ASIA PACIFIC INSTITUTE OF INFORMATION TECHNOLOGY
Masters in Business Administration (Finance)
By
FATHIMATH SHAMSIYYA
[CB No.005302]
[Intake : MP13A1MF] Date of Submission: 11th August 2015 Supervisor: Professor Kennedy Gunawardana Assessor: MR.S.C. Kaluarachchi Word count: 9676
TITLE “An impact of aggressive working capital policy on corporate profitability in the selected listed companies of Colombo Stock Exchange”
Supervisor’s Declaration Student Name: Fathimath Shamsiyya (Cb005302)
Supervisor’s Name: Professor Kennedy Gunawardana
I, Professor Kennedy Gunawardana, have certified that above student work entitled as “An impact of aggressive working capital policy on corporate profitability in the selected listed companies of Colombo Stock Exchange” was undertaken and submitted to the APIIT (Asia Pacific Institute of Information Technology) as partial fulfilment of Masters in Business Administration Degree in Finance. I believe presentation of this research work may equally precise and complete up to the required standard of degree awarding body Staffordshire University.
Sign:
Professor Kennedy Gunawarudena
11th August 2015
Stundent’s Declarion This research work is submitted as the partial fulfillment requirement of Masters in Business Administration (Finance). I assure that this work is not previously accepted in any substance in any degree. Further it is not being concurrently submitted this particular topic in candidature of any degree. All the information taken from different sources are duly referenced as per Staffordshire Reference Guidelines.
Sign:
Fathimath Shamsiyya
11th August 2015
Acknowledgement This research proposal on working capital management in listed companies in Sri Lanka is possible due to the help, support, and blessings from various parties. Firstly, I would like to thank my lecturer Professor Kennedy Gunawardana for his immense advice. As a lecturer and guide, Professor gave me new insights and ideal opinions regarding this research topic that lead to go ahead further with this entire research work. I am overwhelmed and much appreciated to Professor Kennedy Gunawardana for his kindness and valuable time to complete this proposal successfully. I would also like to appreciate the help given from Assessor Mr . Kaluarachchi. Secondly, I would like to thanks, APIIT administration for their help and support. APIIT Librarians help me to get the required and necessary materials to do the research. Finally, I sincerely thank to my parents, and my dear friends, who give continuous support and advices to finish this research proposal.
Abstract This study has investigated the impacts of aggressive working capital policies on firm’s profitability at Colombo Stock Exchange. Out of 292 companies listed at the Colombo Stock Exchange, 162 companies select as a target population by excluding service sector and new companies listed less than five years. The study comprises a sample size of 114 companies, which includes 12 sectors out of 20 sectors in Colombo Stock Exchange. Due to the negative figures in ratio data, those companies’ data eliminated and sample size further reduces to 68 companies consisting 09 sectors. An aggressive working capital policy measures through aggressive investment and financing policy. Corporate profitability measures through return on assets of the companies. The main purpose of this study is to find out the impacts of aggressive working capital policy on corporate profitability in the selected listed companies in the Colombo Stock Exchange. Statistical software package Gretl version gretlw32.exe will be used for data analysis. Hausman test, Breusch-Pagan Lagrangian Multiplier (LM) test and Chow test run to select appropriate model for the final data analysis. Pooled OLS regression uses as an inferential analysis while statistical analysis such as descriptive and correlation matrix uses to evaluate the data before running regression analysis. The results of the study revealed a positive relationship between aggressive working capital policy and corporate profitability for the selected listed companies in Colombo Stock Exchange. The result also indicated that debt used by the companies does not have significant impact on corporate profitability but there is a positive relationship between them. It further indicated that high aggressive working capital policy raises the financial distress level of the company. Based on study results, financial managers in these selected listed companies in Colombo Stock Exchange can improve and enhance profitability further if they adopt more aggressive working capital policy. Key words:
Corporate Profitability, Aggressive Investment Policy, Aggressive Financing
Policy, Debt Ratio, Return on Assets
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TABLEOF CONTENTS Acknowledgement .................................................................................................................................. x Abstract................................................................................................................................................. vi CHAPTER ONE .................................................................................................................................... 1 1.0 Introduction ...................................................................................................................................... 1 1.1
Introduction ............................................................................................................................ 1
1.2 Background of the study ............................................................................................................. 1 1.3 Research Question ...................................................................................................................... 2 1.4 Problem Statement ...................................................................................................................... 2 1.5 Problem Justification................................................................................................................... 3 1.6 Objectives of the study ................................................................................................................ 3 1.7
Significance of the study ......................................................................................................... 4
1.8 Scope of the study ......................................................................................................................... 4 1.9 Conclusion.................................................................................................................................... 5 CHAPTER TWO ................................................................................................................................... 6 2.0 Literature Review ............................................................................................................................. 6 2.1 Introduction ...................................................................................................................................... 6 2.2 Working Capital Management Reviews ............................................................................................ 6 2.2.1 Working Capital Management- International Context ................................................................. 6 2.2.2 Working Capital Management- Sri Lankan Context ................................................................ 8 2.3 Working Capital Policies Reviews ................................................................................................ 9 2.3.1 Working capital policy-International context .......................................................................... 9 2.3.2 Working Capital Policies-Sri Lankan Context ...................................................................... 13 vii
2.4 Critical Reviews and Conclusion and Variables Selection ........................................................... 15 2.4.1 Researcher’s Critical Review................................................................................................ 15 2.4.2 Variables Selection .............................................................................................................. 16 2.4.3 Conclusion of Literature Reviews ......................................................................................... 17 CHAPTER THREE .............................................................................................................................. 19 3.0 Research Methodology ................................................................................................................... 19 3.1 Introduction ................................................................................................................................ 19 3.2 Conceptual Framework ............................................................................................................... 19 3.2.1 Illustration of Conceptual Framework .................................................................................. 20 3.3 List of Hypotheses ...................................................................................................................... 21 Table 1 List of Hypotheses ............................................................................................................... 21 3.4 Population and target population ................................................................................................. 22 3.5 Sample size................................................................................................................................. 22 3.5.1 Research design ....................................................................................................................... 22 3.5.2 Data collection method ............................................................................................................ 22 3.5.3 Sampling method ..................................................................................................................... 23 3.5.4 Illustration of the sample .......................................................................................................... 24 3.6 Proposed statistical method ......................................................................................................... 26 3.6. 1 Statistical method used by previous research studies ............................................................ 26 3.7 Conclusion of Research Methodology ......................................................................................... 27 CHAPTER FOUR ................................................................................................................................ 28 4.0 Data Analysis ................................................................................................................................. 28 4.1 Introduction ................................................................................................................................ 28 4.2 Basic Assumption Tests .............................................................................................................. 29 4.3 Descriptive Statistics ................................................................................................................... 29 4.4 Correlation Matrix .................................................................................................................. 31 viii
4.5 Panel Diagnostics........................................................................................................................ 31 4.5.1 Hausman test ........................................................................................................................ 32 4.5.2 Breusch - Pagan Lagrangian Multiplier Test ............................................................................. 32 4.6.3 Chow Test or F Test ............................................................................................................. 33 4.7 Panel Data Model Selection ........................................................................................................ 34 4.8 Pooled OLS Regression analysis ................................................................................................. 34 Model 1: Pooled OLS, using 340 observations .................................................................................. 35 4.9 conclusion of data analysis .......................................................................................................... 36 CHAPTER FIVE .......................................................................................................................... 37 5.0 Discussion of Findings ............................................................................................................ 37 5.4 Overall Summary of Pooled OLS regression model ................................................................. 40 5.5 Summary of findings ............................................................................................................... 40 CHAPTER SIX .................................................................................................................................... 42 6.0 Recommendation and Conclusion ................................................................................................... 42 6.1 Conclusion.................................................................................................................................. 42 6.2 Limitation and recommendation for future research ..................................................................... 43 References............................................................................................................................................ 45 Bibliography ........................................................................................................................................ 49 Appendices........................................................................................................................................... 50 Appendix A ...................................................................................................................................... 50 Appendix B ...................................................................................................................................... 51
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LIST OF TABLES TABLE 1 LIST OF HYPOTHESES ............................................................................................................................. 21 TABLE 2 FINAL SAMPLE SIZE ................................................................................................................................ 25 TABLE 3 STATISTICAL METHOD USED BY PREVIOUS RESEARCHERS ......................................................................... 26 TABLE 4 EXPLANATORY VARIABLES- DUMMY ...................................................................................................... 28 TABLE 5 DESCRIPTIVE STATISTICS ........................................................................................................................ 29 TABLE 6 CORRELATION MATRIX .......................................................................................................................... 31 TABLE 7 HAUSMAN TEST STATISTIC ..................................................................................................................... 32 TABLE 8 BREUSCH-PAGAN LAGRANGE MULTIPLIER TEST ...................................................................................... 33 TABLE 9 CHOW TEST ........................................................................................................................................... 33 TABLE 10 TESTS APPLIED TO PANEL DATA ............................................................................................................ 34 TABLE 11 POOLED OLS REGRESSION ANALYSIS ................................................................................................... 35 TABLE 12 OVERALL MODEL SUMMARY ................................................................................................................ 40 TABLE 13 SUMMARY OF RESULTS ......................................................................................................................... 41 TABLE 14 DIFFERENT TEST OF NORMALITY ........................................................................................................... 50 TABLE 15 VARIANCE INFLATION FACTORS............................................................................................................ 50 TABLE 16 LM TEST (WHITE'S TEST) ....................................................................................................................... 51
LIST OF FIGURES FIGURE 1: CONCEPTUAL FRAMEWORK .................................................................................................................. 20 FIGURE 2 SAMPLE OF COMPANIES IN THE CSE ....................................................................................................... 24 FIGURE 3 STATISTICS SUMMARY .......................................................................................................................... 30
x
CHAPTER ONE
1.0 Introduction
1.1 Introduction
Maintaining consistent high financial performance is a target for each company doing business. Financial managers involve daily operation to maximize its corporate profitability by managing all its existing resources in an efficient and effective manner. Various academics, professional and researchers argued that profit could maximize only by minimizing risks associated with working capital management components and its policies. Past millennia, there are thousands of researches undertaken in working capital management and corporate performance field. Presently, majority researchers in accounting and finance major, show interested to observe the relationship between aggressive working capital policy and corporate profitability.
