The year 1988 marked a round turn in the Vietnamese economy, which concerned initiating the economic transformation from the former centrally planned to a market economic oriented system with state management.
Chapter Introduction 1.1 Rationale The year 1988 marked a round turn in the Vietnamese economy, which concerned initiating the economic transformation from the former centrally planned to a market economic oriented system with state management To cope with the new environment, the banking sector started its deregulation This is featured in the transformation from a uni-type to two-tier system, wherein the State Bank of Vietnam play the role of cash issuer and controller of commercial banking, and other banks work toward establishing a true business platform During 1988-1990, the commercial banking business was ‘booming’ in terms of the number of new banks established and in lending activities However, due to the poor quality in operation and insecurity, nearly all credit cooperatives and joint stock banks came to bankruptcy Billions of dong were frozen in bad debts within state owned banks and as assets in shareholding banks which still operated To overcome this situation, one requires a suitable credit policy to: • Extend credit to every economic sector, public and private; including households • Diversify funding instruments in light of market developments • Restructure bank loans with further emphasis on medium and long term credit rather than short term lending • Enhance credit quality, and strictly manage credit risks • Diversify the credit market for banks In order to perform this credit policy, Vietnamese banking system initiated computerization, and currently, payments and statistics have been computerized By the end of 1993, banks set up LAN internally In the coming years, the following steps will be taken: • Computer link between State Bank of Vietnam and banks • Computer link among banks • Computer link between banks and customers Along with computerization, different types of advanced payment facilities are gradually introduced; like credit cards, and ATM installation to serve residents and foreign travelers In that situation, efficiency and effectiveness are critical for the success of the banking industry They are not only dependent upon managerial skills but also the adoption of new technology To control credit quantity and quality, it is necessary to have a new technology to enhance credit quality, and manage credit risks Determining which loan applicant should be extended credit, as well as the amount of credit, are major practical decisions that confront commercial bank lending officers, credit analysts, and loan committees These decision makers must assess the financial health of an applicant, which requires analysis of both quantitative and qualitative information on the outlook of the company To correctly perform the analysis, commercial loan officers need to have an understanding of the primary Cs of lending: credit, collateral, capital, capacity, and character As commercial loan officers evaluate a company, they examine numerous financial ratios, percentages, and trends, and perform many interrelated analyses Because the complex nature of the problem requires experience and precludes the use of simple algorithms, expert systems are appropriate for this type of loosely structured, complex problem An expert system is a software system that imitates the reasoning results of human experts in a well defined domain It aims to generate advice about problems in the domain comparable to the advice that a human expert would deduce for the same problems In recent years, practitioners have become more familiar with the technology and the technology has advanced to become more supportive of business applications Recent financial applications of expert systems include: • Decision analysis in securities trading, • Cashflow analysis, and • Venture capital analysis for small telecommunication companies Another problem area that can benefit from this technology is commercial loan analysis, the process of evaluating a company’s financial strengths and weaknesses 1.2 Objective The purpose of this research is to design an expert system that helps commercial bank lending officers, credit analysts, and loan committees to reduce the time devoted to the analysis in evaluating loan applicants and to improve the quality of the evaluation 1.3 Scope of the research The system is designed to analyze commercial loans for industrial or retail borrowers The system only focuses on the credit granting decision and does not consider other aspects of the credit granting decision process such as keeping track of collateral maturities, collection follow-up, etc In addition, the system does not provide a specific evaluation of economic or competitive factors