1. What analysis technique(s) would be appropriate in assessing whether there are groups of variables that convey the same information and how important that information is? Conduct such an analysis.

Binary logistic regression can be used to asses whether the company has gone bankrupt or not.

variables used:-

R9-current ratio R21-asset/debts R24-wcfo/debt

Rcode-

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## 
## Call:
## glm(formula = Bankrupt ~ R9 + R21 + R24, family = binomial)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.20530  -0.73295  -0.04158   0.41851   2.31406  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -5.9044     1.4741  -4.005 6.19e-05 ***
## R9            1.1881     0.4764   2.494   0.0126 *  
## R21           1.9844     0.9081   2.185   0.0289 *  
## R24           4.6583     2.1745   2.142   0.0322 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 182.99  on 131  degrees of freedom
## Residual deviance: 105.12  on 128  degrees of freedom
## AIC: 113.12
## 
## Number of Fisher Scoring iterations: 7

AIC: 113.12

PVALUES- R9-0.0126 R21-0.0289 R24-0.0322

  1. Comment on the distinct goals of profiling the characteristics of bankrupt firms versus simply predicting (black box style) whether a firm will go bankrupt and whether both goals, or only one, might be useful. Also comment on the classification methods that would be appropriate in each circumstance.

Bankruptcy signals are indicators or signs that a company may be experiencing financial distress and may be at risk of declaring bankruptcy. These signals can be used by investors, creditors, and other stakeholders to assess the financial health and viability of a company.

Some common bankruptcy signals include: 1)poor current ratio 2)low asset to debt coverage 3)cash flow from operations to debt coverage

It is important to note that none of these signals alone can definitively predict bankruptcy, and companies can experience financial distress for a variety of reasons. However, paying attention to these signals can help stakeholders make more informed decisions about their investments or business relationships with the company in question.

  1. Explore the data to gain a preliminary understanding of which variables might be important in distinguishing bankrupt from non-bankrupt firms. (Hint: As part of this analysis, use side-by-side box-plots, with the bankrupt/not bankrupt variable as the a variable.)
attach(Bankruptdata)
## The following objects are masked from Bankruptdata4:
## 
##     Bankrupt, R1, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R2,
##     R20, R21, R22, R23, R24, R3, R4, R5, R6, R7, R8, R9, YR
bankrupts1<-split(Bankruptdata,Bankrupt)

crbind <- cbind(bankrupts1$No$R9,bankrupts1$Yes$R9)
boxplot(crbind,ylim = c(0.2,5),names =c("NOT Bankrupt","Bankrupt"),xlab = "R9 - Current ratio")

adbind <- cbind(bankrupts1$No$R21,bankrupts1$Yes$R21)
boxplot(adbind,ylim = c(0.5,4.5),names =c("NOT Bankrupt","Bankrupt"),xlab = "R21 - asset/debt")

wdbind <- cbind(bankrupts1$No$R24,bankrupts1$Yes$R24)
boxplot(wdbind,ylim = c(-0.7,1),names =c("NOT Bankrupt","Bankrupt"), xlab = "R24-wcfo/debts")

  1. Using your choice of classifiers, use Orange to produce several models to predict whether or not a firm goes bankrupt, assessing model performance using Cross-validation.

Model: After performing binary logistic regression on the variables r9,r21,r24 we can infer that these three ratios are key indicators to predictiong the future bankruptcy probability of the company. regression results

  1. Based on the above, comment on which variables are important in classification, and discuss their effect.

After observing the p values of these variables it can be noted that the current ratio is the most important variable because of the following reasons:-

  1. Liquidity: The current ratio is a measure of a company’s liquidity, which refers to its ability to convert assets into cash to meet short-term obligations. If a company has a low current ratio, it may not have enough liquidity to meet its obligations and may be at risk of bankruptcy.

  2. Solvency: The current ratio is also a measure of a company’s solvency, which refers to its ability to meet long-term obligations. A company with a low current ratio may not have the financial resources to meet its long-term obligations, which can increase the risk of bankruptcy.

  3. Creditworthiness: The current ratio is often used by lenders to assess a company’s creditworthiness. If a company has a low current ratio, it may have difficulty obtaining credit, which can limit its ability to operate and grow.

  4. Industry Standards: The current ratio can also be compared to industry standards to assess a company’s financial health. If a company’s current ratio is significantly lower than the industry average, it may be an indication of financial distress.