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Does Working Capital have impact on profitability?
An Evaluation made through financial position of select Indian Meat Processing Industry
Dr. Mohsin Khan1 Dr. Afzalur Rahman2 Dr. Sibghatullah Nasir3
1 Assistant Professor, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai
2 Professor and Head, Department of Commerce, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai
3 Assistant Professor, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai
ABSTRACT
Working Capital management means management of short-term assets in effective way such as quick conversion of liquid assets into cash. In this paper the author is talking about the Working Capital which performance a vibrantpart in business organization these days. This paper will evaluate the liquidity performance for checking the influence of its on profitability with special reference of Indian Select Meat Processing Industry. The paper is written with an aim to empirically examine the effects of liquidity and working capital components on profitability of select Indian Meat Processing Industry. The study bring out the 08 firms of Indian Meat Companies who are exporting their meat. The study covers through CMIE recordof 10 years from 1stApril 2008 to 31stMarch 2018 in total of 80 firms-year observation. ROA and ROE is considered as dependant variables whereas ACP, AIP, APP and CCC are the components of Working capital as the independent variables.Size of the firms (log of sales) and Gross Working Capital Turnover Ratio have taken as control variables. Multiple regression technique to investigate the functional relationship of working capital components. Depending upon the nature of study and type of data, panel data analysis has been incorporated, including pre-testing in the study to arrive at unbiased results.
Key words: Working Capital, ROE and ROE
1. Introduction
The Meat Industry is growing business, use of height technique makes this business very successful and profitable. Due to adequate production resources, available markets and huge livestock population in
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206 India, there is enormous potential for production, consumption and export of meat. The surplus buffalo meat in the country is showing better profitability with better processing technology. There are more than 50 million people are engaged in meat processing industry. India is largest exporter of buffalo meat and third largest exporter of meat after Brazil & Australia (FAO).
If we take the data of 2017-18 the total export of the animal product is Rs. 29,813.69 Crores/
4,623.05 USD Million. In this following are the major product
(i) Buffalo Meat of Rs. 26033.83 Crores, (ii) Crores, Sheep/ Goat Meat of Rs. 835.75 Crores (iii) Poultry Products is Rs. 552.16 Crores (iv) Dairy Products of Rs. 1196.19 Crores, (v) Animal Casing of Rs.
327.44 Crores, (vi) Processed Meat is Rs. 9.91 Crores, (vii) Albumin is Eggs & Milk is Rs. 83.72 Crores and (viii) Natural Honey is Rs. 653.58 Crore(APEDA 2018).
At this stage of the economics of country, the firms of this industry are facing the problems of generating and using a flexible, balanced and efficient mechanism for the use of working capital in connection with the annual increase in production of the meat.
2. Literature review
The paper carried the literature review under the following heads 2.1Working capital management
Ghosh, S.P. (1983)in this research it is highlighted that due tothe inadequate working capital, the efficiency of short-term debt payment of selected companies was not satisfactory.Further he exposedthat credit management policy of the crane manufacturing is not efficient and result in unembellished difficulties in supervision of working capital along with liquidity.
Filbeck. et.al (2005)suggested the minimization of the fund requirement in current asset to minimize the financial cost. However they expressed that the requirement of current assets or working capital depends on the nature of the industries across the period.
Zakaria (2009) evaluates the Malasian small and medium enterprise stable and dynamic liquidity.His study encouraged the issue to monitor relations between corporate liquidity and earning cash flow in Malaysian SMEs.Although creating an allowance for the dynamic dimension of corporate liquidity.In Malaysia corporate liquidity is influenced by the earning and liquidity in Small and Medium Enterprises.
However, in this study the researcher did not find any relationship among stable measures, current proportions and accelerated proportions with corporate liquidity.
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Chauhan, et.al (2017) checked that the firm is continuously following the financial or not and inferred the importance of working capital and its optimization. Later on he used the partial-adjusting dynamic panel model and cash conversion cycle to measure the net working capital to test the target. However it this research systematic target working behavior of working capital is not found.
2.2Effects of working capital components on profitability
Deloof(2003) in his study shows that the credit period and profitability has opposite relation. In this study he took 1009 large Belgian non-financial firm as sample. Shah and Sana in their research find that the gross profit and inventory conversion period has the negative relation. The research was on the oil and gas company of the Pakistan.