within a given industry Chapter 2 Literature Review Financial Perspective Automation in Banking procedures has been evenly spread out over the last decades with the advent of fast computing machines Automation in banking began with the use of computers for performing rote clerical tasks and moved on rapidly to automatic transaction processing Soon, a need was felt for using computers for making managerial decisions, or assist the upper management in making such decisions One of the departments i.e credit department felt the urgent need for the same and systems based on statistical, expert systems began to develop and are currently in use The search for better and efficient methods for making the credit decisions continues in the pursuit of perfection The dream of all lending institutions for a completely reliable, efficient automated procedure for evaluation of prospective credit applicants continues to remain a dream Tamisin (1991) has created a model of the loan negotiation process based on case-based reasoning process The prototype system designed by the author makes use of previous experiences to guide the problem solving process The author gave an introduction to the loan negotiation process by first defining clearly the meaning of negotiation and then described the phases or the life cycle of the negotiation process as divided into three stages Loan negotiation consists of three phases (Marsh, 1984) which are shown in the figure below: PREPARATION and SUBMISSION ANALYSIS and EVALUATION CREDIT CONDITION NEGOTIATION Figure 2.1 Loan Negotiation Phases Preparation and Submission Phase: This phase involves the preparation of the applicant's application using the project proposal, financial report and loan requirements Analysis and Evaluation phase: This phase takes the output of the last phase, the financial application and uses it as the input It gives the overall loan application's status as output This phase involves the analysis and evaluation of the financial report, project proposal and the loan requirements After this, the credit history of the applicant is thoroughly investigated and the overall application status is provided The status signifies the decision for approval, rejection or negotiation of the loan Credit Condition Negotiation Phase: If the applicant is a corporate customer, a negotiation is generally required This phase is invoked when a talk between the two parties is called for The inputs to this phase are the analysis status and loan requirements During the negotiation process, one negotiating agent proposes a set of conditions If the second agent agrees on this set of conditions, then an agreement is reached Otherwise, the second agent proposes its own set of conditions This process is iterated until an agreement or a deadlock situation is reached Bryant (1962) gives the entire working of the mortgage department of a lending institution All the terms and paraphernalia related to the Credit Division are outlined brilliantly However, the drawback with the book was that of it’s outdation As it was written in the early 1960’s, most of the policies and workings of the credit department have undergone appreciable changes and modifications, as of today The second drawback of the book is that, it covers only Home Bank Loans, and strictly remains in that domain Zinkhan (1990) earlier study had indicated the principles of credit require only 'Five Cs' i.e capacity, capital, character, collateral, and conditions - in relationship to the evaluation of a given firm's credit risk This paper suggests the 'Sixth C' of Credit and calls it the 'Customer Profitability Analysis' A number of efforts have been undertaken to quantify and summarize two of these five C's for the purpose of estimation of the bankruptcy of firms: capacity and capital Notable among these efforts are the Z-score model of Altman (1968), the Zeta analysis model of Altman et al (1977), and the application of a discriminant analysis model to small businesses by Edminister (1972) In addition, a credit-scoring model was developed by Chesser (1974) to determine the creditworthiness of a potential business loan customer According to the author, the above mentioned quantitative models ignore the character, collateral, and conditions dimensions of credit risk analysis The purpose of this technique is to jointly evaluate the objective and subjective estimations of the six C's in order to generate an overall indicator of the relative attractiveness of a given potential business loan A hierarchical model of the business loan evaluation process was also suggested as shown in Figure 2.2 Acharya (1990) has designed and tested a Loan Negotiation System based on a rule based system “Negotiation is a dynamic process of adjustment by which two or more parties, each with his own objectives, confer