Credit Analysis, Bond Ratings, Distress Forecast and Financial Information - PowerPoint PPT Presentation

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Credit Analysis, Bond Ratings, Distress Forecast and Financial Information

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  1. Credit Analysis, Bond Ratings, Distress Forecast and Financial Information

  2. Credit Analysis • The process of evaluating an applicant's loan request or a corporation's debt issue in order to determine the likelihood that the borrower will live up to his/her obligations.

  3. Credit Analysis • Evaluate a borrower’s ability and willingness to repay • Questions to address • What risks are inherent in the operations of the business? • What have managers done or failed to do in mitigating those risks? • How can a lender structure and control its own risks in supplying funds?

  4. Existing Loan Decisions Loan Approvals Loan Monitoring Loan Terminations

  5. Loan Application Customer relation Economic condition Financial performance Strategic factor Management quality Risk Approval Others Insurance Amount Interest rate Collateral Covenant Yes Monitoring Repayment timing Covenant Market value of collateral Current Especially Mentioned Loss Substandard Doubtful

  6. The categories: classification of existing loans into A.Current:normal acceptable banking risk. B.Especially mentioned: evidence of weakness in the borrowers’ financial condition or an unrealistic repayment schedule. C. Substandard: severely adverse trends or developments of a financial, managerial, economic, or political nature that require prompt corrective actions. D. Doubtful:full repayment of the loan appears to be questionable. Some eventual loss seems likely. Interest is not accrued. E. Loss:loan is regarded as uncollectible.

  7. Five C’s of Good Credit • Character • Capital • Capacity • Conditions • Collateral

  8. Five C’s of Bad Credit • Complacency • Carelessness • Communication • Contingencies • Competition

  9. Credit Scoring

  10. What is credit scoring? • A statistical means of providing a quantifiable risk factor for a given customer or applicant. • Credit scoring is a process whereby information provided is converted into numbers that are added together to arrive at a score. (“Scorecard”) • The objective is to forecast future performance from past behaviour.

  11. A Simple Linear Model to Replicate the Judgment Used in Classifying the Loan Risk (Dietrich and Kaplan ,1982) Yi = -3.90 + 6.42 DEi - 1.12 FCCi + 0.664 Sdi where • DEi = Total debt/total assets • FCCi = funds from operation/(interest + minimum rental commitment + average debt maturing within three years) • SDi = number of consecutive years of sales decline The higher the Yi score, the higher the estimated risk of the loan.

  12. The hindsight for a simple scoring method • The loan officers may consider more than three variables. • The loan officers may consider non-linear or non-additive functional form. • The loan officers may consider non financial information.

  13. Loss functions for the misclassifications • Uniform loss function. • Loss functions supplied by the bank.

  14. The loss function for model prediction errors • C1: (Resulted from type I error) the cost of predicting a loan applicant will not repay when it subsequently repay. It will be contribution margin on the loan that was foregone, assuming that applicants predicted not to repay are refused loans. • C2: (Resulted from type II error) the cost of predicting that a loan applicant will repay when it subsequently does not repay. It will be the loss associated with the interest and principal the bank can not receive when due. • Note: Using estimates of C2 based on loan loss recovery statistics estimated in the 1971-1975 period, researchers have reported that a C2 error was 35 times more costly than was a C1 error.

  15. Scoring methods and sample sizes • There is a trade off between having a large enough set of observations to efficiently estimate a scoring method and having a set of firms that are homogeneous with respect to attributes relevant to their loan decision. • Solutions: 1.Build a separated scoring system for each industry. But this always resulted in a small sample, especially very few observations for problem loan categories. 2. To control for the hypothesis source of heterogeneity across observations, such as the use of industry relative ratios as a means controlling for differences across industries in their average financial ratios.

