Does the size matter? - comparison of the Debt ratio of the large and small sized firms

Cash ratio

The cash ratio is a measurement of a company’s liquidity. It specifically calculates the ratio of a company’s total cash and cash equivalents to its current liabilities. The metric evaluates company’s ability to repay its short-term debt with cash or near-cash resources, such as easily marketable securities. This information is useful to creditors when they decide how much money, if any, they would be willing to loan a company.

The formula for a company’s cash ratio is: Cash Ratio: Cash + Cash Equivalents / Current Liabilities

For more information, see Debt Ratio.

Data

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system

The cash ratio is expressed as the share of AOP 0068 / 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. ## Data processing and results

udaje <<- read.csv2("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","AOP68","AOP442")    #CHANGE
udaje <<- udaje[,selected.cols]  # extracting just columns defined in the previous line
# substitution of txt variables to numeric ones  (change nothing!!!!)
# rather redundant commands, but 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 <<- data.frame(udaje)
udaje$cash.ratio <- udaje$AOP68 / udaje$AOP442        #CHANGE
udaje$total.assets <- udaje$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$total.assets, probs = 0.25)    
quart3 <- quantile(udaje$total.assets, probs = 0.75)

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

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