Does the size matter? - comparison of the Current ratio of the large and small seized firms

Debt ratio (look Investopedia!)

(7-8 lines) The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations or those due within one year. It tells investors and analysts how a company can maximize the current assets on its balance sheet to satisfy its current debt and other payables.

A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default. Similarly, if a company has a very high current ratio compared with its peer group, it indicates that management may not be using its assets efficiently.

Data

The data originate from the web side Mendeley data and are a side-product of the research published by @stanivsic2020empirical. The data involve various primary and secondary data on the Serbian companies from the 2007-2015. They also include the data regarding the accontancy audits. The near explanation of the data presents the above given paper..(5-7 lines)… The current ratio is expressed as the share of AOP 0043 / AOP 0442  accounts.

Hypothesis

We expect that large companies have better current ratio because of providing more secure trades…

Our aim is to provide some graphical analysis explaining this fact. The following assignment will provide more advanced statistics. (as it is Your first step in data processing, I recommend You to use exactly the same kind of the analysis as me, but with other indicatiors - next time, we will extend our space for the other analysis significantly).

Data processing and results

udaje <<- read.csv2("3rd week/udaje.csv")      # import of the .csv data to data.frame  
                                               # udaje become global - see operator <<-
#########   cleaning data - identification, where are the data missing 
library(Amelia)
missmap(udaje) 

udaje <<- na.omit(udaje)
#missmap(udaje)

For continuing the analysis, the database needs even more reconstruction. First of all, we need exclude variables we do not need for achieving our goals. Inspecting the paper of @stanivsic2020empirical we decided to use just “AOP71”, “AOP43”,“AOP442” columns

selected.cols <- c("AOP71","AOP424","AOP441","AOP442", "AOP43","AOP442")    #CHANGE

Warning messages:
1: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
2: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
3: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render

udaje.tmp <<- udaje[,selected.cols]  # extracting just columns defined in the previous line

# substitution of txt variables to numeric ones  (change nothing!!!!)
# rather redundant commands, bu avoiding problems of confusing data types (numeric vs texts)
udaje.tmp <- apply(udaje.tmp, c(1,2),  # I defined function within apply  - conversion of data from text to numeric types
                    function(x) as.numeric(as.character(x)))

udaje.tmp <<- data.frame(udaje.tmp)

Warning messages:
1: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
2: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
3: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
4: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render

udaje.tmp$current.ratio <- udaje.tmp$AOP43 / udaje.tmp$AOP442       #CHANGE
udaje.tmp$total.assets <- udaje.tmp$AOP71                           # CHANGE

# 1st and 3rd quartle of total assets
# identification of the 25th percentil - 25 percent of the firms have  less total.assets then quart1
quart1 <- quantile(udaje.tmp$total.assets, probs = 0.25)    
quart3 <- quantile(udaje.tmp$total.assets, probs = 0.75)

# library

Warning message:
In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render

#library(ggplot2)      # highly popular library for plotting, however, I have not used it
                      # see https://r-graph-gallery.com/ to choose th3e plot and find an appropriate code

 
# grouped boxplot
boxplot(udaje.tmp$current.ratio[udaje.tmp$total.assets >= quart3], udaje.tmp$current.ratio[udaje.tmp$total.assets <= quart3], 
         names = c("Large firms", "Small firms"), # CHANGE
         ylab = "Current.ratio", 
         main = "Figure")

We are not able to see, whether the boxex overlay, because of the oulier data. That is, why I decided to change the vertical axes scale in the graph as follows

Warning messages:
1: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
2: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
3: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
4: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
5: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
6: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
7: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render
8: In overlay(...) :
  reverting 'unnamed.chunk.label' to 'unnamed-chunk' for duration of render

boxplot(udaje.tmp$current.ratio[udaje.tmp$total.assets >= quart3], udaje.tmp$current.ratio[udaje.tmp$total.assets <= quart3], 
         names = c("Large firms", "Small firms"), # CHANGE
         ylab = "Current.ratio", 
         main = "Figure",
        ylim = c(0,10))

Now, we clarly see that boxes (rectanles) overlay - ther is no difference between large and small firms if speaking about current ratio.