FMCG
Abhinam Chhabra
27 January 2018
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INTRODUCTION
Fast Moving Consumer Goods (FMCG) Industry in India is one of the fastest developing sectors in the Indian economy. At present the FMCG Industry is worth USD 49 billion(2016-17) and it is the 4th largest in the Indian Economy. These products have very fast turnaround rate, i.e. the time from production to the revenue from the sale of the product is very less. It is estimated to grow at 20-25% per annum, Retail market to reach US$ 1.1 trillion in 2020.
It include segments like cosmetics, toiletries, glassware, batteries, bulbs, pharmaceuticals, packaged food products, white goods, house care products, plastic goods, consumer non-durables, etc. The FMCG market is highly concentrated in the urban areas as the rise in the income of the middle-income group is one of the major factors for the growth of the Indian FMCG market.
OVERVIEW OF STUDY
The FMCG sector mainly focus on high volume and low margin so the amount of liquidity of cash is very important factor while assesing the Market Capitalization of a FMCG company. Further the time in which a company can convert their inventory into cash is also important. So I will mainly focus on how different ratio help in drivation of market capitalization of FMCG company. I have used the past 15 years data of different FMCG company to see the trend how the Market Capitalization of differnt companies is changing with different liquidity and turnover ratios. The Market CAP is direcly effected by the change in Current Market Price(CMP) of a stock and stock are effected by different financial reports and Ratios.
Research Objective
Liquidity and Turnover Ratios: How they affect the market capitalization?
Research Hypothesis
H0 : There is no impact of Liquidity or Turnover Ratios on Market Capitalization
H1 : Liquidity and Profitability Ratios affect Market Capitalization
DATA
The data collected for the research is from the bloomberg terminal for 34 listed companies of last 15 years. Some companies have become debt free over the period of time so their Debt/Equity Ratio and Debt/Capital Ratio is zero whereas some companies maintain zero inventory due to adpotion of Just-in-Time strategy so for few years has zero inventory turnover ratio.
Liquidity Ratios
Liquidity ratios measure a company’s ability to pay debt obligations and its margin of safety through the calculation of metrics including the current ratio, quick ratio and operating cash flow ratio.
Type of Liquidity Ratios:
1.Current Ratio
2.Cash Ratio
3.Quick Ratio
4.Total Debt to Equity
5.Total Debt to Capital
Turnover Ratios
A turnover ratio represents the amount of assets or liabilities that a company replaces in relation to its sales. The concept is useful for determining the efficiency with which a business utilizes its assets. In most cases, a high asset turnover ratio is considered good, since it implies that receivables are collected quickly, fixed assets are heavily utilized, and little excess inventory is kept on hand. This implies a minimal need for invested funds, and therefore a high return on investment.
Type of Turnover Ratios:
1.Accounts Recivable Turnover
2.Inventory Turnover
MODEL
In order to test Hypothesis, we proposed the following model:
\[ MCAP.Million.INR. = Revenue.Million.INR. + Capital.Million.INR.+ Total.Debt.Million.INR. +\] \[Total.Inventory.Million. + Cash.Ratio + Quick.Ratio + Current.Ratio + Total.Debt.Equity + Total.Debt.Capital +\] \[Accounts.Receivable.Turnover + Inventory.Turnover\]
fmcg.cf <- read.csv(paste("fmcg.csv", sep = ""))
View(fmcg.cf)M1 <- lm(MCAP.Million.INR. ~ Revenue.Million.INR. + Capital.Million.INR.+ Total.Debt.Million.INR. + Total.Inventory.Million. + Cash.Ratio + Quick.Ratio + Current.Ratio + Total.Debt.Equity + Total.Debt.Capital + Accounts.Receivable.Turnover + Inventory.Turnover, data=fmcg.cf)
summary(M1)##
## Call:
## lm(formula = MCAP.Million.INR. ~ Revenue.Million.INR. + Capital.Million.INR. +
## Total.Debt.Million.INR. + Total.Inventory.Million. + Cash.Ratio +
## Quick.Ratio + Current.Ratio + Total.Debt.Equity + Total.Debt.Capital +
## Accounts.Receivable.Turnover + Inventory.Turnover, data = fmcg.cf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -675596 -52787 -1488 40646 1437914
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 87676.927 15008.650 5.842 9.40e-09 ***
