0.66	0.87
	F Statistics	0.0001	0.000001
ROA	Constant	0.0329	-0.1311
	LTDTA	0.0344	-0.1452*
	STDTA	0.1902***	0.1842**
	TDTA	0.1345**	0.1628*
	CACL	0.0609***	0.0364*
	Size	-0.0098	0.0203
	Tangibility	-0.0938	-0.0349
	Adj. R Square	0.51	0.76
	F Statistics	0.000004	0.000007
ROE	Constant	-5.4105	13.9509
	LTDTA	-1.8548*	-1.6068*
	STDTA	0.3824	-1.1871*
	TDTA	-0.3974*	-1.1307*
	CACL	-0.0738	0.0624
	Size	0.4836	-1.335
	Tangibility	2.6213*	0.2579
	Adj. R Square	0.36	0.38
	F Statistics	0.1057	0.0513

Table 7. Results of Multiple Regression Analysis of Steel Mills Sector

Dep_var	Independent Variables	Balanced OLS	Balanced FE
		Coefficient	Coefficient
EPS	Constant	-139.465**	-132.565*
	LTDTA	-20.9484*	-35.3663**
	STDTA	-5.4948	-4.1867*
	TDTA	20.6674*	23.7587**
	CACL	2.9353**	8.4839**
	Size	14.9756**	17.4131
	Tangibility	-10.4008*	20.7356
	Adj. R Square	0.44	0.83
	F Statistics	0.0258	0.00337
NPM	Constant	-1.3563*	1.458
	LTDTA	-0.4411*	1.9257*
	STDTA	-0.3507*	-0.0465*
	TDTA	0.347	-0.5071**
	CACL	0.0745	-0.0408
	Size	0.1498*	-0.0907
	Tangibility	-0.2422*	-1.2393*
	Adj. R Square	0.53	0.71
	F Statistics	0.0048	0.0705
ROA	Constant	-0.0674	2.8881
	LTDTA	-0.2635	1.4499
	STDTA	-0.4402*	-0.1291*
	TDTA	0.3717*	-0.7862*
	CACL	0.0144	-0.1305*
	Size	0.0889	-0.206
	Tangibility	-0.3558*	-1.1677
	Adj. R Square	0.52	0.46
	F Statistics	0.049	0.0741
ROE	Constant	-2.3898**	-5.376
	LTDTA	-0.6198*	1.8686*
	STDTA	-0.021	0.4151*
	TDTA	2.4244**	-0.2781**
	CACL	0.136*	0.1478*
	Size	0.2365**	0.57
	Tangibility	-0.1094	-0.7986
	Adj. R Square	0.37	0.7
	F Statistics	0.0716	0.0759

Table 8. Results of Multiple Regression Analysis of Automobile Sector

Dep_var	Independent Variables	Balanced OLS	Balanced FE
		Coefficient	Coefficient
EPS	Constant	8.6191	-22.2965
	LTDTA	0.6684	0.9437**
	STDTA	21.3238*	-6.7038**
	TDTA	6.0759**	-6.5216*
	CACL	-0.4825	0.0074
	Size	3.3613***	5.1582*
	Tangibility	-70.3968***	-3.2491
	Adj. R Square	0.35	0.75
	F Statistics	0.0003	0.0007
NPM	Constant	1.3226	0.4598
	LTDTA	0.1274***	0.1128**
	STDTA	-1.001*	-0.2577*
	TDTA	1.7216**	0.2087*
	CACL	0.0141	-0.0097
	Size	-0.0354	-0.037
	Tangibility	-2.4023***	-0.1913**
	Adj. R Square	0.23	0.42
	F Statistics	0.0145	0.0005
ROA	Constant	2.0915***	6.1744**
	LTDTA	0.1767***	0.063***
	STDTA	-0.2821	-.4992*
	TDTA	-0.3832*	1.144**
	CACL	-0.0947**	-0.0624*
	Size	-0.1258**	-0.7129***
	Tangibility	-0.6799*	0.3286
	Adj. R Square	0.48	0.74
	F Statistics	0.0871	0.0006
ROE	Constant	10.4102**	7.2535
	LTDTA	0.2029*	-0.4725***
	STDTA	-2.6434	1.579
	TDTA	-0.3673*	1.937**
	CACL	-0.3019	-0.0443
	Size	-0.7368*	-0.8384*
	Tangibility	-2.3895	0.2473
	Adj. R Square	0.4	0.47
	F Statistics	0.0206	0.000017

Table 9. Results of Multiple Regression Analysis of Communication &Technology Sector