1.2 Background of the study In corporate finance, working capital management is a crucial component and companies need to have an adequate level working capital to run business smoothly. Working capital management is a significant aspect for a successful business. Working capital management means a company ability to finance its short-term current assets and current liabilities. It has an important role in a business. Several researches conduct on the working capital management and named some base papers such as Deloof, (2003), Raheman & Nasr, (2007), Afza & Nazir, (2009) and so on. Mostly, all these studies examine the relationship between working capital management and profitability. Various research findings revealed that working capital management and corporate profitability have a positive relationship while others 1
argued it has a negative relationship. Some studies results showed no significant relationship exist between them. An aggressive working capital policy influences corporate profitability evident from the previous research studies conducted in the other countries such as Pakistan, Nigeria, Kenya and Iran. The degree of aggressiveness of working capital in an investment policy depends on firm’s plan to heavy investment on long-term fixed assets rather than short-term current assets. The requirement of current assets is an essential to maintain at certain level, especially liquid assets that can easily converted into cash, to pay off its short-term obligation. An early payment prevents and reduces the possible risk of financial distress. Thus, different working capital policies have an influence on corporate profitability and liquidity too. On the other hand, there are few researches conducted in the Sri Lankan so far regarding aggressive
working
capital
policy.
Recently,
Perera&Wickremasinghe,(2010),
Bei&Wijewardana,(2012), Pirashanthini et al.,(2013), Bandara&Weerakoon,(2011), and Lingesiya & Nalini,(2011) did research on the working capital policies. However, except two, other studies just narrow down their study area into randomly selected one sector in Colombo Stock Exchange. Listed companies in Colombo Stock Exchange contribute sufficient inputs for the development of Sri Lankan economy, thus, it is extremely useful to investigate impacts of using aggressive working capital policy on corporate profitability. 1.3 Research Question How does working capital policy affect corporate profitability in the listed companies of Colombo Stock Exchange?
1.4 Problem Statement Does an aggressive working capital policy affect the corporate profitability? How does this policy affected to the financial distress? 2
1.5 Problem Justification Working capital management is a serious problem and issue that companies facing in daily operation. An effective working capital policy plays a vital part in a business irrespective of a firm size. Whether company is small-medium enterprise or listed company or, private sole enterprise, having adequate level of working capital is essential to run business efficiently. If a company able to make a profit and compete with other rivals in the competitive market referred as operationally efficient firms. Nevertheless, in a certain situation if firms have less current assets and stills adopting aggressive working capital policy, it may be possible chances to face liquidity issues. Hence, this significant phenomenon looks forward into firm’s possible future. Hence, it is essential to evaluate the effectiveness of aggressive working capital policy on corporate profitability. Heavy investment on current assets rather than fixed assets may create some consequences. Potential benefits that a firm could earn from fixed assets and creating more shareholders wealth becomes an opportunity cost for some companies. On the other hand, if a firm invest more on fixed assets by ignoring its short-term capital requirements, it may face possible bankruptcy or financial distress issues due to lack of funds needed to operating business. Consequently, working capital policies have trade-off between profitability and risks. Thus, an effective working capital policy is useful for a successful business. 1.6 Objectives of the study The purpose of the study is to examine an aggressive working capital policy impact on corporate profitability for listed companies in the Colombo Stock Exchange for the period of 2009-2013. The specific of objectives of the study are as follows:
To determine the impact of aggressive working capital policies on corporate profitability of listed companies in Colombo Stock Exchange, 3
To predict the organizational financial distress by using aggressive working capital policies and,
1.7
To find out, effectiveness of aggressive working capital policy Significance of the study
Managers: Managers involve in day-to-day operation of business that involved working capital. Hence, managers can evaluate required firm’s optimal level of working capital and impacts on firm’s performance if this required level reduces or increases. They can also identify the best working capital policy for a business to maximize its profits. Particularly this study helps them to know driving forces of working capital management and its policies. In addition, they may also able to recognize its influence on corporate profitability. Financial institutions: These parties can acquire company’s financial stand easily and may able to identify healthy companies to invest. This study gives an indication to these that if companies adopting particular working capital policies they may be to earn profit better or not. Shareholders: Moreover, before investing in a company, it is important to know whether company will be profitable in the future or not. This study facilitates them to understand how profitability and firm value affected due to the working capital policy. A recent study of working capital management revealed that working capital policy is significant to maximize the shareholder’s wealth. In addition, some studies have stressed that investors give more weight to buy shares of those companies who create more value for them. Lastly, this study adds further knowledge to the existence empirical researching finding on the working capital management and its policies in the Sri Lankan context.
1.8 Scope of the study The sample data obtained for this research covers only listed companies in the Colombo Stock Exchange over five years, during 2009-2013. Therefore, it may not reflect the company’s entire 4
performance in managing their working capital policies. The research data collects for five years period as a result this study may be indicative but not conclusive. New infant companies and service sectors firms excluded from the target population. However, the sample size is representative and can give an idea of generalization for the population. This study uses secondary data available on published financial statements, so credibility criteria duly enforced. Credibility of this research outlines in the Appendix B. However, this research does not reflect any qualitative aspect such as skills of managers and CEO to improve the company performance and solving working capital problems. 1.9 Conclusion This chapter one highlights the study background and the research questions, problem statement, objectives, scope and significance of the study. The next chapter, literature reviews evaluate critically reviews of previous research studies conducted on the working capital management and its policies.
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CHAPTER TWO 2.0 Literature Review
2.1 Introduction Working capital management, as part of financial and corporate management, shows a critical part of a firm’s development and growth. It plays an important role in a financial performance (Deloof, 2003). Due to this, several researchers and academics find interested to study the impacts of working capital policy on profitability. This literature reviews chapter split into major three sections. First section represents the reviews of previous research studies on working capital management. Second section presents the reviews of working capital policies. These reviews again divide into two sub-sections as international context and local context. Lastly, in the third section pointed out the critical review of researcher and conclude the chapter by selecting variables for the development of hypothesis testing for present study based on the previous studies.