## Revenue.Million.INR. 9.458 53.915 0.175 0.8608
## Capital.Million.INR. 276.398 229.001 1.207 0.2280
## Total.Debt.Million.INR. 31.444 17.723 1.774 0.0766 .
## Total.Inventory.Million. 18.939 1.196 15.837 < 2e-16 ***
## Cash.Ratio -38935.077 25560.188 -1.523 0.1283
## Quick.Ratio 48029.088 30312.422 1.584 0.1137
## Current.Ratio -11263.368 7325.032 -1.538 0.1248
## Total.Debt.Equity 145.915 111.410 1.310 0.1909
## Total.Debt.Capital -3107.415 514.076 -6.045 2.96e-09 ***
## Accounts.Receivable.Turnover -13.968 17.290 -0.808 0.4196
## Inventory.Turnover 354.526 691.904 0.512 0.6086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 149400 on 492 degrees of freedom
## Multiple R-squared: 0.4039, Adjusted R-squared: 0.3905
## F-statistic: 30.3 on 11 and 492 DF, p-value: < 2.2e-16RESULTS
I found that the Inventory and Debt/Capital have a huge signifucance as the FMCG is driven hugely by high volumes and low margins. So, the amount of inventory a company keep and the level of debt and capital company has plays a huge role in Market CAP of a company.
CONCLUSION
This paper was motivated by the need for research that could improve our understanding of how the Market Cap of an FMCG stock is effected by different tupe of financial results and ratios. The unique contribution of this paper is that I investigated the differnt liquidity and turn over ratios impact the valuation of a comapny in FMCG sector. I found that Inventory and Capital play a huge role for a company in FMCG sector due to its low margins and high volumes.
APPENDIX
summary(fmcg.cf)## year Company Revenue.Million.INR.
## FY 2003: 34 Avanti feeds : 15 Min. : -94.00
## FY 2004: 34 Bombay Burmah : 15 1st Qu.: 4.00
## FY 2005: 34 CCL Products : 15 Median : 12.00
## FY 2006: 34 Chaman Lal Setia: 15 Mean : 19.83
## FY 2010: 34 Colgate : 15 3rd Qu.: 20.00
## FY 2011: 34 Dabur : 15 Max. :2767.00
## (Other):300 (Other) :414
## Total.Debt.Million.INR. Capital.Million.INR. Total.Inventory.Million.
## Min. : -99.00 Min. :-68.00 Min. : 0
## 1st Qu.: -11.00 1st Qu.: 2.00 1st Qu.: 484
## Median : 0.00 Median : 10.00 Median : 1236
## Mean : 55.88 Mean : 16.56 Mean : 3285
## 3rd Qu.: 26.00 3rd Qu.: 24.00 3rd Qu.: 3004
## Max. :5317.00 Max. :262.00 Max. :41614
##
## Cash.Ratio Current.Ratio Quick.Ratio Total.Debt.Equity
## Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.00
## 1st Qu.: 0.0600 1st Qu.: 1.040 1st Qu.: 0.3200 1st Qu.: 12.75
## Median : 0.1900 Median : 1.380 Median : 0.5600 Median : 50.00
## Mean : 0.4318 Mean : 1.861 Mean : 0.7812 Mean : 82.97
## 3rd Qu.: 0.4800 3rd Qu.: 1.950 3rd Qu.: 0.9000 3rd Qu.: 114.00
## Max. :15.2100 Max. :20.130 Max. :15.2100 Max. :1104.00
##
## Total.Debt.Capital Accounts.Receivable.Turnover Inventory.Turnover
## Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:11.00 1st Qu.: 7.00 1st Qu.: 0.000