Dep_var	Independent Variables	Balanced OLS	Balanced FE
		Coefficient	Coefficient
EPS	Constant	-54.2449*	-87.8966**
	LTDTA	3.8305	-2.3406
	STDTA	-58.7491**	-49.4246**
	TDTA	4.1709***	1.4622**
	CACL	-3.8975*	-1.6362*
	Size	19.6975***	19.8907***
	Tangibility	-83.0846***	-29.3276**
	Adj. R Square	0.49	0.95
	F Statistics	0.000008	0.0001
NPM	Constant	-1.4054***	-0.4884
	LTDTA	0.0228	0.004
	STDTA	1.1537***	0.6257**
	TDTA	-0.1173*	-0.0329*
	CACL	0.0407*	0.001
	Size	0.1006**	0.0296
	Tangibility	0.2587	0.3849
	Adj. R Square	0.35	0.6
	F Statistics	0.00029	0.000041
ROA	Constant	-14.338***	0.4973
	LTDTA	0.4432*	0.0263*
	STDTA	13.8463***	1.5069****
	TDTA	0.5652*	-0.082***
	CACL	0.3505**	-0.0108
	Size	0.9368***	0.0134
	Tangibility	1.2276	0.6067
	Adj. R Square	0.61	0.98
	F Statistics	0.00001	0.0001
ROE	Constant	-16.9753***	508291*
	LTDTA	0.3344	-0.2139*
	STDTA	16.1578***	3.2218**
	TDTA	0.1385**	0.2472**
	CACL	0.39**	-0.0087
	Size	1.1711***	-0.2902*
	Tangibility	0.7731	0.0786
	Adj. R Square	0.61	0.97
	F Statistics	0.0001	0.00001

***, **, and * refer to significance at 1%, 5% and 10% respectively.

 

The findings suggest that STDTA has a significant negative influence on FP of all sectors except communication and technology sector where positive effect of STDTA on FP of firms is found. LTDTA has positive effect on FP of automobile and communication & technology sectors. However, negative effect of LTDTA is found in steel mill, sugar, textile and petroleum sectors’ firms. TDTA has positive effect on FP of all sectors except automobile and petroleum sectors. In nutshell, sector wise analysis confirms that selection of appropriate CS is necessary for optimal FP of firms.

Conclusion

Optimal CS is crucial for firms’ profitability. For this, management of firms choose such

CS that is consistent with shareholders’ wealth maximization. Review of extant literature reveals that majority of studies were found in developed and emerging economies e.g., China, UK, and Turkey. However, dearth of empirical evidence is found regarding developing countries especially in Pakistan. Of these studies, majority of scholars have examined effect of CS on firms’ performance in Pakistan without considering comprehensive sample. Thus, to fill identified gap, this study aims to provide a deep insight into association between CS firms’ FP, by comparing six sectors. Thus, this study focuses on highlighting differences across industrial sectors regarding effect of CS on Firms’ performance. This study has gathered data from annual audited financial statements of 152 firms listed at PSX during 2010-2017. 

To analyze data gathered, descriptive statistics, correlation and multiple regression analysis techniques have been opted. The findings show substantial positive impact of LTDA on EPS and ROA and adverse effects on NPM and ROE. Sector wise regression analysis reveals that LTDTA has positive role in FP of firms, which infers that long-term debt should be preferred over short term debt because of less cost of financing. STDTA has substantial negative impact on firms’ FP among all sectors except sugar and communication & technology sectors which implies that short term financing involves higher cost of financing thus put unfavorable influence on firms’ profitability. TDTA also has negative influence on firms’ FP among all sectors except automobile, which implies prefer equity financing over debt financing. These findings validate pecking order theory and recommend internal financing (retained earnings) over external financing.

Limitations and Future Research Directions

This study has collected data from 152 firms listed firms at PSX and ignored non-listed firms. Thus, findings may not be generalizable to non-listed firms because of non-availability of data. Another limitation is prime focus on accounting-based measures (e.g., ROA, and ROE) of FP; thus, ignored market-based perspective (measured by Tobin Q) and its inter-relationship with CS of firms. This paper focuses on role of CS in FP; potential researchers can add moderators e.g., firms’ size, age, free cash flows. This study measures CS in terms of LTDTA, STDTA and TDTA, it is recommended to add more proxies of CS e.g., debt to equity ratio and short-term debt to total debt ratio Moreover, this paper examines unidirectional connection between CS and FP; it is recommended to investigate bidirectional relationships. 

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