together to reach a mutually satisfying agreement on a matter of common interest The process of negotiation may end with or without a consensus” According to the author, Negotiation is oriented toward the future, its progress is defined by the negotiating agents It is also oriented with the sole goal that each party tries to obtain maximum payoff Overall Indicator of Loan Attractiveness Indicator of Expected Customer Profitability Indicator of Expected Profitability on Current Transaction Indicator of Capacity and Capital Risk Indicator of Credit Risk Indicator of Expected Profitability on Future Transaction Indicator of Character Risk Indicator of Collateral Risk Indicator of Conditions Risk Figure 2.2 Hierarchical Model of the Loan Evaluation process The five phases through which every negotiation proceeds are: exploration, bidding, bargaining, settling, and ratifying Usually, they may not follow sequentially and the negotiators may alter the sequence of the phases The negotiators may follow the sequence on one aspect of the deal and then start all over again on a second aspect Duchessi et al (1988) describe a knowledge-Engineered system for Commercial Loan Decisions The paper initially describes the Commercial Loan Analysis process which covers a general and a detailed analysis This is followed by a complete description of the expert system 'Commercial Loan Analysis Support System (CLASS)' This expert system is described as one which is designed to evaluate a company's financial posture, recommend commercial loan decisions and pertinent covenants, and document the loan analysis Commercial Loan Analysis process as described by the authors examines numerous financial factors to uncover a company's financial weaknesses Their evaluation begins with a general analysis involving an examination of key financial trends and factors When one of those factors does not meet the industry norm, commercial loan officers must perform a more detailed analysis to uncover the causes General Analysis: It consists of trend analysis, and separate analyses of credit, collateral, capital and capacity General trend analysis provides loan officers with a quick indication of a company's performance in several key areas: sales, operating income, net income, selling and administrative expenses, working capital, and cash flow Credit analysis measures a company's ability to repay its short and long-term obligations The credit analysis really consists of efficiency, profitability, and liquidity analyses Inventory turnover, receivables turnover, fixed asset turnover, and total asset turnover are the primary efficiency measures Profitability analysis considers operating margin, profit margin, return on assets, and return on equity, while liquidity analysis includes the current and quick ratios Collateral analysis examines the relationship between the value of all assets and pledged assets To estimate their value, loan officers appraise book value, age, and condition of assets Capital analysis provides an indication of a company's leverage position Long term debt to total assets, total debt to total assets, interest coverage, and fixed charge coverage are the primary factors If all four measures are above industry standards, a company's capital position is strong If they are all below, capital is poor Capacity analysis measures the degree to which a loan can be supported by a company, using the same ratios as capital analysis Detailed Analysis: This is performed whenever a general analysis indicates a weakness As problem areas are identified and examined, the loan officers accumulate loan covenants, or restrictions, which become part of the final loan agreement Gilliam (1990) describes the financial analysis procedure for the estimation of lending risk which can be calculated using tangible factors or ratios The terms and ratios described correspond to the derivable ratio's that are related to the 'Five Cs' of credit The author states that the financial analysis can provide valuable information about why a company needs to borrow, what the loan will fund, whether operations will be able to generate sufficient cash flow to repay debt, and whether company assets will be available as collateral If the borrower has met the initial criteria for a desirable customer through interviews and credit investigation, the next place to begin risk analysis is with the company's financial statements The degree of reliance on financial analysis is directly related to how they are prepared In defining lending risk, there are three general areas of investigation relating to financial statements Economic Condition, which measures the company's leverage, liquidity, and activity positions; Profitability, which addresses break-even analysis and other trends in company sales and expenses; and Cash