## Median :33.00 Median : 13.00 Median : 1.000
## Mean :33.21 Mean : 69.38 Mean : 4.032
## 3rd Qu.:53.00 3rd Qu.: 27.25 3rd Qu.: 4.250
## Max. :92.00 Max. :8060.00 Max. :107.000
##
## MCAP.Million.INR.
## Min. : 0
## 1st Qu.: 1523
## Median : 6806
## Mean : 65532
## 3rd Qu.: 37164
## Max. :1973346
## library(psych)
describe(fmcg.cf)## vars n mean sd median trimmed
## year* 1 504 7.99 4.33 8.00 7.99
## Company* 2 504 17.55 9.86 18.00 17.57
## Revenue.Million.INR. 3 504 19.83 124.96 12.00 12.40
## Total.Debt.Million.INR. 4 504 55.88 387.77 0.00 7.61
## Capital.Million.INR. 5 504 16.56 30.91 10.00 12.74
## Total.Inventory.Million. 6 504 3285.28 5688.09 1235.50 1889.67
## Cash.Ratio 7 504 0.43 1.19 0.19 0.27
## Current.Ratio 8 504 1.86 1.95 1.38 1.49
## Quick.Ratio 9 504 0.78 1.22 0.56 0.62
## Total.Debt.Equity 10 504 82.97 109.29 50.00 61.94
## Total.Debt.Capital 11 504 33.21 24.56 33.00 32.11
## Accounts.Receivable.Turnover 12 504 69.38 414.39 13.00 18.13
## Inventory.Turnover 13 504 4.03 10.38 1.00 2.01
## MCAP.Million.INR. 14 504 65532.10 191396.38 6806.50 24156.94
## mad min max range skew
## year* 5.93 1 15.00 14.00 -0.01
## Company* 13.34 1 34.00 33.00 -0.01
## Revenue.Million.INR. 11.86 -94 2767.00 2861.00 21.09
## Total.Debt.Million.INR. 26.69 -99 5317.00 5416.00 10.73
## Capital.Million.INR. 16.31 -68 262.00 330.00 2.56
## Total.Inventory.Million. 1365.47 0 41614.00 41614.00 3.44
## Cash.Ratio 0.24 0 15.21 15.21 10.62
## Current.Ratio 0.59 0 20.13 20.13 5.13
## Quick.Ratio 0.40 0 15.21 15.21 9.22
## Total.Debt.Equity 65.23 0 1104.00 1104.00 3.30
## Total.Debt.Capital 30.39 0 92.00 92.00 0.19
## Accounts.Receivable.Turnover 11.86 0 8060.00 8060.00 15.25
## Inventory.Turnover 1.48 0 107.00 107.00 5.99
## MCAP.Million.INR. 9626.52 0 1973346.00 1973346.00 6.67
## kurtosis se
## year* -1.22 0.19
## Company* -1.22 0.44
## Revenue.Million.INR. 460.45 5.57
## Total.Debt.Million.INR. 128.36 17.27
## Capital.Million.INR. 13.02 1.38
## Total.Inventory.Million. 13.68 253.37
## Cash.Ratio 125.19 0.05
## Current.Ratio 33.34 0.09
## Quick.Ratio 102.39 0.05
## Total.Debt.Equity 19.01 4.87
## Total.Debt.Capital -1.09 1.09
## Accounts.Receivable.Turnover 276.02 18.46
## Inventory.Turnover 42.89 0.46
## MCAP.Million.INR. 55.22 8525.47attach(fmcg.cf)aggregate(cbind(Quick.Ratio, Total.Debt.Equity, Accounts.Receivable.Turnover,MCAP.Million.INR.) ~ Company,
data = fmcg.cf, mean)## Company Quick.Ratio Total.Debt.Equity
