Problem #1You are a credit analyst of a growing commercial bank and haverecently received loan applications from two companies ofrelatively similar size.Show below are selected financial data for these two applicants atthe end of the current year:BlueCompanyRedCompanyNet credit salesP675,000P560,000Cost of goods sold504,000480,000Cash51,00020,000Accounts75,00070,000receivable (net)Inventory84,000160,000Current liabilities105,000100,000Assume that the year-end balances for accounts receivable andfor inventory approximate the average balances of these itemsthroughout the year.А.For each of the two companies, compute the following:1.Working capital2.Current ratio3.Acid test ratio4.Accounts receivable turnover5.Average collection period6.Inventory turnover7.Days sales in inventory8.Operating cycleAssuming funds are limited and only one of the two applicants can beaccommodated, to whom would you be willing to give a short -term loan andwhy?В.

Given, Two companies Company Blue and company Red.

You are a credit analyst of a growing commercial bank and have recently received loan applications from two companies of relatively similar size. Show below are selected financial data for these two applicants at the end of the current year: Blue Red Company Company Net credit sales P675,000 P560,000

You are a credit analyst of a growing commercial bank and have recently received loan applications from two companies of relatively similar size. Show below are selected financial data for these two applicants at the end of the current year: Blue Red Company Company Net credit sales P675,000 P560,000