2.2 Working Capital Management Reviews This section outlines working capital management literature reviews from both globally and locally conducted researches. In International context section, outlines few that mostly cited base papers in the working capital management whereas for local Sri Lankan context all the available studies reviewed as this present current study focuses on the Sri Lankan companies. Thus, it is important to outlines details of all previous working capital research conducted in Sri Lanka. 2.2.1 Working Capital Management- International Context Deloof, (2003) carried out a sample of 1009 non-financial Belgium firms for a period of 19921996 to study the impacts of working capital management on firm’s profitability. The study 6
found out a strong negative relationship between gross operating income and working capital management. They discussed most of the firms in their sample size have a large amount of cash and the way of financing this cash may have an impact on their profitability. Furthermore, the study noted that managers in less profitable firms could create firm value if they reduce the number of days in account receivable and minimize the number of days in inventory conversion period to sales. In addition, study also reported that less profitable firm waits longer to settle their due payment to creditors. Raheman & Nasr, (2007) in their research study regarding working capital management and profitability of 94 selected listed firms in Karachi Stock Exchange, Pakistan showed a negative relationship between working capital management and its profitability. Further, the study explained that by reducing days in cash conversion cycle, increases profitability and creates more positive value for firms and its shareholders. Mathuva, (2010) examined working capital management and corporate profitability for 30 listed firms on the Nairobi Stock Exchange, Kenya. The study found that most profitable firms took less time to collect cash from its customers. Moreover, study commented that if firms delayed payment to its creditors, the more profitable firm becomes. This study finding is also in line with Raheman & Nasr, (2007). Banos-Caballero et al., (2014) had an attempt to examine working capital management through the net trade cycle with net operating profit using descriptive statistics. Finding of the study explained that an optimal level of working capital ensured higher profitability due to higher sales and discount given on early payments and it has a positive effect on firm’s performance. However, if required level of working capital exceeds the optimum level, firms opportunity cost and financing constraints starts. This study also revealed that firm can increase its performance by reducing the number of days in its account receivable. Gill et al.,( 2010) had empirically examined the relationship between cash conversion cycle and profitability on 88 listed companies in New York Stock Exchange from 2005 to 2007. Research 7
findings showed positive relationship existed between cash conversion cycle and profitability. It means by increasing the days in cash conversion cycle firms can increase profitability. Therefore, the study confirmed a positive relationship between the working capital management components and profitability of listed firms in New York Stock Exchange. 2.2.2 Working Capital Management- Sri Lankan Context Perera & Wickremasinghe, (2010) focused to investigate working capital practice management of manufacturing sector firms listed and unlisted in Sri Lanka. Research sample comprises of thirty listed companies and ten unlisted companies for five years from 2008-2012. The sample data gathered through a survey questionnaire and interviews among chief financial officers and managing directors. The study discovered that majority firms in the manufacturing sector used an informal working capital management. Finance managers are responsible to manage working capital components while managing directors formulate working capital policies on ad-hoc basis. The results showed that normally firms used cash budgets and current ratio as technique to manage working capital. Furthermore, chi-square result confirmed no significance relationship between specific working capital policy and its profitability. Ajanthan, (2013) examined working capital management and profitability of firms in three sectors in Colombo Stock Exchange (manufacturing, beverage, food and tobacco and chemicals and pharmaceutical companies) for five years from 2007-2011. The study disclosed no significant relationship between working capital management and profitability. Niresh, (2012) investigated working capital management and financial performance of 30 selected manufacturing companies in Colombo Stock Exchange from 2008-2011. This study findings also found no significant relationship exist between working capital management and its policies on profitability. This study results are similar with study finding of Ajanthan, (2013). Ramesh & Balaputhiran, (2013) studied the relationship between working capital management and profitability for 17 manufacturing companies listed in Colombo Stock Exchange from 20068
2010. Pearson correlation analysis result found a statistically significant moderate positive relationship between inventory turnover in days and cash conversion cycle with net profit. Nevertheless, it also indicated a weak negative relationship between current ratio, creditors’ payment period and debtors’ payment period with net profit. Furthermore, the study discussed improper management of working capital and allocating cash more than enough cash makes firms inefficient and reduces benefits of short-term investment. Yogendrarajah & Thanabalasingam, (2014) examined selected nine listed trading firms in Colombo Stock Exchange using SPSS software for five years from 2004 to 2009 to find out working capital management influence on financial performance. The study findings proved a negative relationship between return on assets and inventory turnover and cash conversion cycle. 2.3 Working Capital Policies Reviews This section focus on literature reviews of aggressive working capital policies on corporate profitability. There are various literatures on working capital policy globally conducted but few research studies conducted on this area in the Sri Lankan. 2.3.1 Working capital policy-International context Weinraub & Sue Visscher, (1998) had empirically investigated to find out industry differences in working capital investment and financing practices on ten different cross-section over ten years period. The regression analysis used to examine the relationship between working capital policies and industries financial performances showed different no statistical significance but there is a positive relationship between theses working capital practices across industries and its performance. Oloo & Mwangi, (2014) analysed information related to the aggressive financing working capital policy for 38 listed companies in Nairobi Securities Exchange in Kenya. In this study, study used both primary and secondary data by using statistical software SPSS version 2.1 and 9
showed a positive effect on profitability. Therefore, study recommended companies should further enhance their aggressive financing policy to improve their profitability. Khaksarian,( 2014) investigated working capital policies with profitability measured through return on assets and tobin’s q. This study results showed that aggressive working capital policy has a negative relationship with profitability. The study also confirmed that an increase in aggressive investment policy resulting a decrease in return on assets. Valahzaghard et al., (2013) had empirically investigated working capital policies in different view by take into account the industry along with firm financial performance. However, the study results concluded aggressive investment policy and tobin’s q has no statistical significant relationship. Usman et al., (2014) have randomly selected a sample of 32 listed firms in manufacturing industry in Karachi Stock Exchange, Pakistan for five years from 2006-2010 to study the impacts of working capital management and its policies on corporate profitability. By running panel data regression model showed a negative relationship between working capital management (cash conversion cycle) and profitability (return on assets) for all except for the chemical firms. This result contradicts with Gill et al., (2010) who found a positive relationship between working capital management and profitability. Reddy, (2013) evaluated aggressive working capital policy’s impact on profitability of Indian companies during 2007-2011. Panel regression result found a negative impact on profitability. Further, study highlighted firms can improve profitability by adopting more conservative working capital policies and noted that if companies follows more aggressive working capital policy there is a greater chance of financial distress occurrence and may lead to bankruptcy if the managers couldn’t manage its current assets and liabilities properly. Afza & Nazir, (2007) have also studied the relationship between working capital policies and profitability. This study tested aggressive investment and financing policy for 17 industrial 10
groups of public limited companies listed on Karachi Stock Exchange from 1998-2003. The findings showed a negative relationship between return on assets and aggressive investment and financing policy. However, this result indicated that industries that follow an aggressive investment policy also follow an aggressive financing policy. Nazir & Afza, (2009) once again did the research on impact of aggressive working capital management policies on firm’s profitability with additional market measures of profitability as tobin’s q. In this study, sample data included only the non-financial listed companies in Karachi Stock Exchange during 1998-2005. The sample data included consistent companies in the business and eliminated all the negative equity firms, and new firms and any firms delisted during this period. This research study comprises 204 non-financial companies over 17 industrial sectors. The panel data regression showed that there is a negative relationship between aggressive investment policy and return on assets and tobin’s q. However, study findings found a positive relationship between tobin’s q and aggressive financing policy. Thus, this study concluded that investors give more weight to those companies who follow aggressive financing approach to manage its current liabilities (Nazir & Afza, 2009, p.19). Al-Shubiri, (2010) also investigated relationship between aggressive investment and financing policy for 59 industrial listed companies in Aman Stock Exchange during the period of 20042007. An impact of working capital policies on firm’s profitability measured through crosssectional regression model. The result showed a negative relationship between working capital policies and profitability. The study noted that firm makes a negative return if they follow aggressive investment policy and aggressive financing policy to manage its working capital. Nyabuti & Alala, (2014) have investigated aggressive investment and financing working capital policy’s influence on return on assets of listed companies in Nairobi Securities Exchange in Kenya. The study finding shows a negative relationship between aggressive investment and financing working capital policy and return on assets.
