The Academy of Economic Studies Master DAFI RISK ANALYSIS ON THE LEASING MARKET Coordinator: Prof.Dr. Radu Radut Student: Carla Biclesanu Bucharest, 2008
Table of contents Introduction Chapter I Financial leasing pro s and con s Chapter II Financial analysis of C&C Business Company Chapter III Principal components analysis-case Study over 15 leasing solicitants Conclusions Bibliography
Chapter I- Financial Leasing Pro s and Con s: Provides a definition of Leasing as a financing method Presents the main characteristics and types of leasing Makes an overview of the Romanian leasing market Presents the fundamentals underlying credit risk policies
"Firms now lease everything but time." - U.S. News & World Report
Leasing as a financing method A lease provides an alternative way of obtaining the use of a property. Types of leasing: 1. Financial leasing 2. Operational leasing A finance lease is a contract primarily financial in nature that transfers substantially all the risks and rewards incident to ownership of an asset from the lessor to the lessee. The latter agrees to pay the lessor a series of payments, the sum of which exceeds the purchase price of the asset and provides profit. Title of property may or may not be transferred at the end of the agreed lease period. An operational lease implies that the lessor owns the asset and pays for all necessary investments. The lessor retains the residual value of the equipment and takes it back when the leasing period expires. The lessee simply pays a monthly fee while using the asset.
Leasing Advantages Lower costs and short time release of cash Longer financing period and various options available Fiscal advantages Lower bankruptcy risk
Leasing Disadvantages In case of client default, the lessor can easily legally remove a potentially critical piece of equipment used in the operation process of the lessee It increases the financial risk due to the fixed stream of cash payments. Cancellation costs are high Leasing as opposed to bank financing, grants no "asset value" in the property of the lessee. A lease can sometimes provide a more limited flexibility No improvements upon the value of the asset are made during the period of the lease
Leasing vs Bank LEASE BANK FINANCING 1. Sales tax payable over term of lease 1. Sales tax due up front 2. Conserves valuable working capital 2. Short term money is not used for long term purpose 3. Up to 100% of the asset can be financed under 3. A client contribution (~20% usually) is mandatory certain circumstances 4. Fixed rate for life of lease 4. Floating Interest Rate 5. Limited restrictive covenants 5. Often includes restrictive covenants 6. Transfers risk of equipment obsolescence to 6. 100% Obsolescence Risk lessor 7. Provides a quick and simple financing 7. Normally requires more time and paperwork transaction 8. Treat as expense and bypass capital budget 8. Unable to borrow as capital outlay not budgeted 9. Can be structured to creatively fit a company s 9. Inflexible specific needs 10. Asset can be upgraded easier 10. Asset harder to dispose of 11. Payments may be tax deductible to a large 11. Only interest is tax deductible extend 12. Finance maintenance and other soft costs: 12. freight, warranties, installation, etc. Only finances the acquisition of the asset 13. The company s equity does not increase by including the value of the asset. 13. The value of the asset is included in the company s equity.
Credit risk analysis Aspects taken into consideration by the leasing company: Financial analysis of the credit solicitant company Non financial criteria Credit risk and its components: - Probability of default - Recovery rates of defaulted leases - Exposure at default
Chapter II- Financial Analysis of C&C Business Company Emphasizes the principles of corporate financial analysis and presents the risk assessment of a company trading in cosmetic products Presents how the analysis will lead to credit approval
Description of Credit applicant C&C Business Company Trader of cosmetic products Over 140 stores in Bucharest and all major cities in Romania Over 1000 employees
C&C Business seeks financing for acquiring a plot of land in Bucharest Acquisition value: 1,000,000 euro Down-payment provided: 25% (of the acquisition value) Financed value: 75% (of the acquisition value) Term of the lease: 60 months Number of lease per year: 12 (monthly lease rates) Interest: 8% Residual value: split (20% of the acquisition value, but included in monthly lease rates)
Financial Analysis: major role in decision making process The most argued and debated indicators that offer an important view and complex understanding of the financial standing of a company are: Profitability Leverage Liquidity Efficiency
C&C Business Key Financials as of Dec 2006-Dec 2007 Key Financials in EUR Ths 31-Dec-06 12 31-Dec-07 12 Auditor / Opinion local / qual. not aud. / not aud. Total Revenues 28,965.24 100.00 44,191.71 100.00 Gross Profit 6,079.16 20.99 11,245.02 25.45 EBITDA 2,668.75 9.21 5,670.75 12.83 EBIT 2,441.20 8.43 5,316.27 12.03 Net Profit / Loss 2,204.28 7.61 4,037.10 9.14 Net Operating CF 425.88 1.47 2,431.31 5.50 CF from Investing Activities -579.94-2.00-1,219.55-2.76 CF from Financing Activities -36.03-0.12 425.79 0.96 CF from Equity Activities 0.00 0.00-1,922.21-4.35 Balance Sheet Total 14,404.59 100.00 20,035.18 100.00 Current Assets 13,218.80 91.77 17,866.88 89.18 Fixed Assets 1,185.79 8.23 2,168.30 10.82 ST Liabilities 10,230.06 71.02 12,076.34 60.28 LT Liabilities 0.00 0.00 70.36 0.35 Senior Debt 2,205.40 15.31 2,459.42 12.28 Equity 6,071.50 28.98 7,888.48 39.37 Equity incl. Subdebt 6,071.50 28.98 7,888.48 39.37 Tangible Net Worth 4,133.13 28.78 7,830.59 39.20 Avg F/X 3,525 3,337 YE F/X 3,382 3,610
Financial Analysis of C&C Business Company 1. Profitability ratios C&C BUSINESS PROFITABILITY RATIOS Ratios FY 2006 FY 2007 Gross Profit Margin (GPM) 20.99% 24.45% EBITDA Margin 9.20% 12.83% Return on Assets (ROA) 15.94% 18.65% Return on Equity (ROE) 37.84% 47.31% On account of booming sales and higher mark-ups, company s profitability followed un uphill trend during the analyzed interval.