  16. Credit Analysis and Financial Ratios

  17. Credit Analysis Short Term Long Term Days Sales in AR Days Sales in Inventory Days Purchases in AP Cash Conversion Ratio Current Ratio Quick Ratio Op. Cash Flow to Current Liabilities LT Debt/Equity Total Liab/Equity PPE/Total Assets Interest Coverage Op. Cash Flow/Tot Liab Op. Cash Flow/PPE Exp Common Size (To total assets) Cash AR Inventory Total Current Assets PP&E Intangibles Current Liabilities Total Liabilities Equity Relationships % Chg in AR to % Chg in Sales %Chg in Invt to % Chg in Sale

  18. The importance of financial ratios used in credit decision:---Survey conducted on loan officers • Debt/Equity • Current ratio • Cash flow/Current maturities of long-term debt • Fixed charge coverage • Net profit margin after taxes • Times interest earned • Net profit margin before taxes • Degree of Financial leverage • Inventory turnover in days • Accounts receivable turnover in days

  19. The importance according to the frequency adopted in loan agreements • Debt/Equity • Current ratio • Dividend payout ratio • Cash flow/Current maturities of long-term debt • Fixed charge coverage • Times interest earned • Degree of Financial leverage • Equity/Asset • Cash flow/Total debt • Quick ratio

  20. What are bond ratings? • Bond ratings are opinions of relative creditworthiness, derived through fundamental credit analysis and expressed through a symbol system. • Creditworthiness: tendency to pay obligations on time. • Default probability and severity of loss given default • Not statement of default timing • Not Buy and sell recommendations

  21. The role of ratings: • Improve the information flow between borrowers and lenders. • Information asymmetry • Improve transparency • Minimize monitoring and principal/agent costs • Owners vs managers of firms • Fund sponsors vs fund manager • Public goods

  22. Bond ratings and debt covenants

  23. Bond ratings—Standard and Poor’s • AAA highest grade—ultimate degree of protection of principle and interest • AA high grade—differ from AAA in small degrees • A upper medium grade • Have considerable investment strength but are not entirely free from adverse effects of changes in economic and trade conditions. Interest and principal are regarded as safe. They to some extent reflect changes in economic conditions • BBB or medium –grade category is borderline between definitely sound obligations and those where the speculative element begins to dominate. These have adequate asset coverage and normally are protected by satisfactory earnings. They are susceptible to fluctuations due to economic conditions. This is the lowest rating that qualifies for commercial bank investment. • There is a lower range of ratings ranging from BB which are lower medium grade all the way to the D category representing bonds in default.

  24. ITEMS AFFECTING THE RATINGS OF CORPORATE BONDS • Items considered: • Asset protection—measures the degree to which a company’s debt is covered by the value of its assets. • Tangible assets/LTD • AAA—5 to 1 • AA—4 to 1 • A—3 to 3.5 to 1 • BBB—2.5to 1 • LTD/(LTD + Equity) • AAA—less than 25% • AA— less than 30% • A— less than 35% • BBB— less than 40%

  25. ITEMS AFFECTING THE RATINGS OF CORPORATE BONDS • Fixed-charges-coverage ratio • AAA rating –cover interest and rental charges after tax by 5 to 7 times –industrial firm • AA—4 times • A—3 times • BBB—2 times

  26. ITEMS AFFECTING THE RATINGS OF CORPORATE BONDS • Cash flow—crudely—net income plus depreciation—to total funded debt—notes payable and lease obligations • 65% for AAA • 45-65 for AA • 35-45 for A • 25-30 for BBB

  27. ITEMS AFFECTING THE RATINGS OF CORPORATE BONDS • Management abilities, philosophy, depth and experience Depth and breadth of management Goals, planning process, strategies for R&D, product promotion, new product planning and acquisitions • Specific provisions of debt security • Conditions for issuance of future debt issues, specific security provisions-mortgaging, sinking fund, redemption, covenants

  28. Distress Forecast and Financial Information

  29. Distress analysis and financial information • Definition: financial distress means that a firm has severe liquidity problems that can not be solved without a sizable rescaling of the equity’s operations or structure. • Definition of Insolvency • Total liabilities of a company exceeds its assets “at a fair valuation” • The firms inability to pay its creditors as obligations come due (technical insolvency) • Some states prohibit the payment of cash dividends if the company is insolvent