Flow, which assists in determining sources and uses of cash to pinpoint why a company is borrowing and whether the loan can be repaid Lenders have access to several tools to analyze these areas Trend analysis focuses on a company's direction regarding assets, liabilities, revenues, and expenses Trend analysis focuses on a company's direction regarding assets, liabilities, revenues, and expenses Comparing management's plan to actual performance highlights management's ability to forecast and plan for future events The lender's primary purpose is to make loans that can be repaid while minimizing the bank's exposure to loans with poor credit quality To be successful, it is important for lenders to assess areas of relative strength and weakness by reviewing the economic condition, profitability, and cash flow of a company using tools such as trend analysis and industry comparison Harris (1994) gives the fundamentals of trading describing it as a zero-sum game when measured relative to underlying fundamental values This paper classifies traders into three categories winning, utilitarian and futile This paper then throws light on the origins of trading profits , how the contributions of the three categories affect the price efficiency and market liquidity 2.2 Existent Systems Holsapple et al (1988) throw light on the adaptation of expert system technology to financial management The paper describes the rudimentary architecture of expert systems, recognition of the potential of expert systems by the financial markets and their acceptability and impact The conclusion made by the paper is that current technology is inadequate for applications requiring insight, creativity, and intuition According to the author, an expert system is a software system that imitates the reasoning results of human experts in a well defined domain It aims to generate advice about problems in the domain comparable to the advice that a human expert would deduce for those same problems Table 2.1 Expert Systems for Financial Applications of ' Commercial Loan Analysis' Expert System Company Function Authorizer's Assistant Financial Analyzer Lending Advisor Mortgage Loan Analyzer Underwriting Advisor American Express Athena Group Syntelligence Arthur Anderson Syntelligence Credit Authorization Commercial Loan Approval Credit Analysis Mortgage Loans Evaluation Commercial Underwriting Marble (1988) ( Managing and Recommending Business Loan Evaluation ) : is an expert system with inductive learning to evaluate Business Loans The paper first describes the loan evaluation process and the basic difficulties that confront a loan decision The paper then describes the design and construction of Marble Countrywide Loan-Underwriting Expert System (CLUES) (1991): The paper gives the life cycle of a loan and then describes the planning, construction and working principles of CLUES An important aspect covered in this paper is the enumeration of the reasons as to why the rule based methodology was used in CLUES The drawbacks and advantages of the various AI technologies were also touched upon Mortgage Risk Evaluator (MRE) (1962): Nestor, one of the earliest neural network companies, has a product that appraises mortgage applications The system was trained on several thousand actual applications, about half of which were accepted and the other half of which were rejected by the human underwriters Learning from the successes and failures of this body of experience , the system looks for patterns in the data to determine what constitutes a bad risk AVCO Financial Services , in Irvine, California, uses this neural network system for Credit Risk Analysis The system was trained on more than 10,000 credit case histories A New York Times edition reported that one test indicated there would have been a 27 percent increase in profits if the neural network system had been used instead of the computerized evaluation system previously used by AVCO Besides credit risk analysis , other commercial applications of neural networks in Finance include Identification of forgeries, interpreting handwritten forms, rating investments and analyzing portfolios Neuroforecaster is an advanced windows based, user-friendly, intelligent, neural network forecasting tool Made by Accel Infotech (S) Pte Ltd., Singapore It is packed with the latest technologies including neural network, fuzzy computing and non-linear dynamics Besides performing the Loan Analysis, it can also be used for other financial applications some of which are enumerated below Stock Price Prediction, Stock market six monthly return forecast, Stock selection based on company ratios, Stock market index forecast, National GDP forecast, Sales Forecast, US$ to Deutshmark exchange rate forecast, Fraud Detection and Fault Diagnosis, Air passenger arrival forecast, Credit