2. Leverage ratios C&C BUSINESS LEVERAGE RATIOS Ratios FY 2006 FY 2007 Indebtedness ratio 15.31% 11.92% Gearing ratio 0.6x 0.65x Leverage ratio 2.37x 2.54x Interest cover 13.5x 25.3x Comfortable leverage in both FY06 and FY07, the company relying partly on short term bank facilities, which finance some 10%-15% of total assets, and maintaining an adequate level of equity. 3. Liquidity ratios C&C BUSINESS LIQUIDITY RATIOS Ratios FY 2006 FY 2007 Current Ratio 1.3 1.5 Quick ratio 0.1 0.1 Acceptable current ratio levels, yet poor and flat quick ratio triggered by the large amounts of inventories maintained.
4. Activity ratios C&C BUSINESS ACTIVITY RATIOS Ratios FY 2006 FY 2007 Inventory Turnover Ratio 177 days 168 days Receivables Period 5 days 3 days Payables Period 66 days 58 days Suppliers are paid at 58 days and the accounts receivables are cashed in a matter of days (our company being a retailer with a broad customer base), revealing once again the heavy dependence upon supplier credit.
Rating C&C Company is ranked 2.5 by the leasing company which is using a rating scale consisting of 10 grades ranging in half steps from 0.5 to 5.0 The single grades are defined as per the following table: Financial as well as non-financial criteria is considered when granting the credit rate, with weights of 60% and 40% respectively.
Final decision No bank incidents or bad payment behavior registered. According to the financial analysis and information available regarding the credit solicitant and the lease object, the leasing company will proceed to granting financing to the solicitant.
Chapter III-Principal Components Analysis: Is a case study over 15 leasing solicitants from the production sector. Aims to reveal how, out of 10 variables that characterize the companies, two principal factors (components) can be extracted. The factors summarize the original selected variables. Point out where C&C Business company can be placed (clustered).
Principal components analysis (PCA) The main applications for this technique are: to reduce the number of variables (indicators); to detect the relationships between indicators and principal components extracted; to classify companies.
Selected variables (out of 15 food production companies) Variable Variable 1 Variable 2 Variable 3 Variable 4 Variable 5 Variable 6 Variable 7 Variable 8 Variable 9 Variable 10 Fixed assets Current assets Current liabilities Total liabilities Equity Turnover Operating expenses Operating income Financial result Net profit
Data Matrix Company Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Var 8 Var 9 Var 10 1 1638 2120 1921 3541-1638 6119 9741 288 164 122 2 1079 2110 2242 2278-1079 4347 3833 1808 1690 1651 3 791 690 1476 1767-791 2590 2798-117 -288-288 4 619 1023 922 1297-619 2612 3457 5-51 9 5 448 363 639 639-448 973 1163 9 4 2 6 536 304 210 885-536 612 129 77 12 9 7 15 26 43 43-15 195 200 4 2 1 8 225 119 268 309-225 1457 1870 72 52 49 9 2 20 9 16-2 45 37 8 8 8 10 786 479 831 891-786 3902 4952 21-51 -51 11 475 194 436 436-475 402 440 20 4 3 12 162 397 37 636-162 399 922 51 46 45 13 1388 3398 2959 3118-1388 5361 6177 148 29 24 14 641 999 1196 1196-641 5147 5129 169 98 81 15 48 249 255 255-48 983 928 55 55 41 The scope of the Analysis is to find W new variables, called principal components, (W being significantly lower than 10), in order to reduce the dimensionality of the data matrix. The variance of each principal component must be as large as possible.