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Javid & Marie Zita,( 2014) investigated the working capital policies and corporate performance for 20 companies in cement sector in Pakistan. Their study result showed when increasing aggressive investment policy ratio, the aggressiveness decreases, and return on assets, net profit margin and return on equity and tobin’s q reduces. This research study stressed that adopting aggressive working capital policy; firms cannot make more profit and creates firm value. Furthermore, the study revealed that managers could not increase return on assets based on book-value performance if they follow aggressive working capital policy. This study result is similar with previous studies conducted by Afza & Nazir, (2007), Padachi, (2006) and Raheman & Nasr, (2007) Palani & Mohideen, (2012) has studied aggressive working capital policy’s impact on firm profitability of 204 Indian firms listed in Bombay Stock Exchange during 2002-2010 for 16 different industrial groups. The study tested its relationship with take into economic condition represented as gross domestic per capita as a control variable. Panel data regression analysed using E-view software and results showed a negative relationship between aggressive investment and financing policy and return on assets. Amiri (2014) also reported relationship between working capital policies and profitability for 93 firms for five years through applying systematic elimination of companies not related to financial sector. The study used E-view software to do data analysis and showed no significant relationship between aggressive investment and financing policy and return on assets. Hassani & Tavosi, (2014) surveyed the effects of working capital policies on liquidity for companies listed in Tehran Stock Exchange for a period of 2006-2012. However, the study noted that companies used aggressive working capital policies, risk level of firm’s increases. Therefore, the study concluded an aggressive investment and financing policy had a negative relationship with liquidity.
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2.3.2 Working Capital Policies-Sri Lankan Context Pandey and Perera (1977), empirically investigated working capital policies effect on corporate profitability. The study focused on the private manufacturing companies listed in Colombo Stock Exchange and found that most of the companies in Sri Lanka follow informal working capital policies (cited in Pirashanthini et al., 2013). Bei & Wijewardana, (2012) conducted an empirical research to investigate working capital policies impact on firm performance of listed companies in Colombo Stock Exchange, Sri Lanka during 2002-2009. This study identified main three working capital policies as aggressive, conservative, matching, and used multiple-regression analysis to test factors that influence on working capital policies and profitability. The study proved that different types of working capital policies affects differently for firm efficiency, liquidity and profitability of a company. Pirashanthini et al., (2013) analyzed the relationship between aggressive working capital policies on profitability for twenty randomly selected companies in manufacturing sector listed in Colombo Stock Exchange from 2008-2012. The study used secondary data for correlation and regression analysis to measure the impacts of working capital policies on firm’s profitability. From the study, results showed a R2 of 0.132. However, no variables are statistically significant at significant level 5% and study concluded no significant relationship exist between aggressive investment and financing policy with return on assets. Bandara & Weerakoon, (2011) empirically studied impacts of working capital management practices on firm value for seven different sectors of companies listed in Colombo Stock Exchange consisting 74 companies for a 5 years period from 2005-2009. The study identified working capital practices into three different types as aggressive, moderate and conservative approach and used as independent variables and firm value measured through market value added and economic value added used as a dependent variables. The panel data regression employed to test the relationship and result showed mix results for different working capital approaches. The study conclude that firms following moderate working capital approach may 13
able to improve firm’s value by resulting higher economic value added and market value added of firms in Sri Lanka. Lingesiya & Nalini, (2011) had an attempt to study the relationship working capital management and its policies with profitability for 30 manufacturing firms listed in Colombo Stock Exchange for a period of 2006-2010. Their study result reported a negative relationship between working capital management and its policies and profitability. The study used pooled OLS regression analysis and showed significant p-value for aggressive investment and financing policy and debt ratio, as 0.0020, 0.0603 and 0.0260 respectively. In addition, study stressed that companies can further enhanced its profitability if they manage working capital policies properly. Subramaniam & Anandasayanan, (2011) studied aggressive working capital policies and conservative working capital policy impact on profitability for 70 listed firms from 16 sectors in Colombo Stock Exchange during 2003-2009.
This study used aggressive investment and
financing policy as explanatory variables to measure the aggressive working capital policies. While, profitability measured through return on equity, return on assets, and size of the firms, sales growth and firm leverage as control variables. Using STATA Software, run regression model and result found a negative relationship between working capital policies and profitability. Addition, study pointed out that investors believe firms with less equity and longterm debt may able to create value for them and earn high profit. Murugesu, (2013) indicated aggressive investment working capital policy has a negative impact on manufacturing company’s profitability in Colombo Stock Exchange. The study selected 20 manufacturing firms using random sampling method for over five years from 2008-2012. The study used three dependent variables (return on equity, return on assets and tobin’s q) to measure profitability. An aggressive investment and financing policies are taken to measure the aggressiveness of working capital policies. Correlation matrix and regression analysis used to investigate the existing relationship between aggressive working capital policies and profitability. The findings of the study showed no significant impact on aggressive investment policy. 14
2.4 Critical Reviews and Conclusion and Variables Selection 2.4.1 Researcher’s Critical Review Based on the above literatures, most of the studies found a negative relationship between working capital policies and financial performance. All above studies confirmed for an existing relationship between working capital policies and financial performance. Some studies findings showed working capital policies have strong impacts on profitability. However, all these researches prove the degree of aggressiveness varies from one study to another. This variation arisen may be due to several reasons such as resource constraints, variation in data sample and so on. Several studies confirmed that if a firm follow an aggressive investment policy also follows an aggressive financing policy. Working capital policy and business itself have some characteristics related. Researcher observes few studies mentioned the reasons behind the selected samples and reason for systematic elimination of company’s nature of business. Further, it is very evident that different researchers used different financial performance measurement to calculate profitability. Some literatures used return on assets, return on equity, net profit margin, gross profit margin and net operating income. However, more than 80% of the above research studies used return on assets as profitability measures as it shows actual firm’s return in relation to all its resources employed. Based on research studies carried out in Sri Lanka, especially for the same sector anylsis, some researchers have found no significant relationship between working capital management and profitability. Likewise, Ajanthan, (2013) Niresh, (2012) Pirashanthini et al., (2013) and Ramesh & Balaputhiran, (2013), conduct research on manufacturing sector companies listed in Colombo Stock Exchange. However, except later all other studies found no significant relationship between working capital management and profitability. The later study results found a negative relationship exists between working capital management and profitability of the manufacturing companies in Colombo Stock Exchange. This may be possible due to the different statistical
15
analysis of data, however it contradicts with credibility criteria for mentioned by (Saunders et al., 2009). 2.4.2 Variables Selection Based on above literature reviews, majority studies use aggressive working capital policies and take into account two variables as independent variables to measure aggressiveness working capital polices. They are total current to total assets (AIP) and total current liabilities to total assets (AFP). There are various ratios to measure profitability. Nevertheless, most studies use return on assets (ROA), return on equity (ROE), and tobin’s q and therefore for this study researcher choose return on assets (ROA) to measure corporate profitability. Following are the formulae to calculate the variables as used in previous studies and current study followed same method to calculate the ratios.
AIP =
Total Current Assets(TCA) Total Assets (TA)
×100%
If AIP ratio is high, means company following a conservative investing policy and if it is low means, company is adopting aggressive investing policy. It interprets to mean that company has fewer liquid assets compared to long-term fixed assets. Whereas in conservative investment policy company have, more liquid assets compared to fixed assets. The positive beta coefficient of this ratio means aggressive investment policy and it will have negative impact on the corporate profitability. The negative beta-coefficient of this ratio shows conservative investment policy and it will positively affect corporate profitability.
AFP =
Total Current Liabilities (TCL) ×100% Total Assets (TA)
If AFP ratio is higher, it means company is following an aggressive financing policy. This means company is maintaining high current liabilities than long-term debt. Whereas, low ratio indicates that company is adopting a conservative financing policy. Therefore, positive beta 16
coefficient indicates aggressive financing policy have positive impact on profitability while negative beta-coefficient specifies conservative policy and have negative impact on profitability.