rating of bank loan applications, Property price valuation, Bond Rating, Construction demand forecast, XOR-A classical problem 2.3 Relative Comparison between Methodologies Digiammarino et al (1991) elucidate on the gradual evolution of Artificial Intelligence technology in the automation of loan underwriting The paper claims that several applications of information technology are now experiencing rapid growth as they penetrate the middle-size and smaller-size ranks of the industry The author first describes the ScoreCard, a statistical technique for credit evaluation, followed by the comparison between the Statistical, AI and Neural Network methodologies for Loan underwriting As per the author, the Scorecard is the most familiar kind of decision support system in consumer credit and dates back to the 1960's or earlier Derived from statistical analyses that correlate application and credit report data to each lender's actual loan losses, these models accurately measure the probability of each applicant defaulting The score is calculated by the application processing system and used along with collateral and credit policies to recommend a decision to the reviewing credit officer The degree to which these recommendations are followed varies widely from one organization to another Innovative financial institutions' current involvement in advanced research in decision support systems is currently moving in two dimension: profit-driven objectives and more sophisticated modeling techniques, including artificial intelligence The issue of profitability is so important, that leading organizations are beginning to look for ways to address it head-on in decision systems In addition to sharpening the objective of decision support models by incorporating profitability, innovators are applying more advanced techniques to build the models themselves The author claims that classical statistical techniques are capable of producing more powerful models than are commonly used today, but the improvement is only marginal compared to the difficulty of implementing more complex equations As a result, a great deal of attention is being devoted to the use of artificial intelligence in consumer credit Neural networks are potentially very valuable tools for the credit industry in situations where these factors occur together: large amounts of data, complex interactions, quick feedback on the results of each decision, and a lack of human constraints Expert systems take the opposite approach, eschewing hard data in favor of the judgment of human experts A weakness is that expert systems not make use of hard data even when such information could enhance human judgment In these instances ( for example, credit application screening ) , the expert system can be modified to include a risk measure as part of the final recommendation The key difference between a classical statistical model and a neural net is that the net typically analyzes a larger number of mathematical forms for the relationships between predictive variables This procedure can lead to better predictive ability when the predictors work in complex but stable and well-defined ways Eyden (1994) This paper compares the use of artificial neural networks (ANNs) and multiple discriminate analysis (MDA) in the prediction of credit risk It clearly brings out the comparison between statistical modeling versus neural networks in financial decision making MDA is traditionally regarded as the most applicable statistical method for the prediction of credit risk and is widely used by financial institutions and other organizations MDA is based on a linear equation and has the limitation of not being appropriate for non-linear data ANN's on the other hand, incorporate both linear and nonlinear components and therefore, according to the paper, prove that they are more suitable for different data types The paper presents a case study of financial application (credit scoring problem) to measure the comparative performances of the two models The findings of the paper indicate that ANN's outperform MDA in the forecasting of credit risk Costantino (1991) gives out a very useful eight-point procedure for determining whether expert systems and neural networks are in fact superior to current credit-scoring processes The paper says that, although the framework of this procedures is loosely defined, it addresses the full range of issues that confront creditors in the adoption of new technologies The paper draws a clear cut method of evaluating the superiority or inferiority of the AI technology over the statistical methods for credit risk analysis According to the author, the evaluation of new technologies requires an information base that considers basic business componentseconomic returns, people, systems, and control The impact on these basis