First step into PCA is to build the correlation matrix, based on standardized scores of the original variables. Var 1 Var 2 Var 3 Var 4 Var 5 Var 6 Var 7 Var 8 Var 9 Var 10 Var 1 1.000 Var 2 0.854 1.000 Var 3 0.883 0.951 1.000 Var 4 0.960 0.918 0.919 1.000 Var 5 0.587 0.848 0.785 0.597 1.000 Var 6 0.840 0.821 0.865 0.860 0.633 1.000 Var 7 0.870 0.770 0.780 0.873 0.507 0.956 1.000 Var 8 0.881 0.805 0.818 0.892 0.553 0.968 0.988 1.000 Var 9 0.251 0.392 0.396 0.302 0.412 0.295 0.155 0.305 1.000 Var 10 0.241 0.387 0.389 0.290 0.415 0.284 0.142 0.292 0.999 1.000 The essence of the correlations in this matrix must be captured. Most of the correlations are large correlations in the matrix, but there are also several low and medium correlations, variables 9 and 10 are almost perfectly correlated (financial result and net profit) and, at the same, time low correlated with the other eight variables.
Extraction of Principal Components 8 Scree Plot 6 4 2 Eigenvalue 0-2 1 2 3 4 5 6 7 8 9 10 Component Number In order to decide how many components to retain, a method called Scree Test can be used. This consists of a graphic representation, called Scree Plot. The eigenvalues indicates the amount of variance accounted by each new component. As the graphic shows, two components can be extracted.
Total variance explained Initial Eigenvalues Component Total % of Variance Cumulative % 1 7.082 70.8198 70.8198 2 1.8198 18.1982 89.018 3 0.6466 6.4658 95.4838 4 0.2955 2.9551 98.4389 5 0.084 0.8402 99.2791 6 0.0631 0.6315 99.9105 7 0.0079 0.079 99.9896 8 0.0009 0.0085 99.9981 9 0.0002 0.0017 99.9998 10 0 0.0002 100 The eigenvalues indicates the amount of variance accounted by each new component The first principal component explains the maximum variances in all variables- a variance of 70.8198% of the total sample variances. The second principal component explains 18.1982% of the total sample variances. Cumulative, the first two principal components explain 89.018% of the total sample variances. Consequently, sample variation is well summarized by two principal components and the reduction in the data is from 10 variables (indicators) to 2 variables.
Component matrix is known as the factor matrix or loading matrix. It includes the correlation between principal components scores and the original variables. Rotated Component Matrix Component W1 W2 Comments: - The first principal component is very well correlated with variables 1 to 8; VAR1 VAR2 0.939 0.893 0.107 0.32 - The second principal component is very well correlated with variables 9 and 10; VAR3 VAR4 VAR5 VAR6 VAR7 0.905 0.948 0.652 0.94 0.953 0.306 0.161 0.431 0.145-0.02 -Variables 1 to 8 represent the assets, the liabilities and the equity of the companies and also the results in terms of expenses and sales. In other words, the performance of companies according to investments made and funds attracted. This principal component underlines the economic dimension of companies. VAR8 VAR9 VAR10 0.942 0.142 0.13 0.132 0.975 0.978 - Variables 9 and 10 represent the financial result and the net profit of companies. The second principal component expresses the financial dimension or performance of companies, in terms of profitability.
The final output of PCA is that it reveals which are the best and the worst companies in terms of financial results and economic dimensions and which companies need to be more carefully analyzed. 2.5 2.0 1 13 REGR factor score 1 for analysis 1 1.5 1.0.5 0.0 -.5-1.0-1.5-2 3-1 14 10 4 11 6 5 8 12 15 79 0 1 2 3 2 4 REGR factor score Total Pop ulation REGR factor score 2 for analysis 1
Comments: since the second factor (factor_2) represents the financial performance of companies, the second company has the best financial results. If one takes a glance at Table 4, sees that Company 2 has the highest Financial Result and net profit out of all fifteen companies. since the first factor (factor_1) represents the economic performance of companies, companies 1 and 13 have the best results in that respect. As Table 4 shows, companies 1 and 13 have the highest operating income, turnover and expenses, performance appraised with the help of most important assets and liabilities (out of the fifteen companies). companies 3, 4 and 10 have the worst financial performance. In Table 4, it can be seen that they all reported loss, except for the company 4 which reported net profit, but due to extraordinary expenses and not to its operating activities. companies 5,6,7,8,9,11,12,15 are all part of a group which probably needs more analysis in order to make a relevant difference between them. The companies are medium and low financial and economic efficient.
Conclusions PCA is an excellent technique for data reduction, based on which an easier interpretation of the original variables can be completed. From the point of view of the leasing company, PCA helps in two manners: (1) it provides an overall view over the main dimensions (economic and financial) of companies that should be taken into consideration when analyzing the credit request and (2) it also allow forming an hierarchy of credit solicitants based on their performances. To sum up, PCA is a helpful tool for credit risk analysis, but not sufficient, because economic and financial performances of companies must be analyzed in detail, which means ratios calculations and non financial criteria taking into consideration.