ROA =
Operating Profit income & tax Total Assets (TA)
before ×100%
In all previous research, researchers have put the same assumption based on the beta coefficient of these variables. 2.4.3 Conclusion of Literature Reviews It is necessary to point out that above-mentioned reviews taken from various sources such as research paper developed around the world. Source of information mainly includes published sources such as journals. In over all, numerous working capital management and its policies research studies carried out for the developing regions in South Asian compared to the other regions in the World. Still fewer studies conducted to show a practical approach of working capital policies in a qualitative research aspect. Kieschnick & LaPlante, (2012) had an attempt to study the working capital management and shareholders wealth in United States of American companies in a practical approach. Mostly, all the studies reviewed in the above are based on the secondary data. From the reviews of previous studies are evident that there is clear link between working capital policies and profitability. From the previous studies it is obvious that majority of the research studies found similar results. Majority researchers identified that there is a negative relationship between aggressive working capital policies and firm’s profitability. It means most of the profitable firm do not follow aggressive working capital policy rather they follow a conservative or moderate working capital policy to manage its-short-term liabilities and assets. Further, total debt ratio is also affecting profitability of the company. However, there are also studies found positive relationship exist between working capital policy and profitability like Weinraub & Sue Visscher, (1998), Gill et al., (2010) and Yogendrarajah & Thanabalasingam, (2014). 17
The above-mentioned studies carried out with different size of samples data size, time-periods, in different countries for different sectors or industries and they are distinctively varied from to one another. In addition, researcher would like to note that very few researches conducted so far, for the Sri Lankan context covering companies from all the sectors in Colombo Stock Exchange and all above studies conducted are outdated to represent current financial performance analysis in Sri Lankan companies. Hence, this study focuses on the recent data published covering five years period from 20092013 and it will give more current situation to show impact of aggressive working capital policies on corporate profitability. Therefore, this study will investigate impact of working capital policies on profitability for listed companies in Colombo Stock Exchange. Research Methodology is outlined in the next chapter three.
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CHAPTER THREE 3.0 Research Methodology 3.1 Introduction This chapter explains the framework of the research study. It includes the research design, total population and sample size taken and statistical method plan to use for data analysis. 3.2 Conceptual Framework Conceptual framework show overall picture of research under taken in this study as a guide, to test the relationship between independent and dependent variables. Based on previous, literatures this conceptualization is formulated. Dependent variable is corporate profitability measured through return on assets ratio as this ratio indicates how well company used its total assets to generate profit. Independent variables are aggressive investment and financing working capital policies. These two policies indicated degree of aggressiveness of working capital policies measured through ratios total current assets to total assets and total current liabilities to the total assets respectively.
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3.2.1 Illustration of Conceptual Framework
Independent Variables
Dependent Variables
Working capital policies
Corporate Profitability
AIP (TCA/TA)
ROA (EBIT/TA)
AFP (TCL/TA)
Aggressive
financing
policy
(AFP)
Control Variable Debt ratio DR (TL/TA) Figure 1: Conceptual framework
Source: Author’s own work, (2015)
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3.3 List of Hypotheses Table 1 List of Hypotheses
H01: There is no relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange Hypothesis 01 Ha1:There is a relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange
H02:There is no relationship between aggressive financing policy
and return on assets of the selected listed companies in Colombo Stock Exchange Hypothesis 02 Ha2:There is a relationship between aggressive financing policy and return on assets of the selected listed companies in Colombo Stock Exchange
H03: There is no impact between debt used by the listed companies in Colombo Stock Exchange and its profitability Hypothesis 03 Ha3: There is a impact between debt used by the listed companies in Colombo Stock Exchange and its profitability
Source: Author’s own work, (2015)
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3.4 Population and target population The population of the study is listed companies in Colombo Stock Exchange. According to Colombo Stock Exchange Web site, (2015) there are 292 listed companies since year 1990 to 2014 which covers 20 sectors. However, excluding service sector companies and other inappropriate companies with unavailable data and new companies, target population becomes 162 listed companies that cover 12 sectors. 3.5 Sample size The target population of 162 listed companies, taken sample size is 114 companies, which covers 12 business sectors by Anderson sampling table at 95% significant level. However, due to the negative ratio figures data, further some companies eliminated from the sample data sample and final sample size is 68 companies consisting nine sectors for five years period from 2009-2013. 3.5.1 Research design This study is a quantitative research by using deductive approach. Hypothesis testing of this study investigates through regression analysis study with minimal interference for selected listed companies. A cross-sectional study may undertake, as it is easy and less time consuming. In such study, data gathers just once to answer the research questions (Sekaran & Bougie, 2010). 3.5.2 Data collection method The secondary data is collected using financial statements of companies and other available sources in Colombo Stock Exchange Web site.
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3.5.3 Sampling method The probability distribution of stratified sampling technique will use for this study to avoid biasness. Furthermore, Saunders et al., (2009) analyze a decision tree to decide sampling method. Therefore, sample size covers 41% of the target population; sample data representation may help to give unbiased generalisation.
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Trading (7/162)*114= 5
3.5.4 Illustration of the sample Manufacturing (33/162)*114=24
Stores and supplies (4/162)*114= 3
Population 292 Motors (6/162)*114= 4
Target population 162
Plantations (18/162)*114=13
Sample 114
Land & property (19/162)*114= 13
Hotels& Travels (30/162)*114= 21
Chemicals & Pharmaceuticals (9/162)*114= 6
Beverage food & Tobacco (19/162)*114= 13
Footwear& Textile (3/162)*114= 2 Figure 2 Sample of companies in the CSE
Construction & Engineering (3/162)*114= 2 Source: Colombo Stock Exchange, CSE, (2015) Diversified Holdings (11/162)*114= 8
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As seen above figure 2, sample size is 114 companies from 12 sectors in Colombo Stock Exchange. Nevertheless, due to the negative ratio figures, the sample data reduced further. In previous studies, researchers also eliminated negative data before the actual process of data analysis. Therefore, in this study, after careful elimination of negative ratio figures, sample size reduces to total 68 companies consisting nine sectors of Colombo Stock Exchange. The following table shows details frequency of recent sample size taken for final data analysis. In addition, researcher would like to mention that after reduction of negative ratio figures three sectors (food and textile, construction, engineering and chemical, and pharmaceuticals) left just one company to represent the sector. Therefore, due to small size for entire sector representation, these three sectors eliminated from sample to avoid replication error of data with other sectors. Table 2 Final sample size
# 1 2 3 4 5 6 7 8 9
Sectors Manufacturing Beverage Food and Tobacco Diversified Holding Hotels and Travels Land and Property Plantations Trading Motors Stores and Supplies Total observations
Five years observation 75 50 30 45 30 55 20 20 15 340
Source: Author’s own work (2015)
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# companies 15 10 6 9 6 11 4 4 3 68
3.6 Proposed statistical method 3.6. 1 Statistical method used by previous research studies Table 3 Statistical method used by previous researchers
Authors
Oloo& Mwangi, (2014)
Title
Variables
Effect of Aggressive Financing Policy on Profitability of listed Companies at the Nairobi Securities Exchange, Kenya
Independent Variable: Aggressive Financing Policy
Method
descriptive statistics, regression analysis
Dependent Variable: Gross Operating Profit Pirashanthini Working Capital Approaches and Independent variables: et al., ( 2013) Firm’s Profitability of manufacturing companies in Sri Aggressive Investment Policy and Aggressive Lanka Financing Policy
Regression and correlation
Dependent variables: Return on assets, Return on Equity Independent Variables: regression Aggressive Investment analysis Policy and Aggressive Financing Policy Dependent Variables: Return on Assets and Tobin’s Q Control Variables: Size, Growth and Debt ratio(Leverage)
Nazir & Impact of Aggressive Working Afza, (2009) Capital Management Policy on Firm’s Profitability
26
Al-Shubiri, Analysis of the relationship (2010) between working capital policy and operating risk: an empirical study on Jordanian industrial companies
Independent Variables: AIP regression and AFP analysis Dependent Variables: ROA, ROE and Tobin’s Q
T. Afza and Is it better to be aggressive or Independent Variables: cross-sectional M. S. Nazir conservative in managing working Aggressive financing regression (2009) capital? policy, aggressive model investment policy Dependent Variables: Return on assets, return on equity Khaksarian, A study on the effect of working Independent variables: regression (2014) capital management on AIP and AFP model profitability on Cement and Dependent Variables: Petrochemical industries: Evidence Return on Assets and from Tehran Stock Exchange Tobin’s Q Control Variables: Size, Growth and Debt ratio (Leverage) Javid & Impact of Working Capital Policy Independent Variable: panel data Marie Zita, on Firm’s Profitability: AIP regression (2014) A Case of Pakistan Cement Dependent Variables: ROA, analysis Industry ROE, NPM, and Tobin’s Q Control Variables: Size, Growth and Leverage(Debt Ratio) Source: Author’s own work based on the previous research studies evaluated in the report,(2015)
3.7 Conclusion of Research Methodology In reference to above table 3, shows main base papers of previous research studies directly related to the variables selected in the conceptual framework. Consequently, present study mainly focuses regression analysis to test their hypothesis. Therefore, author intends to use regression model to test hypothesis using Gretl version gretlw32.exe. In the next chapter four, outlines the data analysis.