entities can be best understood by addressing the eight issues as given below: The cost of new technologies vs the old: This calculation depends on the number of creditscoring systems requiring redevelopment and the configuration of each of the new technologies Thorough understanding of the current process: Answers to questions like "Which applicants are rejected/ accepted at high rates? " ; " Which applicant profiles are accepted as often as they are rejected? "; for each credit-scoring system in use helps to understand the nature of the incoming populations and how the current process selects new accounts Relationship of the approval rate to the bad rate: This relationship can be developed by analyzing a sufficiently large random sample of good performers, bad performers, and rejects Knowing the relationship between approval rates and bad rates helps to determine the size and potential risk associated with the applicants most likely to be affected by the new technology Long term cost-return tradeoffs: To evaluate this, a summary table is drawn from the last three evaluation steps and ratios are calculated Service Level maintenance or enhancement: Whether the new technology will cause decision turnaround times to increase to unacceptable levels or improve This can be tested during the test-benchmark step Real-time environments can significantly degrade the performance of technologies that were designed as batch systems The Readiness of organization to manage the development and implementation of the new technology: This step is the most qualitative one to evaluate The firm's data processing capacity: This can be found by the answers to questions like "Are there enough programmers available to code and test the required programs ?"; "Will new hardware have to be acquired in order to implement the new technology ?" ; " Will new hardware and interfaces need to be acquired ?" The technology's auditability and security capability: Auditability comes in two flavors-applicant and technology Auditability is key to maintaining system controls Security is the key to maintaining competitive advantage and to preventing fraudulent transactions Chapter Theoretical consideration 3.1 Credit analysis: what make a good loan? Credit Department must satisfactorily answer three major questions regarding each loan application: Is the borrower creditworthy? How you know? Can the loan agreement be properly structured and documented so that the bank and its depositors are adequately protected and the customer has a high probability of being able to service the loan without excessive strain? Can the bank perfect its claim against the assets or earnings of the customer so that, in the event of default, bank funds can be recovered rapidly, with low cost and low risk? 3.1.1 Is the borrower creditworthy? The question that must be dealt with before any other is whether or not the customer can service the loan - that is, pay out the credit when due, with a comfortable margin for error This usually involves a detailed study of six aspects of loan application - character, capacity, cash, collateral, conditions, and control All must be satisfactory for the loan to be a good one from the lender’s point of view • Character The loan officer must be convinced that the customer has a well-defined purpose for requesting bank credit and a serious intention to repay Once the purpose is known, the loan officer must determine if it is consistent with the bank’s current loan policy Even with a good purpose, however, the loan officer must determine that the borrower has a responsible attitude towards using borrowed funds, is truthful in answering the bank’s questions, and will make every effort to repay what is owed Responsibility, truthfulness, serious purpose, and serious intention to repay all moneys owed make up what a loan officer calls character • Capacity The loan officer must be sure that the customer requesting credit has the authority to request a loan and the legal standing to sign a binding loan agreement This customer characteristic is known as the capacity to borrow money • Cash Does the borrower have the ability to generate enough cash, in the form of income or cash flow, to repay the loan? In general, borrowing customers have only three sources to draw upon to repay their loans: (a) cash flows, (b) the sale or liquidation of assets, or (c) funds raised by issuing debt or equity securities However, bankers have a strong preference for cash flow as the principal source of loan repayment because asset sales can weaken a borrowing customer’s balance sheet, while additional borrowing by a loan customer can make the bank’s position as creditor less secure Moreover, shortfalls in cash flow are common indicators of failing businesses and troubled loan relationships The loan officer’s