Chisti(2013) in his research found that the working capital effect the liquidity and profitability of the firm. The researcher took the 16 Indian firms listed in BSE from the different sector. Agha, H.(2014) found in his research that the minimizing the inventory turnover positively effect the profit of the firm. However Yadav and Kumar S.B(2014) found that there is no relation in between management of working capital and Profitability. They used the OLS regression technique on 10 large scale steel manufacturing companies.
D.Mathuva (2015)in his study shows the adverse relationship among ACP and cost-effectiveness and CCC and profitibility. He had taken 30 firms that is listed on the NSE. However, the variables of Inventories in days and Average Payable period had the positive relation with profitability.
2.3Liquidity Management
John (1993) has swotted the connection in between liquidity and insolvency expenses enlisting along with linear regression on the panel records of 223 USA companies through of pair of years. This research disclosed that the liquidity proportions were favorably associated to the insolvency prices, however detrimentally linked to the economic make use of.
Lamberson (1995) has checked out exactly how the working capital opening of tiny companies sallies to alter in the degree of economical tasks. An example of fifty agencies was actually removed to perform the research. The writer has used fast as well as current proportions as an amount of liquidity role of organizations. The looking for subjected that the liquidity roles of picked organizations intensified a little throughout enhancement durations, without noticeable adjustment in liquidity throughout economical discontinuances. The research recognized that assets
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208 in working capital as determined through supply to current resources and overall possessions to overall proportions lingered consistent during the course of the duration of the research study.
Islam (1996) while creating his research study on the subject "Working capital management of the decided-on report factories in Bangladesh" has recommended the general liquidity of the factories that were chosen for research study over a duration of a decade coming from 1984 to 1994.
The research subjected damaging capital in those picked study factories. Bench up of a significant part of current resources in stocks and loosened debt-collection plan were actually the leading sources of their poor capital management. At some point, the research confirmed that all these caused extremely certainly not enough posture of liquidity in those picked study factories in Bangladesh.
Kiernan (1999) has actually administered an analysis along with examining the Net Liquid Balance (NLB) technique in identifying company liquidity and also split the agency's overall working capital in to 2 components, the section called for to preserve the agency's functions as well as the agency's excess money information or even NLB. The research additionally incorporated 2 resources of liquidity management, the cash money transformation duration as well as NLB.
Eljelly (2004) has reviewed the connection in between success as well as liquidity in his term paper posted in the diary called as "International Journal of Commerce and Management" along with the newspaper allowed "Liquidity-profitability compromise: An Empirical Investigation in an Emerging Market". He has determined the current ratio as well as money transformation pattern along with an example of 929 Joint stock providers in Saudi Arabia for a duration of 5 years coming from 1996 to 2000. Everyone business that manage yearly audited monetary files have been actually picked for the research overlooking along with electric energy and also financial market providers.
The example integrated 3 fundamental Saudi private sectors i.e. Agriculture, Industrial as well as Services market. The research recognized a notable unfavorable partnership in between the organization's earnings as well as its own liquidity degree. This partnership was much more popular in agencies along with significant current proportions as well as longer cash money sale patterns. At the business degree, the research located that cash money transformation pattern was a lot more critical as a resolution of liquidity than current ratio and it determines earnings. The measurements variable was likewise discovered to possess considerable impact on earnings at the business degree.
To finish along with, it possessed reasonings for successful liquidity management in several Saudi business.
A research study carried out through Sayaduzzaman (2007) on total performance of management of working capital along with exclusive recommendations to the liquidity of British
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American Tobacco Ltd. that was Bangladeshi provider. The research disclosed the evaluation of various liquidity proportions and confirmed that the provider affected sufficient liquidity in the course of the research time frame.
Mayank (2014) has studied the impact of working capital management on the profitability of Indian automobile company i.e. Mahindra and Mahindra ltd company. The result of multiple regression analysis further surveyed that developed model was a good fit and explains as high as 99% of the variation in the dependent variable. Furthermore, the results also exposed that working capital turnover ratio was the most significant predictor for the respondent company i.e. Mahindra and Mahindra Ltd.
3. Research gap found in the study
Literary works on working capital management has been chronicled in various means. Both the theoretical and empirical researches are belongs to the developed country and the this type of research is rear in developing countries including India. The large firm was the focus of these type of the research. All these researches emphasize the relationship of different type of current assets with the profitability. However, there is no research on the meat processing industry in India. In addition, the researcher used the traditional technique of working capital management.