## 1 Avanti feeds 0.7280000 43.4666667
## 2 Bombay Burmah 0.8233333 210.2000000
## 3 CCL Products 0.6700000 61.3333333
## 4 Chaman Lal Setia 1.2493333 100.4666667
## 5 Colgate 0.4126667 3.5333333
## 6 Dabur 0.6386667 18.0000000
## 7 Emami 1.6353333 27.0666667
## 8 Eveready 0.1506667 66.4000000
## 9 Gillette 1.0053846 0.0000000
## 10 GM Breweries 0.2240000 56.8666667
## 11 Godfrey 0.2720000 18.0000000
## 12 Godrej 0.4193333 25.1333333
## 13 Goodricke Group 0.4953846 24.4615385
## 14 GSK 1.4380000 0.0000000
## 15 Gujarat Ambuja Exports Ltd 0.3706667 58.6000000
## 16 Heritage food 0.3493333 99.1333333
## 17 HUL 0.6461538 20.0769231
## 18 Jayshree Tea 0.3166667 99.8000000
## 19 Kohinoor Foods 0.9566667 343.1333333
## 20 Kwality Ltd 0.8626667 224.3333333
## 21 Marico 0.5240000 58.4000000
## 22 P&G 0.8913333 0.0000000
## 23 Radico Khaitan 1.0486667 146.8666667
## 24 Rasoi 3.5260000 38.6666667
## 25 Ruchi Soya 0.6513333 224.9333333
## 26 Sanwari Consumers Ltd 1.0680000 158.2666667
## 27 Tata Coffee Ltd. 0.4426667 34.6666667
## 28 Tata Global Beverages 0.7320000 61.1333333
## 29 TTK Healthcare 0.9726667 33.0000000
## 30 United Breweries Ltd 0.8313333 162.8666667
## 31 United Spirits Ltd 0.4433333 132.6666667
## 32 Vadilal Ltd 0.3740000 151.0000000
## 33 Venky's 0.9680000 90.3333333
## 34 VST INDS 0.3973333 0.9333333
## Accounts.Receivable.Turnover MCAP.Million.INR.
## 1 32.733333 4948.6667
## 2 5.866667 11166.6000
## 3 7.400000 8640.0000
## 4 9.733333 787.4000
## 5 77.666667 117327.4000
## 6 21.200000 189102.3333
## 7 18.333333 73445.7333
## 8 22.266667 6266.6667
## 9 9.692308 65434.1538
## 10 1343.066667 1797.4000
## 11 50.333333 22749.8000
## 12 50.400000 165520.6667
## 13 8.846154 2519.5385
## 14 31.200000 110335.4000
## 15 19.666667 3827.3333
## 16 124.333333 4616.1333
## 17 28.538462 941271.9231
## 18 9.533333 1931.1333
## 19 8.133333 1530.8667
## 20 3.666667 9745.0667
## 21 24.800000 109963.3333
## 22 27.133333 84190.2000
## 23 4.866667 12418.2000
## 24 133.600000 817.1333
## 25 8.400000 13791.6000
## 26 16.666667 6511.5333
## 27 9.466667 10975.4667
## 28 128.066667 57093.0000
## 29 9.733333 2573.6667
## 30 5.133333 104063.1333
## 31 6.133333 172247.4000
## 32 12.133333 1317.7333
## 33 11.600000 3359.6667
## 34 57.200000 14155.8000Var1 <- aggregate(Capital.Million.INR., by=list(View=Company), mean)
colnames(Var1) <- c("Company", "Average Capital")
Var1## Company Average Capital
## 1 Avanti feeds 17.133333
## 2 Bombay Burmah 7.600000
## 3 CCL Products 22.400000
## 4 Chaman Lal Setia 13.200000
## 5 Colgate 13.933333
## 6 Dabur 15.933333
## 7 Emami 17.666667
## 8 Eveready -4.800000
## 9 Gillette 4.692308
## 10 GM Breweries 12.533333
## 11 Godfrey 12.933333
## 12 Godrej 39.466667
## 13 Goodricke Group 7.076923
## 14 GSK 11.666667
## 15 Gujarat Ambuja Exports Ltd 18.000000
## 16 Heritage food 20.933333
## 17 HUL 4.615385
## 18 Jayshree Tea 10.400000
## 19 Kohinoor Foods 11.000000
## 20 Kwality Ltd 57.466667
## 21 Marico 22.133333
## 22 P&G 10.600000
## 23 Radico Khaitan 18.866667
## 24 Rasoi 22.466667
## 25 Ruchi Soya 24.800000
## 26 Sanwari Consumers Ltd 35.266667
## 27 Tata Coffee Ltd. 12.400000
## 28 Tata Global Beverages 9.733333
## 29 TTK Healthcare 6.066667
## 30 United Breweries Ltd 21.200000
## 31 United Spirits Ltd 23.866667
## 32 Vadilal Ltd 9.800000
## 33 Venky's 16.133333
## 34 VST INDS 11.533333Var2 <- aggregate(Total.Debt.Capital, by=list(View=Company), mean)
colnames(Var2) <- c("Company", "Average Debt/Capital")