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CHAPTER FOUR 4.0 Data Analysis 4.1 Introduction The study data analysis consists of five years data for 68 companies. Therefore, researcher used panel data that includes data from year 2009 to 2013 covering of nine different sectors of Colombo Stock Exchange. Hence, multiple panel regression analysis conducted using Gretl version gretlw32.exe, to find out the impact of aggressive working capital policy on firm’s corporate profitability. This study used dummy variables to distinguish between the aggressive and conservative working capital policies. D11 used for Aggressive working capital while D22 represents the conservative working capital policies. It is as follows: Table 4 Explanatory Variables- Dummy
Dummy Variables for aggressive working capital policies Explanatory Variables D1 AIP 0 AFP 1 DR 1
D2 1 0 0
Source: Author’s own work, (2015)
1
D1: TCA/TA is less than 50 % means aggressive investment policy, TCL/TA is higher than 50% means aggrieve financing policy. Debt ratio (DR) also categorized as high and low leverage. As most previous research stated that AFP policy associated with high leverage ratio (Deloof, 2003) D1 follows higher DR ratio which is higher than 50%. 2 D2: represent conservative working policy. AIP is more than 50%, AFP less than 50% and DR is less than 50%.
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4.2 Basic Assumption Tests According to Berry, (1993) it is necessary to perform essential assumption tests before running a regression analysis. This basic assumption tests run to check research data is good for the model. The results of normality, multicollinearity, and heteroskedasticity tests are shown in Appendix A. In this study, normality test (table 14) confirms using three different, Jarque-Bera, DoornikHansen and Shapiro-Wilk test. The significance level of these tests (p-values) is very small. This indicates that residuals are not normally distributed. Although referring to the large number of observations and central limit theorem, this assumption can ignore normality of the residuals (Hassani & Tavosi, 2014, p.34). In referring to Appendix A, table 15 showed there is no multicollinearity in the variation inflation factor. In addition, Durbin-Watson shows 1.558067; means there is no auto-correlation problem of residuals in the model. White’s test (LM test) for heteroskedasticity (Appendix A table16,) shows residuals are homogenous as p-value (0.0709) is more than 0.05. 4.3 Descriptive Statistics Table 5 Descriptive Statistics
Variable ROA AIP AFP DR Variable ROA AIP AFP DR
Mean 13.8899 0.691176 0.135294 28.5816 Std. Dev. 15.1977 0.462689 0.342542 20.3282
Median 10.2000 1.00000 0.000000 24.0000 C.V. 1.09416 0.669423 2.53183 0.711234 29
Minimum 0.0700000 0.000000 0.000000 0.110000 Skewness 3.39227 -0.827589 2.13255 0.469779
Maximum 125.020 1.00000 1.00000 87.2700 Ex. kurtosis 14.7372 -1.31510 2.54777 -0.751689
140 120 100 ROA
80
AIP 60
AFP DR
40 20 0 Mean
Median
Minimum
Maximum
Figure 3 Statistics Summary
Descriptive statistics use to analyze the data to categorize the main characteristics outlining their variability i.e. mean, standard deviation, minimum and maximum. Above table 5 presents descriptive statistics of all variables used in data analysis for all 68 firms in Colombo Stock Exchange. Return on assets, aggressive investment and financing policy and debt ratio all have a positive mean value, which ranges from a low of 13.8899 to 28.5816. Standard deviation for the variables of return on assets, aggressive investment policy, aggressive financing policy, and debt ratio are 15.1977, 0.4626, 0.3425, and 0.20.33 respectively. The maximum standard deviation is for debt ratio, the lowest standard deviation is for return on assets. The maximum value is 125.020 for return on assets and lowest 87.2700 for debt ratio. The minimum value for return on assets and lowest value for debt ratio are 0.0700000, and 0.110000 respectively.
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4.4 Correlation Matrix Correlation matrix is an important test to check auto-correlation of the variables in the models. Table 6 Correlation Matrix
Correlation coefficients, using the observations 1:1 - 68:5 5% critical value (two-tailed) = 0.1064 for n = 340 ROA 1.0000
AIP
AFP
DR
-0.2127
0.2946
0.2425
ROA
1.0000
-0.2940
-0.4115
AIP
1.0000
0.2984
AFP
1.0000
DR
Table 6 indicates the correlation matrix of dependent and independent variables for the selected companies in Colombo Stock Exchange 5 years from 2009 to 2012. Dependent variable return on assets has a positive correlation with aggressive investment policy and debt ratio. However, it has a negative correlation with aggressive financing policy. The result also indicates is no problem of auto-correlation among selected variables. Most researchers considered presence of auto-correlation if correlation is 0.70 or above (Sekaran & Bougie, 2010, p.352).
4.5 Panel Diagnostics Panel diagnostics involve main three tests primarily run for panel data to suitable model regression. It includes Hausman test, Bresuch-Pagan LM test and Chow test. Following are results of these tests using Gretl version gretlw32.exe.
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4.5.1 Hausman test Hausman test involves main two tests; they are fixed effects and random effects. Fixed effect model assumed firm specific intercept across sectors, which captures the effects of those variable particular to that specific firm, and eliminate anything that is time invariant. Whereas, random effect model just as opposite to it assumed that individual firm intercepts which varies within the sectors and omitted variables effects are not fixed (Clark & Linzer, 2012). If p-value is more than 0.05, random effect is desirable. If it is less than 0.05, than choice will be fixed effect model. To test hypothesis to select which model is suitable as follows: H0: Random effect is appropriate.
H1: Fixed effect is appropriate
Table 7 Hausman Test Statistic
Hausman test statistic: H = 7.30884 with p-value = prob(chi-square(3) > 7.30884) = 0.062679 (A low p-value counts against the null hypothesis that the random effects model is consistent, in favor of the fixed effects model.) Table 8 explains the results of the Hausman specification test. The p-value is 0.062679 which is higher than 0.05. Based on Hausman Specification test rejected null hypothesis and accept alternative. Therefore, it indicates that fixed effect is suitable for our data analysis. 4.5.2 Breusch - Pagan Lagrangian Multiplier Test
The Breusch-Pagan Lagrange multiplier (LM) test decides between random effect model and pooled OLS regression. It gives details of major existing variation among units. In case of no variation across sectors, pooled OLS regression is desirable than random effect model. (Clark & Linzer, 2012).