Financial Reporting, Financial Statement Analysis and Valuation

8th Edition

ISBN:9781285190907

Author:James M. Wahlen, Stephen P. Baginski, Mark Bradshaw

Publisher:James M. Wahlen, Stephen P. Baginski, Mark Bradshaw

Chapter6: Accounting Quality

Section: Chapter Questions

Problem 3DIC

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You are a credit analyst of a growing commercial bank and have recently received loan applications from two companies of relatively similar size. Show below are selected financial data for these two applicants at the end of the current year: RED CO BLUE CO. Net credit sales P700,000.00 P650,000.00 Cost of goods sold P500,000.00 P490,000.00 Cash P60,000.00 P20,000.00 Accounts receivable (net) P77,000.00 P60,000.00 Inventory P94,000.00 P180,000.00 Current liabilities P110,p00.00 P105,000.00 Assume that the year-end balances for accounts receivable and for inventory approximate the average balances of these items throughout the year. A. Compute the following on BLUE 1. Inventory turnover 2. Days sales in inventory 3. Operating cycle
You are a credit analyst of a growing commercial bank and have recently received loan applications from two companies of relatively similar size. Show below are selected financial data for these two applicants at the end of the current year: RED CO BLUE CO. P700,000.00 P650,000.00 Net credit sales Cost of goods sold P500,000.00 P490,000.00 Cash P60,000.00 P20,000.00 Accounts receivable (net) P77,000.00 P60,000.00 P94,000.00 P180,000.00 Inventory Current liabilities P110,D00.00 P105,000.00 Assume that the year-end balances for accounts receivable and for inventory approximate the average balances of these items throughout the year. A. Compute the following on RED 1. Working capital 2. Current ratio 3. Acid test ratio 4. Accounts receivable turnover
Consider again the example introduced in Section 4.5 of a credit card company that has a database of information provided by its customers when the customers apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3). Figure 4.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year does this simple linear regression model explain? d. Recall the multiple regression in Figure 4.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of posthigh school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts a, b, and c? What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? e. Add the coefficients of determination for the simple linear regression in parts a, b, and c, and compare the result to the coefficient of determination for the multiple regression model in Figure 4.23. What does this tell you about multicollinearity in the multiple regression model in Figure 4.23? f. Add age, a dummy variable for gender, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 4.23. Code the dummy variable for gender as 1 if the customers gender is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
Consider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
Given the following data; TOTAL SALES OMR 250000 CASH SALES OMR 125000 SALES RETURN OMR 5000 OPENING SUNDRY DEBTORS OMR 20000 CLOSING SUNDRY DEBTORS OMR 10000What will be the Net credit sales? a.OMR 245000 b.OMR 200000 c.OMR 125000 d.OMR 120000
Study this case and answer question asked below: You have the following quotes from Bank A & Bank B Bank A: Bank B SPOT USD/CHF 1.4650/55 USD/CHF 1.4653/60 3-months 5/10 6-months 10/15 Cash/Spot 1/2 SPOT GBP/USD 1.7645/60 GBD/USD 1.7640/50 3-months 25/20 6-months 35/25 If you want to buy 1 million GBP Spot against CHF you have to pay Minimum Amount you have to pay is a. CHF 2.5866 Million b. CHF 2.5881 Million c. CHF 2.5875 Million d. CHF 2.5863 Million
A bank’s customer wants to exercise a $6,750,000 loan commitment. Show the balance sheet after the loan is established using purchased liquidity to fund the loan. Assets Liabilities and Equity Cash $1,550,000 Deposits $7,960,000 Loans 3,470,000 Equity 1,440,000 Securities 4,380,000 Total Assets $9,400,000 Total Liabilities and Equity $9,400,000 Please answer with excel explaination. Don't upload image.
You are the loan department supervisor for a bank. This installment loan is being paid off early, and it is your task to calculate the rebate fraction, the finance charge rebate (in $), and the payoff for the loan (in $). (Round dollars to the nearest cent.) AmountFinanced Number ofPayments MonthlyPayment PaymentsMade RebateFraction FinanceChargeRebate LoanPayoff $1,700 18 $123.89 12
1.You are the loan department supervisor for a bank. This installment loan is being paid off early, and it is your task to calculate the rebate fraction, the finance charge rebate (in $), and the payoff for the loan (in $). (Round dollars to the nearest cent.) AmountFinanced Number ofPayments MonthlyPayment PaymentsMade RebateFraction FinanceChargeRebate LoanPayoff $4,700 36 $168.33 31 ____ $ ____ $ ___ 2.Calculate the table factor, the finance charge, and the monthly payment (in $) for the loan by using the APR table, Table 13-1. (Round your answers to the nearest cent.) AmountFinanced Number ofPayments APR TableFactor FinanceCharge MonthlyPayment $9,400 36 13% $ ___ $ ___ $ ___
Snowflake Manufacturing [SM] has recently come to the bank you work for looking for a $280,000 long-term loan. The following data was submitted with SM's loan application: 2020 $475,200 155,400 175,200 408,600 103,500 13,200 34,200 56,100 64,200 2019 $316,500 120,000 2021 $820,800 Current assets.. Current liabilities. Non-current liabilities. Shareholders' equity.. Operating income... Interest expense... Income tax expense.. Net income... 414,600 60,800 303,700 168,900 7,200 65,400 96,300 106,300 300,000 490,200 208,500 28,500 72,000 108,000 115,000 Operating cash flow... The loan committee has asked you to analyze the data using at least three relevant financial ratios and complete a brief trend analysis. They would also like you to briefly discuss any limitations that might exist when completing this type of analysis and what data dashboards can do to help this. Finally, they have asked that your report also contain a recommendation about whether SM's loan request should be approved.…
Use the information provided below to prepare the following for January and February 2023 as per guide lines on theouc4.1 Debtors Collection Schedule INFORMATIONThe following information was provided by Intel Enterprises:1. The bank balance on 31 December 2022 is expected to be R40 000 (unfavourable).2. Credit sales are expected to be as follows:December 2022 January 2023 February 2023R576 000 R540 000 R648 0003. Credit sales usually make up 40% of the total sales. Cash sales make up the balance. Cash customersreceive a 10% discount.4. Credit sales are normally collected as follows:* 30% in the month in which the transaction takes place, and these customers are entitled to a 5% discount.* 65% in the following monthThe rest is usually written off as bad debts.5. Budgeted purchases of inventory are as follows:December 2022 January 2023 February 2023R1 000 000 R800 000 R920 000 6. Fifty percent…
A borrower has two alternatives for a loan: (1) issue a $630,000, 75-day, 6% note or (2) issue a $630,000, 75-day note that the creditor discounts at 6%. Assume a 360-day year. This information has been collected in the Microsoft Excel Online file. Open the spreadsheet, perform the required analysis, and input your answers in the questions below. X Open spreadsheet a. Compute the amount of the interest expense for each option. Round your answer to the nearest dollar. for each alternative. b. Determine the proceeds received by the borrower in each situation. Round your answers to the nearest dollar. < (1) $630,000, 75-day, 6% interest-bearing note: $ (2) $630,000, 75-day note discounted at 6%: $ c. Alternative is more favorable to the borrower because the borrower