Var2## Company Average Debt/Capital
## 1 Avanti feeds 28.4666667
## 2 Bombay Burmah 62.5333333
## 3 CCL Products 34.9333333
## 4 Chaman Lal Setia 47.0000000
## 5 Colgate 3.2000000
## 6 Dabur 14.5333333
## 7 Emami 17.6000000
## 8 Eveready 38.3333333
## 9 Gillette 0.0000000
## 10 GM Breweries 29.4000000
## 11 Godfrey 14.8000000
## 12 Godrej 16.1333333
## 13 Goodricke Group 17.5384615
## 14 GSK 0.0000000
## 15 Gujarat Ambuja Exports Ltd 35.1333333
## 16 Heritage food 43.7333333
## 17 HUL 11.4615385
## 18 Jayshree Tea 49.4666667
## 19 Kohinoor Foods 76.5333333
## 20 Kwality Ltd 62.8666667
## 21 Marico 31.4000000
## 22 P&G 0.0000000
## 23 Radico Khaitan 56.2000000
## 24 Rasoi 23.0000000
## 25 Ruchi Soya 65.6666667
## 26 Sanwari Consumers Ltd 58.0666667
## 27 Tata Coffee Ltd. 24.1333333
## 28 Tata Global Beverages 32.4000000
## 29 TTK Healthcare 23.8000000
## 30 United Breweries Ltd 43.6666667
## 31 United Spirits Ltd 53.7333333
## 32 Vadilal Ltd 58.4666667
## 33 Venky's 44.7333333
## 34 VST INDS 0.8666667Var3 <- aggregate(Capital.Million.INR., by=list(View=year), mean)
colnames(Var3) <- c("Year", "Average Capital")
Var3## Year Average Capital
## 1 FY 2003 10.647059
## 2 FY 2004 18.264706
## 3 FY 2005 10.264706
## 4 FY 2006 26.647059
## 5 FY 2007 24.939394
## 6 FY 2008 19.968750
## 7 FY 2009 20.121212
## 8 FY 2010 26.500000
## 9 FY 2011 25.147059
## 10 FY 2012 15.500000
## 11 FY 2013 10.882353
## 12 FY 2014 12.852941
## 13 FY 2015 8.264706
## 14 FY 2016 11.757576
## 15 FY 2017 6.818182Var4 <- xtabs(~ Company + year, data = fmcg.cf)
Var4## year
## Company FY 2003 FY 2004 FY 2005 FY 2006 FY 2007
## Avanti feeds 1 1 1 1 1
## Bombay Burmah 1 1 1 1 1
## CCL Products 1 1 1 1 1
## Chaman Lal Setia 1 1 1 1 1
## Colgate 1 1 1 1 1
## Dabur 1 1 1 1 1
## Emami 1 1 1 1 1
## Eveready 1 1 1 1 1
## Gillette 1 1 1 1 0
## GM Breweries 1 1 1 1 1
## Godfrey 1 1 1 1 1
## Godrej 1 1 1 1 1
## Goodricke Group 1 1 1 1 1
## GSK 1 1 1 1 1
## Gujarat Ambuja Exports Ltd 1 1 1 1 1
## Heritage food 1 1 1 1 1
## HUL 1 1 1 1 1
## Jayshree Tea 1 1 1 1 1
## Kohinoor Foods 1 1 1 1 1
## Kwality Ltd 1 1 1 1 1
## Marico 1 1 1 1 1
## P&G 1 1 1 1 1
## Radico Khaitan 1 1 1 1 1
## Rasoi 1 1 1 1 1
## Ruchi Soya 1 1 1 1 1
## Sanwari Consumers Ltd 1 1 1 1 1
## Tata Coffee Ltd. 1 1 1 1 1
## Tata Global Beverages 1 1 1 1 1
## TTK Healthcare 1 1 1 1 1
## United