4. Statement of the Problems
A vital issue in corporate finance, Working capital management of firm has grown crucial consideration in the recent period. The Several theoretical as well as empirical researches have demonstrated that working capital management (WCM) have unswervingly influences liquidity, risk and profitability of a firm and eventually have a say, it influences in value of the firms. It has also been noticed that problematic conditions in both cases of excessive or inadequate working capital positions. The excessive levels of current assets of the firms would result senseless holding costs and cannot generate considerable returns to the firms. The inadequate working capital position on the other side blights profitability and results in production stoppages and inadequacies, sales disruptions and subsequently decline in share value. Although, working capital management is crucial for all firms not withstanding of their size, nature and type. But it is more imperative for small firms as they have more investment in fixed assets rather than current assets. Furthermore, small firms in India face unembellished problems in recuperating their book debts/ receivables. Also, these firms face difficulties in raising long-term finance as a result these firms rely more on short term funds to finance their current assets. Furthermore, it has been arguing that large scale firms have to comply
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210 with very stringent legal requirements pertaining to their financing, and they are expected to have different working capital management strategies as compared to small firms.
5. Significance of Study
There is very few studies has been conducted in developing country and there is no study has been conducted on meat processing industry particularly in India. In these types of study researcher used the traditional techniques to study the relationship between the component of working capital and the profitability. The policy and liquidity aspect of the working capital is ignored in these studies. The present study is trying to fill the gap by focusing on Indian meat Industry and by taking into consideration missing working capital component and liquidity.
6. Aims and Objectives of the Study
Based on systematic review of the research work on the various component of the working capital management and the profitability, followings are objective of this research.
1. To empirically investigate the effects of working capital components on profitability of Indian select meat processing Companies.
2. To determine the effect of liquidity on profitability of Indian select meat processing Companies.
3. To provide valuable suggestions for the management of the different component of the working capital management that effect the profitability of Indian Meat Processing industry.
7. Methodology
Eight Indian meat processing firm has been taken into consideration and the similar empirical research is being conducted that was used by Marc Deloof 2003. The study focused on the component of working capital and its management and practice that effect the profit of the firm. The study cover the period of ten years starts from 1st April 2008. The secondary data is being arranged from the financial statement of these firms and from CMIE database available on the website. The total observation is eighty in number. Details regarding the variables are as follows.
8.1 Variables
Dependent Variable Abv Proxies Measure Evidences
Profitability ROA Earnings before Garcia-Teruel and Martinez-Solano (2007),
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interestand taxes / Total Assets
Karaduman et al. (2011), Sharma andkumar (2011), Akinlo (2012), Priya andNimalathasan (2013)
Profitability ROCE Net Operating Profit / Employed Capital
Raheman and Nasr (2007), Raheman et al. (2010), Kaddumi and Ramadan (2012), Vahid et al. (2012), Panigrahi andSharma (2013), Enqvist et al. (2014), Javid (2014), Khidmat and Rehman (2014)
Independent Variable Abv Proxies measure Evidences
Average Collection
Period ACP 365 × [account
receivables /sales]
Deloof (2003), Garcia-Teruel & Martinez-Solano (2007), Dong and Su (2010),Mathuva (2009), Farzinfar,& Zahra, (2012), Karaduman et al. (2011)
Average Inventories in
Days IND 365 × [inventories
/purchase]
Deloof (2003),Garcia-Teruel and Martinez-Solano (2007),Dong and Su (2010), Karaduman et al.
(2011),Sharma and Kumar,(2011), Akinlo (2012),
Average Payment Period APP 365 × [account payables /purchase]
Dong and Su (2010),Mathuva (2009), Farzinfar,&
Zahra, (2012), Karaduman et al. (2011),Vahid et al.