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Table 8 Breusch-Pagan Lagrange multiplier Test
H0 :
The pooled OLS model is desirable, H1:
random effects model is desirable
Breusch-Pagan test statistic: LM = 0.00582474 with p-value = prob (chi-square(1) > 0.00582474) = 0.939165 (A low p-value counts against the null hypothesis that the pooled OLS model is adequate, in favor of the random effects alternative.) Table 9 shows the probability value of LM test is greater than 0.05 (P-value0.939165). Based on p-value accept null hypothesis that indicates that pooled OLS model is better than random effects model. It conclude that Pooled OLS regression model is suitable. This interprets to mean that there are no differences of aggressive working capital policies adopted by different firms among the sectors in Colombo Stock Exchange. 4.6.3 Chow Test or F Test Chow test applies for the selection of fixed effect model and Pooled OLS Model. This gives details of the model according to its nature of data. The hypothesis of the chow test is: H0: The pooled OLS model is adequate H1: Fixed effect model is adequate. Table 9 Chow Test
Augmented regression for Chow test OLS, using 340 observations Dependent variable: ROA coefficient
std. error
t-ratio
p-value
const
15.7021
3.32488
4.723
3.44e-06 ***
AIP AFP DR
-5.17719 6.35117 1.21019
2.56675 3.28688 3.58636
-2.017 1.932 0.3374
0.0445 ** 0.0542 * 0.736
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splitdum sd_AIP
0.212154 1.95116
4.76562 3.72599
0.04452 0.5237
0.9645 0.6009
sd_AFP sd_DR Mean dependent var Sum squared resid R-squared F(7, 332) Log-likelihood Schwarz criterion
10.1288 -3.75414 13.88985 68809.61 0.12119 6.54053 -1385.165 2816.962
5.00081 -3.75414
2.025 -0.7723 S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn
0.0436 ** 0.4405 15.19768 14.39645 0.102661 3.04E-07 2786.33 2798.536
Chow test for structural break at observation 34:5,F(4, 332) = 1.52626 with p-value 0.1941
The result of p-value in chow test (table 9) is 0.1941 that is greater than 0.05. Therefore, null hypothesis of chow test accepted and reject the alternative. Thus, chow test suggest that Pooled OLS model regression is suitable for our data set. 4.7 Panel Data Model Selection Table 10 Tests applied to Panel data
Test Applied
P-Value
Model Selection
Hausman test LM Test Chow Test
0.0958466 0.939165 0.1938
Random Effect Pooled OLS Pooled OLS
Based on above table 10 concludes that majority test results showed that Pooled OLS regression model is suitable for panel data analysis. 4.8 Pooled OLS Regression analysis According to table 10, majority tests are in favour of Pooled OLS regression, therefore study select Pooled OLS regression for all companies. The regression equation is as follows: 34
ROAit = β0 + β1 (AIP)it + β2 (AFP)it + β3 (DR)it + +eit Where ROA is return on assets, AIP is aggressive investment policy, AFP is aggressive financing policy, DR is debt ratio, e is error term. Model 1: Pooled OLS, using 340 observations Table 11 Pooled OLS Regression Analysis
Included 68 cross-sectional units Time-series length = 5 Dependent variable: ROA
Coefficient
Std. Error
t-ratio
p-value
const
16.4761
2.35887
6.9848
<0.00001
***
AIP
-4.24322
1.84893
-2.2950
0.02235
**
AFP
10.9187
2.47898
4.4045
0.00001
***
DR
-1.33485
2.4063
-0.5547
0.57945
Mean dependent var
13.88985
S.D. dependent var
15.19768
Sum squared resid
70074.93
S.E. of regression
14.44148
R-squared
0.105030
Adjusted R-squared
0.097039
F(3, 336)
13.14387
P-value(F)
3.90e-08
Log-likelihood
-1388.263
Akaike criterion
2784.526
Schwarz criterion
2799.841
Hannan-Quinn
2790.628
rho
0.044598
Durbin-Watson
1.558067
* Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
The results of pooled OLS Model revealed in the above table 11, showed aggressive investment and financing policy were statistically significant at level 5% and 1% respectively. The p-value of return on assets, aggressive investment policy, and aggressive financing policy are 0.00001, 35
0.02235, and 0.00001 respectively. The p-values of all variables show that are highly significant except debt ratio (p-value=0.57945). The beta-coefficients of Pooled OLS regression indicate that an aggressive investment and financing policy have positive impacts on corporate profitability (return on assets) of selected listed companies in Colombo Stock Exchange. 4.9 conclusion of data analysis Based on the basic assumption tests our study sample data set is fit to the ordinary linear model. The residuals are not normally distributed. On the other hand, there is no problem of hetereoskedasticity and multicollinearity (auto-correlation) in our data set. Based on panel diagnostic, which includes Hausman, Breusch-Pagan and Chow test, majority test agreed that pooled OLS regression method is suitable. The result of pooled OLS regression in table 11 showed a significance p-value with aggressive investment and financing policy and return on assets. While, it shows that debt ratio is not statistically significant. The next chapter evaluate the detailed discussion of these findings.
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CHAPTER FIVE 5.0 Discussion of Findings 5.1 The first hypothesis: There is a relationship between aggressive investment policies on return on assets The results of Pooled OLS model in table 11 shows that aggressive investment policy has a significant (P-value=02235) impacts on corporate profitability of selected listed companies in Colombo Stock Exchange (α=0.05). This can interpret as that when a company adopt more aggressive investment policies, higher the chances of enhancing its profitability. Similarly reducing aggressive investment policy may lower firm’s profitability. The negative betacoefficient (β= -4.24322) of aggressive investment policy indicate a positive relationship existed between aggressiveness working capital investment policies and return on assets (profitability). This means as total current assets to total assets (AIP) decreases the level of aggressiveness increase and firm return on assets decreases. Whereas, total current assets to total assets ratio increases the aggressiveness decreases and increase the return on assets. Therefore, there is a positive relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange. It also means most of the Sri Lankan companies listed in Colombo Stock Exchange improve its profitability by maintaining high liquid assets compared to total assets. This finding led to reject first null hypothesis and accept first alternative hypothesis. (Ha1: There is a relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange).
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5.2 The Second Hypothesis: There is a relationship between aggressive financing policies and return on assets According to Pooled OLS regression results in the above table 11, aggressive financing policy are significant (P-value= 0.00001) on corporate profitability of selected listed companies in Colombo Stock Exchange (α=0.01). The positive coefficient (β=10.9187) indicates a positive relationship between the aggressiveness financing policy and corporate profitability. This indicates that when company adopt high level of aggressive financing policy, the level of aggressiveness goes down and yields a negative return. This suggests that listed Sri Lankan companies enhance it profitability by having low ratio of total current liabilities to total assets (AFP). Further, this is an indication that profitable companies take longer days to pay the bills as mentioned in the previous studies (Afza & Nazir, 2007). Conversely, some previously researchers argued that taking too many days to settle the payment to creditors may deteriorate the relationship between them and it brings a bad brand image to the company. This finding led to reject second null hypothesis and accept second alternative hypothesis. (Ha2: There is a relationship between aggressive financing policy and return on assets of the selected listed companies in Colombo Stock Exchange). 5.3 The Third Hypothesis: There is an impact between debt used by the listed companies in Colombo Stock Exchange and its profitability Based on results of pooled OLS regression model in table 11, control variable, debt ratio is not significant at 5 % of significant level (p-value=0.57945). Even though it is not significant, results indicates there is a positive relationship between debt ratio and return on assets. It is consistent with the traditional risk theory of high risk and high return.
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In previous empirical studies evaluated that financial distress increased when company follows aggressive financing policy. Therefore, companies in Colombo Stock Exchange need careful management on aggressive financing policy to reduce the level of financial distresses caused due to increasing debt ratio along with high profitability. Managers need to understand the sufficient and adequate level working capital requirement needed to run their day-to-day operation. Previous researchers such as Deloof, (2003) noted this issue trade-off risk and return in their study to avoid liquidity and bankruptcy problems. This finding led to reject third alternative hypothesis and accept third null hypothesis. (Ho3: There is no impact between debt used by the listed companies in Colombo Stock Exchange and its profitability). The present study results above discussed are similar and consistent with previous studies of of Weinraub & Sue Visscher, (1998); Murugesu, (2013); Yogendrarajah & Thanabalasingam, (2014); Ramachandran & Janakiraman, (2009) (Gill et al., 2010) findings that there is a positive relationship between aggressive working capital policy and corporate profitability. However, this study results contradicts with Usman et al., (2014); Subramaniam & Anandasayanan, (2011); Lingesiya & Nalini,(2011); Afza & Nazir, (2007); Afza & Nazir, (2009); Oloo & Mwangi,(2014); Khaksarian, (2014) and Al-Shubiri, (2010) who found a negative relationship between aggressive working capital policy and firm’s profitability. Moreover, this result are not consistent with study findings of Ajanthan, (2013); Bei & Wijewardana, (2012); Pirashanthini et al., (2013); and Niresh, (2012) who found no significant relationship between working capital policies and firm profitability of listed companies in Sri Lanka.