Breweries Ltd 1 1 1 1 1
## United Spirits Ltd 1 1 1 1 1
## Vadilal Ltd 1 1 1 1 1
## Venky's 1 1 1 1 1
## VST INDS 1 1 1 1 1
## year
## Company FY 2008 FY 2009 FY 2010 FY 2011 FY 2012
## Avanti feeds 1 1 1 1 1
## Bombay Burmah 1 1 1 1 1
## CCL Products 1 1 1 1 1
## Chaman Lal Setia 1 1 1 1 1
## Colgate 1 1 1 1 1
## Dabur 1 1 1 1 1
## Emami 1 1 1 1 1
## Eveready 1 1 1 1 1
## Gillette 0 1 1 1 1
## GM Breweries 1 1 1 1 1
## Godfrey 1 1 1 1 1
## Godrej 1 1 1 1 1
## Goodricke Group 1 1 1 1 1
## GSK 1 1 1 1 1
## Gujarat Ambuja Exports Ltd 1 1 1 1 1
## Heritage food 1 1 1 1 1
## HUL 0 0 1 1 1
## Jayshree Tea 1 1 1 1 1
## Kohinoor Foods 1 1 1 1 1
## Kwality Ltd 1 1 1 1 1
## Marico 1 1 1 1 1
## P&G 1 1 1 1 1
## Radico Khaitan 1 1 1 1 1
## Rasoi 1 1 1 1 1
## Ruchi Soya 1 1 1 1 1
## Sanwari Consumers Ltd 1 1 1 1 1
## Tata Coffee Ltd. 1 1 1 1 1
## Tata Global Beverages 1 1 1 1 1
## TTK Healthcare 1 1 1 1 1
## United Breweries Ltd 1 1 1 1 1
## United Spirits Ltd 1 1 1 1 1
## Vadilal Ltd 1 1 1 1 1
## Venky's 1 1 1 1 1
## VST INDS 1 1 1 1 1
## year
## Company FY 2013 FY 2014 FY 2015 FY 2016 FY 2017
## Avanti feeds 1 1 1 1 1
## Bombay Burmah 1 1 1 1 1
## CCL Products 1 1 1 1 1
## Chaman Lal Setia 1 1 1 1 1
## Colgate 1 1 1 1 1
## Dabur 1 1 1 1 1
## Emami 1 1 1 1 1
## Eveready 1 1 1 1 1
## Gillette 1 1 1 1 1
## GM Breweries 1 1 1 1 1
## Godfrey 1 1 1 1 1
## Godrej 1 1 1 1 1
## Goodricke Group 1 1 1 0 0
## GSK 1 1 1 1 1
## Gujarat Ambuja Exports Ltd 1 1 1 1 1
## Heritage food 1 1 1 1 1
## HUL 1 1 1 1 1
## Jayshree Tea 1 1 1 1 1
## Kohinoor Foods 1 1 1 1 1
## Kwality Ltd 1 1 1 1 1
## Marico 1 1 1 1 1
## P&G 1 1 1 1 1
## Radico Khaitan 1 1 1 1 1
## Rasoi 1 1 1 1 1
## Ruchi Soya 1 1 1 1 1
## Sanwari Consumers Ltd 1 1 1 1 1
## Tata Coffee Ltd. 1 1 1 1 1
## Tata Global Beverages 1 1 1 1 1
## TTK Healthcare 1 1 1 1 1
## United Breweries Ltd 1 1 1 1 1
## United Spirits Ltd 1 1 1 1 1
## Vadilal Ltd 1 1 1 1 1
## Venky's 1 1 1 1 1
## VST INDS 1 1 1 1 1boxplot(fmcg.cf$MCAP.Million.INR., horizontal=TRUE,
xlab="Market captilization", las=1)
library(lattice)
histogram(~MCAP.Million.INR., data = fmcg.cf,
main = "Market Cap", xlab="Rs in Million", col='red' )
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(Revenue.Million.INR. ~ Company ,data=fmcg.cf, main="Revenue", xlab="Company", ylab="Revenue INR Millions", horizontal=TRUE)
## [1] "192" "133" "136" "166" "239" "253" "255" "254" "58" "50" "296"
## [12] "324" "315" "331" "356" "371" "113" "404" "405" "419" "443" "434"
## [23] "445" "463" "477" "500" "72"scatterplot.matrix(~Revenue.Million.INR.+Total.Debt.Million.INR.+Current.Ratio+Total.Debt.Equity+Accounts.Receivable.Turnover+ MCAP.Million.INR., data=fmcg.cf,
main=" Mcap versus other variables")## Warning: 'scatterplot.matrix' is deprecated.