(2012), Enqvist et al. (2014), Ukaegbu (2014)
Cash Conversion Cycle CCC
CCC=
(ACP+INV_DAYS – APP
Banos-Caballero, Garcia- Teruel,Martınez –Solano (2012),Vahid et al. (2012),Enqvist et al. (2014), Ukaegbu (2014)
Control Variable Abv Proxies measure Evidences
Working Capital Ratio WCR
Current Assets/Current Liabilities
Deloof (2003), Dong and Su (2010), Karaduman et al. (2011), Sharma and Kumar(2011), Akinlo (2012)
Size of firm LN_SALE
S log (sales)
Deloof (2003), Garcia-Teruel & Martinez-Solano (2007), Jose et al., (1996), Nazir&Afza, (2009), Raheman and Nasr, (2007), Shin & Soenen (1998),
Gross Working Capital
Turnover Ratio GWCTR Net sales/ Working Capital
Al-Shubiri (2010), Kaddumi & Ramadan (2012), Palani & Mohideen (2012), Vahid et al. (2012),
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212 Murugesu (2013), Reddy
Note:
ROA= Return on Assets, ROE = Returns on Equity, ACP= Average Collection Period, IND= Average Inventory in Days, APP= Average payable Period, CCC= CashConversion Cycle, WCR= Working Capital Ratio, SIZE= Size of the firms, GWCTR = Gross Working Capital Turnover Ratio.
7.2. Statistical tools
Below the structure is describing the tools and techniques which are used in this study for finding out the objectives.
TOOL AND TECHNIQUES USED
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7.3. Results of the study
The normality test of data is conducted through Shapiro-Wilk W test and PP Plots. The results reflects that p-value is under (0.05) for all the variables except ROA, ROE, ACP and SIZE. This is the evidence that all the variables whose values are less than 0.05 are not normally distributed. In the case of ROA, ROE, ACP and SIZE, the p-value is more than (0.05). Hence these variables are normally distributed.
Though, the data of being not normal is very common occurrence while using financial and economic data.
It is incredible to have perfect normal data. Various techniques have been recommended by the researchers and econometricians by whom data could transmute towards normality. In the literature of econometrics, the variable transformation technique is recommended by Verbeek (2004), Wooldridge (2006), Hair et.
al.(2006), Cameron & Trivedi, (2009). The study used log transformation technique to transform variables into its natural logarithm where it require to remedy this problem.
7.4. Results of Linearity
Q-Q plot has been used in all the eight models to check the linearity assumption which helps to examine the shape of residuals in each model. The residuals are very close to a straight line with some outliers as exception. In particular these residuals do not show much deviation from the straight line. It is evident that these models are following the linearity in their residuals. Consequently, all the best appropriate models of the study fulfil the linearity assumption of regression equations.
•Shipro wilk test
•P-P plot
Normality (Gretl)
•Pearson correlation
•Variance Inflation factor(VIF)
Test of multicolinearity
(Gretl)
• White’s for pooled OLS
• Wald for Fixed effect
• Breusch and Pagan LM test for Random effect
Test of hetroskedasticity
(Gretl)
•Q-Q plot
test of linearity (Gretl)
•Durban-Watson
Test of Autocorrelation
( Gretl •Pooled OLS
•Fixed effect
•Random effect
Test of panel regression (Gretl)
•B/W OLS & FE- F test
•B/W OLS & RE- LM test
•B/W FE & RE - Hausman test
Panel diagnostic test (Gretl)
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214 7.5. Test of Multi-collinearity
Table 1 shows Pearson Correlation Coefficients (Dependent, Independent variables & Control variables)
ROA ROE ACP IND APP CCC WCR SIZE GWCTR
ROA 1.000
ROE 0.697 1.000
ACP 0.116 -0.181 1.000
IND -0.508 -0.216 -0.305 1.000
APP -0.388 0.143 -0.543 0.574 1.000
CCC 0.162 -0.271 0.893 -0.250 -0.800 1.000
WCR 0.074 -0.038 0.281 -0.230 -0.207 0.241 1.000
SIZE 0.172 0.524 -0.393 0.266 0.562 -0.523 -0.378 1.000
GWCTR 0.125 0.061 0.186 -0.315 -0.090 0.080 0.011 -0.097 1.000
Sources: E-views
While going through the above table it can been seen here high collinearity, which is marked as red between the variables.
Therefore deeper investigation of multi collinearity in models has been judged by examining the variance inflation factor (VIF) of explanatory variables. The individual models have been run separately to detect multi collinearity issues. Estimating the regression models by using individual models is appropriate method. This is because of the fact that working capital can be measured through different proxies and using all those proxies in a single model creates multi collinearity issues. The results of variance inflation factor (VIF) are clear that VIF of explanatory variables in all the models are below 10, which indicates that the models do not suffer from any multi collinearity problem. These observations confirm that VIF for working capital components and control variables are within the acceptable limit as none of them exceeds the threshold limit of 10. This implies that the regression coefficients will be fairly estimating the models and the standard errors would be stable and unbiased.