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5.4 Overall Summary of Pooled OLS regression model Table 12 Overall Model Summary
Mean dependent var
13.88985
S.D. dependent var
15.19768
Sum squared resid
70074.93
S.E. of regression
14.44148
R-squared
0.105030
Adjusted R-squared
0.097039
F(3, 336)
13.14387
P-value(F)
3.90e-08
Log-likelihood
-1388.263
Akaike criterion
2784.526
Schwarz criterion
2799.841
Hannan-Quinn
2790.628
rho
0.044598
Durbin-Watson
1.558067
From the table 12, overall model summary, the value of R-squared of Pooled OLS regression is 0.105030. This means that 10.50 % variation in independent variables (AIP, AFP and DR) can be accounted in the dependent variable return on assets. The standard error of estimated regression is 89.5% and adjusted R-Squared is 0.097039. The overall model is fit, as F-statistics is significant (3.90e-08) but very small. Durbin-Watson value is 1.558067, which indicated no problem of auto correlation in the data set. This means that 10.50% of changes in dependent variable (ROA) can be explained by explanatory variables (AIP,AFP and DR) and remaining 89.5% variation are error term due to other factors that are not identified in this research that may impact on the profitability of selected companies of Colombo Stock Exchange. 5.5 Summary of findings The following table 13 summarised the data findings results for the aggressive working capital policy impacts on corporate profitability of listed companies Colombo Stock Exchange. The sample data of 68 companies comprise of nine different sectors for total five years and these data collected from the respective companies’ annual reports .Using Gretl version gretlw32.exe run the Pooled OLS regression in order to find out the relationship between aggressive working capital policy and corporate profitability. 40
Table 13 summary of Results
No Hypothesis
Results
1 H01:There is no relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange Ha1:There is a relationship between aggressive investment policy and return on assets of the selected listed companies in Colombo Stock Exchange 2 Ho2:There is no relationship between aggressive financing policy and return on assets of the selected listed companies in Colombo Stock Exchange Ha2:There is a relationship between aggressive financing policy and return on assets of the selected listed companies in Colombo Stock Exchange 3 Ho3:There is no impact between debt used by the listed companies in Colombo Stock Exchange and its profitability
H0 rejected
Ha accepted
Tool
positive
Ha accepted
positive
H0 accepted
no significant but positive
Regression
H0 rejected
Ha3:There is an impact between debt used by the Ha rejected listed companies in Colombo Stock Exchange and its profitability
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Sign
CHAPTER SIX 6.0 Recommendation and Conclusion 6.1 Conclusion In corporate finance literature, still there are few research studies carried in working capital policy, especially, regarding the capital structures of short-term finance and different types of policies to manage this working capital. In this study, researcher presented an empirical study in Sri Lankan companies’ working capital policies and profitability including different sectors of companies in Colombo Stock Exchange. The sample data includes 68 companies from 09 different sectors listed in Colombo Stock Exchange for five years from 2009-2013. The main purpose of this study is to find out the impact of aggressive working capital policy on corporate profitability of selected listed companies in Colombo Stock Exchange during 20092013. The specific research objectives are to find out the relationship between the aggressive working capital policies (investing and financing) and corporate profitability and to find out the financial distress and effectiveness of working capital policies. The pooled OLS regression model shows a positive relationship between aggressive investment and financing policy and corporate profitability of the selected companies in Colombo Stock Exchange. Debt ratio and return on assets have a positive impact but it is not statistically significant at 5%. This finding is similar with previous study of Weinraub & Sue Visscher, (1998); Murugesu, (2013); Yogendrarajah & Thanabalasingam, (2014); Ramachandran & Janakiraman, (2009) and add knowledge to the existing working capital policies literatures that showed a positive relationship between aggressive working capital policies and profitability. On the other hand, study findings are contradicts with previous studies of Lingesiya & Nalini, (2011),Afza & Nazir, (2007), Afza & Nazir, (2009), Oloo & Mwangi, (2014), Khaksarian, (2014) and Al-Shubiri, 42
(2010) who found a negative relationship between aggressive working capital policies and profitability. This may be due to several factors such different measurement of the variables and different characteristics of different industries and the different type of data and data analysis tool used in the study. Pointing out these factors, in the next section researcher recommends some future research areas can apply to this research study. In addition, the research objectives of the study are achieved as the study findings revealed that there is a relationship between aggressive working capital policies and profitability. The study showed a positive relationship between aggressive working capital policy and corporate profitability of companies listed in Colombo Stock Exchange. The results showed no statistical significance between debt ratio and return on assets but there is a positive relationship between them. 6.2 Limitation and recommendation for future research The study sample data limited to random selected 09 industrial sectors in Colombo Stock Exchange comprising 68 companies and research period of research is limited to only five years. Therefore, the results may be indicative. Researcher recommends to future research to be conduct in the following areas to get more accurate and appropriate findings. Future research can conduct with additional explanatory variables along with aggressive working capital policies to find the effects of more appropriate results. Previous researchers Afza & Nazir,( 2009); Deloof, (2003), include control variables like real Gross Domestic Product, sales growth and firm size in their research study Further, future research can investigate aggressive working capital policies with more additional measurement of corporate profitability such as tobin’s q and return on equity Study can conduct to find out different industrial factor impact with aggressive working capital policies along with profitability like previously (Afza & Nazir, 2009) conducted study for listed companies in Karachi Stock Exchange ,Pakistan
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In addition, future researchers can also conduct study of aggressive working capital policy with liquidity, risks and firm value. previously Bandara & Weerakoon, (2011) investigated seven different sectors in Colombo Stock Exchange consisted 74 companies to test firm value and risks impact on working capital policies with economic value added and Market value added. In addition, a research study can also focus to companies operating in the financial sector only. Lastly, future researchers may also conduct empirical investigation on financial performance and working capital policies with CEO characteristics. Gomes, (2013) has empirically investigated the characteristics of CEO age and education qualification level has any impact on the working capital management and profitability of Portugal firms. This study results showed a positive relationship between CEO characteristics with profitability. Further, they noted that older Male Portugal CEO with high qualification level as CEO behind almost all the profitable firms in Portugal.
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References
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Appendices
Appendix A
Normal Distribution different tests Table 14 Different test of normality
Tests Doornik-Hansen test Shapiro-Wilk W Jarque-Bera test
Test for normality of SECTORS: D_H Signif S_W signif 48.3059
J-B
signif
21.6366
2.00E-05
3.24E-11 0.91355
4.60E-13
Multicollinearity- VIF factor Variance Inflation Factors: Minimum possible value = 1.0, Values > 10.0 may indicate a multicollinearity problem. Table 15 Variance Inflation Factors
AIP 1.190 AFP 1.172 DR 1.223 VIF (j) = 1/(1 - R(j)^2), where R(j) is the multiple correlation coefficient between variable j and the other independent variables Properties of matrix X'X: 1-norm = 909 Determinant = 32296701 Reciprocal condition number = 0.016396562
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Heteroskedasticity Table 16 LM test (white's test)
Unadjusted R-squared = 0.034184 Test statistic: TR^2 = 11.622529, with p-value = P(Chi-square(6) > 11.622529) = 0.070939
Appendix B Research creditability: Creditability aspect of this research is judged in main three ways. This as follows: Reliability:-Reliability explains that the results of the study should be if another research is conduct on the same way in different circumstance. (Saunders et al., 2009) stated that is should not be subject bias, observer bias and subject error. Validity:-This research finding is real. The relationship of the two variables show same relationship in reality and do not exist any fictitious numbers or values to make the findings attractive (Saunders et al., 2009). Generalization: Generalization is also known as external validity. This explains the extents of our research results are applicable to other research setting in the same field. In this research, data collected from financial statements of the respective companies in the Colombo Stock Exchange Web site. This data are belongs to real World companies of different Accounts. The values of ratios return on assets (EBIT/TA), aggressive investment policy (TCA/TA), aggressive financing policy (TCL/TA) and debt ratio (TL/TA) calculated from the financial statements of the companies.
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To meet the research development, aggressive investment and financing policy and debt ratio categorise into two major sub- groups, which used to for data analysis. As in this research is focused on aggressive working capital policies only so all the aggressive ratio in dummy variable D1 (i.e AIP less than 50 % while AFP and DR if it is more than 50%).and conservative approach ratios in dummy variable D2. (i.e aggressive investment policy ratio is more than 50 % while aggressive financing policy and debt ratio is less than 50%). Therefore, the findings of this research meet all three criteria of reliability, validity and generalization.
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