## Use 'scatterplotMatrix' instead.
## See help("Deprecated") and help("car-deprecated").
scatterplot.matrix(~ Capital.Million.INR.+ Total.Inventory.Million.+ Cash.Ratio+Quick.Ratio+Total.Debt.Capital+Inventory.Turnover+ MCAP.Million.INR., data=fmcg.cf,
main=" Mcap versus other variables")## Warning: 'scatterplot.matrix' is deprecated.
## Use 'scatterplotMatrix' instead.
## See help("Deprecated") and help("car-deprecated").
interaction.plot(Company, MCAP.Million.INR., Capital.Million.INR., type="p",
col=c("red","blue"), pch=c(20, 18),
main = "Market Capitalization")
cor(fmcg.cf$MCAP.Million.INR.,fmcg.cf$Cash.Ratio)## [1] 0.02529334cor(fmcg.cf$MCAP.Million.INR.,fmcg.cf$Total.Debt.Million.INR.)## [1] 0.0648256cor(fmcg.cf$MCAP.Million.INR.,fmcg.cf$Inventory.Turnover)## [1] 0.012469cor(fmcg.cf$MCAP.Million.INR.,fmcg.cf$Accounts.Receivable.Turnover)## [1] -0.03102749cor(fmcg.cf$MCAP.Million.INR.,fmcg.cf$Revenue.Million.INR.)## [1] -0.02813166cor.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Cash.Ratio)##
## Pearson's product-moment correlation
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Cash.Ratio
## t = 0.56689, df = 502, p-value = 0.571
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06218565 0.11238667
## sample estimates:
## cor
## 0.02529334cor.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Inventory.Turnover)##
## Pearson's product-moment correlation
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Inventory.Turnover
## t = 0.27939, df = 502, p-value = 0.7801
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07495424 0.09970203
## sample estimates:
## cor
## 0.012469cor.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Revenue.Million.INR.)##
## Pearson's product-moment correlation
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Revenue.Million.INR.
## t = -0.63055, df = 502, p-value = 0.5286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1151902 0.0593558
## sample estimates:
## cor
## -0.02813166t.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Cash.Ratio)##
## Welch Two Sample t-test
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Cash.Ratio
## t = 7.6866, df = 503, p-value = 7.99e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 48781.75 82281.59
## sample estimates:
## mean of x mean of y
## 6.553210e+04 4.318254e-01t.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Inventory.Turnover)##
## Welch Two Sample t-test
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Inventory.Turnover
## t = 7.6862, df = 503, p-value = 8.013e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 48778.15 82277.99
## sample estimates:
## mean of x mean of y
## 65532.103175 4.031746t.test(fmcg.cf$MCAP.Million.INR., fmcg.cf$Revenue.Million.INR.)##
## Welch Two Sample t-test
##
## data: fmcg.cf$MCAP.Million.INR. and fmcg.cf$Revenue.Million.INR.
## t = 7.6843, df = 503, p-value = 8.118e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 48762.35 82262.20
## sample estimates:
## mean of x mean of y
## 65532.10317 19.82738library(coefplot)## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphacoefplot(M1, intercept=FALSE)