  30. Financial Crisis, Some Warning Signals • Heavy borrower of working capital • Gross margins narrowing • Business environment subject to rapid change • If volume drops, can production cover expenses • Outdated marketing data • Organization highly structured/decision time • Equipment age/economic downturn • Intensity of industry competition • Increasing borrowing without an increase in sales • Increasing inventory and receivables without an increase in sales

  31. Distress analysis and financial information • Indicators of financial distress: • Cash flow analysis. • Corporate strategy analysis. • Financial statements of the firm and a set of firms in comparison. • External variables such as security returns and bond ratings.

  32. Univariate model of distress prediction: involves the use a single variable in prediction model.

  33. 1. Dichotomous classification tests: • Case study of U.S. Railroad Bankruptcies: Use the ranking of certain variable(s) to predict the bankruptcy of railroad companies. For example, Transportation expenses to operating revenues (TE/OR), and Times interest earned (TIE)

  34. Ranking according to (TE/OR) (cutoff = 0.4305)

  35. 1. Dichotomous classification tests:

  36. 1. Dichotomous classification tests: • Type I error and Type II error: A type I prediction error occurs when a non-bankrupt (NB) firm is predicted to be bankrupt (B) firm. A type II prediction error occurs when a bankrupt (B) firm is predicted to be non-bankrupt firm. Be noted that the loss function for type II error is greatly higher than that of type I error; research has shown that to be 35 times.

  37. 1.Dichotomous classification tests: • Ranking according to (TIE)

  38. 2. Profile Analysis • Comparisons of the mean ratios of distress and non-distress firms have been common in bankruptcy prediction. • For each failed firm, a non-fail firm of the same industry and the same asset size was selected. • The equally-weighted means of 30 financial ratios were computed for each of the failed and non-failed groups in each of the five years before failure.  It examines if there are observable differences in the mean ratios of the two sets of firms.

  39. 2.Profile Analysis(1) 0.45 0.45 0.17 -0.12 -5 -4 - 3 -2 - 1

  40. 2.Profile Analysis(2) 0.08 0.08 0.05 -0.20 -5 -4 - 3 -2 - 1

  41. 2.Profile Analysis(3) 0.85 0.51 0.38 0.37 -5 -4 - 3 -2 - 1

  42. 2.Profile Analysis(4) 0.42 0.43 0.30 0.05 -5 -4 - 3 -2 - 1

  43. 2.Profile Analysis(5) Current ratio 3.5 3.2 2.5 2.1 -5 -4 - 3 -2 - 1

  44. Overview of the uni-variate results There are four categories of variables showing the most consistent difference between bankrupt and non-bankrupt firms were: • Rate of return • Financial leverage • Fixed payment coverage • Stock return and volatility

  45. Multivariate models of distress prediction • We can use econometric tools by applying more than one financial variables that can effectively discriminate healthy firms from distressed firms. Those tools include Discriminant Analysis, qualitative dependent variable regressions (e.g. Linear probability models, probit regression, and logit regression), and non-linear forecasting tools, such as Neural Network techniques. • The dependent variable of these models is either a prediction as to group membership (bankrupt of non-bankrupt), or a probability estimate of group membership (for example, the probability toward bankruptcy).

  46. (1) Discriminant Analysis:

  47. (1) Discriminant Analysis: • Two dependent variables (Zi). • Every sample firm is featured two descriptive variables (XI,YI). • These two descriptive variables have different normally distributed means and same variance-covariance matrix within each group. • So there is a discriminant function that can effectively distinguish both groups: • ZI= Moody’s Rank equal to or better than A; or Moody’s Rank equal to or lower than Baa. • XI= Assessed Property Valuation per Capita • YI= General Obligation Bonded Debt per Capita

  48. (1) Discriminant Analysis: • Step 1: To estimate the coefficients for the discriminant function, which is able to maximize the between group SSE of ZI and minimize the within group SSE of ZI =0.000329 =-0.004887