7.6. Result of Heteroskedasticity
Before estimation of the final models the assumption of heteroskedasticity has been tested. In the existence of heteroskedasticity, the inconsistency in estimators are not provided by the normal OLS technique and the use of OLS in such a condition influence t, F and χ2 tests, subsequently provide
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misleading results. While, the presence of heteroskedasticity in the models does not disturb the consistency properties of OLS estimators, but these estimators are no longer BLUE. The white test used for Pooled OLS Models, wald for Fixed effect and LM for Random Effect models.
7.7. Autocorrelation results by Durbin-Watson test
It is very essential for the traditional linear regression to be unrestricted from autocorrelation problems else its occurrence would lead to biased standard errors that eventually make t -values and p- values inappropriate. In order to ensure that the error terms (μt) of population regression function are random or uncorrelated, Durbin-Watson test has been used. The durbin Watson test has been used to check the autocorrelation problems in Pooled OLS, Fixed Effects and Random Effects models.
7.8. Panel Diagnotics Test
Following are the process for diagnose the Panel through different test for different model.
MODEL 1 MODEL 2 MODEL 3 MODEL 4 MODEL 5 MODEL 6 MODEL 7 MODEL 8
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS FE (f test) (FE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
OLS VS RE (LM) (RE)
FE VS RE (Houseman)
(FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
FE VS RE (Houseman) (FE)
Best Model FE
Best Model FE
Best Model FE
Best Model FE
Best Model FE
Best Model FE
Best Model FE
Best Model FE
NOTE;
OLS=Ordinary Least Square, FE= Fixed Effect, RE= Random Effect, LM= Lagrange Multiplier.
7.9. Fixed Effect Model
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216 As per the result of Panel Diagnostics Test, the technique for fixed effects estimations on balanced panel data have been used to regress the profitability for accounting terminology for the firm. There are eight fixed effects regression models have been evolved accordingly which are rooted in the following equations. These models have been evolved with the help of two dependent variables i.e. Return on Assets and Return on Equity and two control variables are also added in all the models. Liquidity have been kept consistent in all the models.
Thus, the estimated equations are as follows:
n
all it i
it X
Y 0
Whereas
Yit :Dependent Variables where i = entity or firm or company and t = time β0 : The intercept of equation
βi : Coefficient of Xit variable
Xit : The different independent variables (ε )it : Stochastic error term of firm i at time t.
t : Time 1….2….3…4…..n
Hence, the estimated equations of the present study are as follows:
Model 1 𝑅𝑂𝐴𝑖𝑡 = 𝛽0+ 𝛽1 ACP 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡
Model 2 𝑅𝑂𝐴𝑖𝑡 = 𝛽0+ 𝛽1 IND 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡.
Model 3 𝑅𝑂𝐴𝑖𝑡 = 𝛽0+ 𝛽1 APP 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡 + 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡+ 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡. Model 4 𝑅𝑂𝐴𝑖𝑡 = 𝛽0+ 𝛽1 CCC 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡.
Model 5 𝑅𝑂𝐸𝑖𝑡 = 𝛽0+ 𝛽1 ACP 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡.
Model 6 𝑅𝑂𝐸𝑖𝑡 = 𝛽0+ 𝛽1 IND 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡. Model 7 𝑅𝑂𝐸𝑖𝑡 = 𝛽0+ 𝛽1 APP 𝑖𝑡+ 𝛽2 𝑊𝐶𝑅 𝑖𝑡 + 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡+ 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡+ 𝜺 𝑖, 𝑡. Model 8 𝑅𝑂𝐸𝑖𝑡 = 𝛽0+ 𝛽1 CCC 𝑖𝑡 + 𝛽2 𝑊𝐶𝑅 𝑖𝑡+ 𝛽3 𝐿𝑁_𝑆𝐴𝐿𝐸𝑆 𝑖𝑡 + 𝛽4 𝐺𝑊𝐶𝑇𝑅 𝑖𝑡 + 𝜺 𝑖, 𝑡
Table 2shows the result of Fixed Effect Models with the dependent variable Return on Assets (ROA) for model 1 to 4
Variables ACP IND APP CCC WCR SIZE GWCTR R-sq DW stats F-Stats
Model 1
Coeff -0.109 0.006 12.9 0.060 0.43 0.91 7.37
t-Stat -1.729 1.67 1.57 0.70
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Prob. 0.127 0.137 0.04 0.045 0.00
Model 2
Coeff. -0.306 0.000 13.2 0.00 0.44 0.94 7.65
t-Stat -1.307 0.047 1.58 -0.117
Prob. 0.0326 0.032 0.05 0.04 0
Model 3
Coeff -0.035 0.003 14.8 -0.001 0.40 0.90 6.67
t-Stat -0.23 0.654 1.83 -0.28
Prob. 0.02 0.034 0.03 0.786 0
Model 4
Coeff -0.12 0.006 12.1 -0.001 0.46 0.91 7.85
t-Stat -1.79 1.899 1.47 -0.255
Prob. 0.01 0.099 0.18 0.805 0
Table shows the result of Fixed Effect Models with the dependent variable Return on Equity (ROE) for model 5 to 8
Variables ACP INVD APP CCC WCR SIZE GWCTR R-sq DW stats F-Stats
Model 5
Coeff -0.04 0.00 14.8 1.877 0.48 1.51 6.35
t-Stat -1.19 1.09 2.95 7.24
Prob. 0.002 0.30 0.02 0.999 0.00
Model 6
Coeff -0.21 0.00 14.5 0.000 0.49 1.58 6.52
t-Stat -1.50 0.20 2.68 0.10
Prob. 0.001 0.005 0.03 0.96 0.00
Model 7
Coeff -0.005 0.002 15.6 -2.55 0.47 1.53 5.68
t-Stat -0.32 0.71 2.95 -0.00
Prob. 0.009 0.49 0.02 0.994 0.00
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218
Model 8
Coeff -0.05 0.004 14.4 -0.000 0.48 1.51 6.87
t-Stat -1.97 1.190 2.89 -0.048
Prob. 0.08 0.272 0.02 0.962 0.00
8. Limitations
The results are based on financial data of sample firms. However, the accuracy and reliability of data depends upon the quality of financial statements maintained by the firms. As there is no legal compulsion for Private Firms in India to disclose their annual reports and there is no other sources available to cross check them thus the data availability problem for public domain remains high that might have affected the robustness of the results. Hence the study suffers from all those limitations that are associated with annual financial statements.
The outliers has not been excluded from the dataset due to limited sample size, therefore their presence would have influenced the overall results. The study has focused on limited components of working capital management that influence the profitability of firms. While as other important variables that significantly influence profitability have been dropped due to non-availability of data. Therefore, limitations and difficulties in accessing data of sample firms are difficult exercise.
The small sample size of a single group of firms has limited the explanatory power of the regression models.
9. Findings, Suggestions and Future Direction
The study makes available the results of descriptive statistics of dependent, independent and control variables. The results of normality, linearity, multi-collinearity, heteroscedasticity and autocorrelation have been tested and reported here before estimating the regression models. Furthermore, panel diagnostics test (PDT) has been examined to select the best models among the pooled OLS, fixed effects and random effects models. The results of the panel diagnostic showed that Fixed Effect Model is more appropriate for all the eight models. The chapter focused its discussion on regression results based on the results of panel diagnostic test. Moreover a detailed discussion has been made from the findings of the regression models.
The impact of components of working capital and liquidity has been measured in the study along with the firm-specific control variables.
The area of research on working capital management of firms has grown gradually, generally in the context of Indian Food Processing Industry and particularly in Indian Meat Processing industry. However, the evidences regarding the impact of working capital management on profitability derived from the studies carried out in developed economies may not fit to the firms in Indian institutional context due to the
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variations between different economic and financial orientations, ownership types, geographical areas, industries and temporal periods. Moreover, the limitations of those studies that currently exist and the dearth of literature on subject matter from the Indian perspective have provided incentive for this kind of research.
Therefore in line of these observations, the current research has been undertaken that has produced a set of results. It has helped to reveal much about the nature of working capital management of Indian Select Meat Processing Companies. Several implications can be drawn from the findings and conclusions of this study. The finding can be applied with quiet reliability in context of working capital management of firms without degradation of their performance.
The present research has been pursued to empirical investigate the impact of working capital management on the profitability of Indian Select Meat Processing Companies. The study generally provided a number of insights which could form the basis of both further research in India, and comparative research in other developing economies. Therefore, it has been suggested that there are different proxy measures of profitability other than ROE and ROAwhich can be recommended to test the results for robustness.Further research needs to be extended by investigating the working capital polices of Meat Processing Industry with that of developed and other developing economies particularly Brazil, Australia as India is the second largest exporter of Meat.
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