evaluation of a borrower’s cash involves asking and answering such 10 : Provision Fiscal : : : : : : : : Minority Interests : : : : : : : : Deffered Income : : : : : : : : Reserves : : : : : : : : : : : : : : : : NET WORTH : : : : : : : : : : : : : : : : Preferred Stock : : : : : : : : Common Stock : 27,000.00: 31,500.00 : 43,500.00 : 43,500.00 : 43,500.00 : 43,500.00 : : Paid in Capital : 73,500.00: 202,500.00 : 555,000.00 : 555,000.00 : 555,000.00 : 555,000.00 : : Minority Interest : : : : : : : : Warrants : : : : : : : 83 CTY THUONG MAI PHU NHUAN BALANCE SHEET Page: : Other Equity : : : : : : : : Revaluation Reserve : : : : : : : : Capital Reserves : : : : : : : : Retained Earning : 501,489.00: 767,880.00 : 108,340.00 : 120,372.00 : 371,709.00 : 1,085,066.00 : : Cumulative Trans Adj : : : : : : : : : : : : : : : : Total Net Worth : 601,989.00: 1,001,880.00 : 706,840.00 : 718,872.00 : 970,209.00 : 1,683,566.00 : : : : : : : : : : Total Liab&Net Worth : 4,944,837.00: 6,517,118.00 : 8,122,989.00 : 12,239,279.00 : 18,008,217.00 : 27,538,910.00 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : 84 CTY THUONG MAI PHU NHUAN INCOME STATEMENT Page: : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : :History :History :History :Forecast :Forecast :Forecast : : DETAILED FINANCIALS :31/12/92 :31/12/93 :31/12/94 :31/12/95 :30/12/96 :30/12/97 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : Net Sales : 10,084,899.00: 19,582,815.00 : 25,239,465.00 : 39,928,632.00 : 63,166,776.00 : 99,929,334.00 : : : : : : : : : : Cost of Goods Sold : 6,571,689.00: 12,493,718.00 : 16,373,694.00 : 25,771,416.00 : 40,770,174.00 : 64,498,090.00 : : Depreciation in COGS : 52,644.00: 90,229.00 : 138,480.00 : 138,848.00 : 134,652.00 : 77,476.00 : : : : : : : : : : Gross Profit/Revenue : 3,460,566.00: 6,998,868.00 : 8,727,291.00 : 14,018,368.00 : 22,261,950.00 : 35,353,768.00 : : : : : : : : : : SG & A Expenses : 3,297,141.00: 6,147,339.00 : 8,693,865.00 : 13,190,264.00 : 20,866,892.00 : 33,011,256.00 : : Depreciation : 104,090.00: 168,440.00 : 252,111.00 : 252,781.00 : 245,141.00 : 141,051.00 : : Amortization : 21,072.00: 40,908.00 : 72,137.00 : : : : : : : : : : : : : Operating Income : 38,263.00: 642,181.00 : -290,822.00 : 575,323.00 : 1,149,917.00 : 2,201,461.00 : : : : : : : : : : Interest Exp ST : 197,886.00: 511,597.00 : 829,908.00 : 665,763.00 : 992,918.00 : 1,501,962.00 : : Interest Exp Exist : : : : 135,277.00 : 119,353.00 : 103,429.00 : : Interest Exp New Dt : : : : : : : : Interest Exp Sub Dt : : : : : : : : : : : : : : : : Tot.Interest Expense : 197,886.00: 511,597.00 : 829,908.00 : 801,040.00 : 1,112,271.00 : 1,605,391.00 : : : : : : : : : : Capitalized Interest : : : : : : : : Interest Income : : : : : : : : Other Gain on Sale : 111,201.00: 178,101.00 : 177,504.00 : 246,161.00 : 389,425.00 : 616,067.00 : : Minority Int/Equity : : : : : : : : Other Loss On Sale : : : : : : : : : : : : : : : 85 : Profit Before Taxes : -48,422.00: 308,685.00 : -943,226.00 : 20,444.00 : 427,071.00 : 1,212,137.00 : : : : : : : : : : Income Tax -Current : -21,008.00: 14,130.00 : -246,183.00 : 7,575.00 : 158,244.00 : 449,138.00 : : Income Tax -Deferred : -18,631.00: 28,164.00 : -37,503.00 : 837.00 : 17,490.00 : 49,642.00 : : : : : : : : : : Profit bf Extraord : -8,783.00: 266,391.00 : -659,540.00 : 12,032.00 : 251,337.00 : 713,357.00 : : : : : : : : : : Extraordinary Items : : : : : : : : : : : : : : : : Net Profit : -8,783.00: 266,391.00 : -659,540.00 : 12,032.00 : 251,337.00 : 713,357.00 : : : : : : : : : : Common Dividend : : : : : : : : Preferred Dividend : : : : : : : : Adjustment to R.Earn : : : : : : : : Retain Earnings : -8,783.00: 767,880.00 : 108,340.00 : 120,372.00 : 371,709.00 : 1,085,066.00 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : 86 CTY THUONG MAI PHU NHUAN CASH FLOW Page: : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : :History :History :History :Forecast :Forecast :Forecast : : CASHFLOW :31/12/92 :31/12/93 :31/12/94 :31/12/95 :30/12/96 : 30/12/97 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : Sales - Net : 10,084,899.00: 19,582,815.00 : 25,239,465.00 : 39,928,632.00 : 63,166,776.00 : 99,929,334.00 : : Change in Receivable : : -566,634.00 : 30,220.00 : -2,704,375.00 : -3,087,251.00 : -4,884,008.00 : : : : : : : : : : CASH FROM SALES : 10,084,899.00: 19,016,181.00 : 25,269,685.00 : 37,224,257.00 : 60,079,525.00 : 95,045,326.00 : : : : : : : : : : Cost of Goods Sold : -6,571,689.00:-12,493,718.00 :-16,373,694.00 :-25,771,416.00 :40,770,174.00 :-64,498,090.00 : : Change in Inventories: : -382,038.00 : -950,044.00 : -1,799,800.00 : -2,751,353.00 : -4,352,620.00 : : Change in Payables : : -71,324.00 : 203,532.00 : 1,821,613.00 : 2,077,639.00 : 3,286,810.00 : : : : : : : : : : CASH PRODUCTION COST : -6,571,689.00:-12,947,080.00 :-17,120,206.00 :25,749,603.00 :-41,443,888.00 :-65,563,900.00 : : : : : : : : : : GROSS CASH PROFITS : 3,513,210.00: 6,069,101.00 : 8,149,479.00 : 11,474,654.00 : 18,635,637.00 : 29,481,426.00 : : : : : : : : : : SG & A Expense : -3,297,141.00: -6,147,339.00 : -8,693,865.00 :-13,190,264.00 :20,866,892.00 :-33,011,256.00 : : Change in Prepaids : : 22,883.00 : 16,174.00 : -9,793.00 : -15,491.00 : -24,508.00 : : Change in Accruals : : 102,874.00 : 86,264.00 : 924,306.00 : 802,733.00 : 1,269,917.00 : : Change in Oth B/S Ac : : -20,033.00 : 4,877.00 : -100,613.00 : -159,170.00 : -251,806.00 : : Other Income/Expense : 111,201.00: 178,101.00 : 177,504.00 : 246,161.00 : 389,425.00 : 616,067.00 : : : : : : : : : : Cash Operating Exp : -3,185,940.00: -5,863,514.00 : -8,409,046.00 :-12,130,203.00 :19,849,395.00 :-31,401,586.00 : : : : : : : : : : Income Taxes Paid : 39,639.00: 11,248.00 : 123,433.00 : 185,795.00 : -225,027.00 : -578,970.00 : : : : : : : : : 87 : NET CASH AFTER OPER : 366,909.00: 216,835.00 : -136,134.00 : -469,754.00 : -1,438,785.00 : -2,499,130.00 : : : : : : : : : : Cap Exp due to Repla : : : : : : : : : : : : : : : : CASH FOR DEBT SERVIC : 366,909.00: 216,835.00 : -136,134.00 : -469,754.00 : -1,438,785.00 : -2,499,130.00 : : : : : : : : : : Interest Expense : -197,886.00: -511,597.00 : -829,908.00 : -728,494.00 : -992,480.00 : -1,410,512.00 : : Bank Fees & Commissn : : : : : : : : Dividend paid,PreStk : : : : : : : : : : : : : : : : Financing Costs : -197,886.00: -511,597.00 : -829,908.00 : -728,494.00 : -992,480.00 : -1,410,512.00 : : : : : : : : : : NET CASH INCOME : 169,023.00: -294,762.00 : -966,042.00 : -1,198,248.00 : -2,431,265.00 : -3,909,642.00 : : : : : : : : : : Current Portion LTD : : -146,718.00 : -132,071.00 : -228,175.00 : -228,175.00 : -228,175.00 : : : : : : : : : : CASH AFTER DEBT AMOR : 169,023.00: -441,480.00 : -1,098,113.00 : -1,426,423.00 : -2,659,440.00 : -4,137,817.00 : : : : : : : : : : Dividend paid,ComStk : : : : : : : : Account Rec Intra-Co : : : : : : : : Account Pay Intra-Co : : : : : : : : Due from/to Intra-Co : : : : : : : : Extraordinary Items : : : : : : : : Cap Exp - Tangible : -156,734.00: -976,141.00 : -848,888.00 : : : : 88 CTY THUONG MAI PHU NHUAN CASH FLOW Page: : Cap Exp - Intangible : -21,072.00: -40,908.00 : -72,137.00 : : : : : LT Investments : : 4,180.00 : 3,182.00 : : : : : : : : : : : : Discretionary Trans : -177,806.00: -1,012,869.00 : -917,843.00 : : : : : : : : : : : : : : FINANCING SUPLUS : -8,783.00: -1,454,349.00 : -2,015,956.00 : -1,426,423.00 : -2,659,440.00 : -4,137,817.00 : : : : : : : : : : Change in ST Debt : : 745,427.00 : 1,300,888.00 : 1,502,379.00 : 2,713,181.00 : 4,222,834.00 : : Change in LT Debt : 146,718.00: 462,139.00 : 383,862.00 : : : : : Change in Equity : : 133,500.00 : 364,500.00 : : : : : Minority Interest IS : : : : : : : : Minority Interest BS : : : : : : : : : : : : : : : : Total External Fin : 146,718.00: 1,341,066.00 : 2,049,250.00 : 1,502,379.00 : 2,713,181.00 : 4,222,834.00 : : : : : : : : : : Cash After Financing : 137,935.00: -113,283.00 : 33,294.00 : 75,956.00 : 53,741.00 : 85,017.00 : : Actual Change inCash : : -85,119.00 : -4,209.00 : 75,956.00 : 53,741.00 : 85,017.00 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : Net Income + Depre : 169,023.00: 565,968.00 : -196,812.00 : 446,355.00 : 701,628.00 : 1,046,573.00 : : : : : : : : : : Change in B/S Accts : : : : : : : : Other Current Assets : : -61,534.00 : -109,679.00 : -112,739.00 : -178,353.00 : -282,153.00 : : Other Non-Cur Assets : : -10,327.00 : 18,617.00 : -28,478.00 : -45,052.00 : -71,272.00 : : Other Current Liab : : 51,828.00 : 95,939.00 : 40,604.00 : 64,235.00 : 101,619.00 : : Other LT Liabilities : : : : : : : : Provisions : : : : : : : : : : : : : : : : Change in Other B/S : : -20,033.00 : 4,877.00 : -100,613.00 : -159,170.00 : -251,806.00 : : : : : : : : : : : : : : : : : : Other Income/Expense : : : : : : : 89 : Total Interest Incom : : : : : : : : Other Income : 111,201.00: 178,101.00 : 177,504.00 : 246,161.00 : 389,425.00 : 616,067.00 : : Other Expense : : : : : : : : : : : : : : : : Other Income/Expense : 111,201.00: 178,101.00 : 177,504.00 : 246,161.00 : 389,425.00 : 616,067.00 : : : : : : : : : : : : : : : : : : Due from/to Intra-co : : : : : : : : Assets : : : : : : : : Due from Affil-Cur : : : : : : : : Due from Aff-Non Cur : : : : : : : : Liabilities : : : : : : : : Due to Affil-Cur : : : : : : : : Due to Parents/Subs : : : : : : : : Due to Affiliate-LT : : : : : : : : Due to Related Co : : : : : : : : : : : : : : : : Due from/to Intra-Co : : : : : : : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : 90 CTY THUONG MAI PHU NHUAN FINANCIAL RATIO Page: : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : :History :History :History :Forecast :Forecast :Forecast : : FINANCIAL RATIOS :31/12/92 :31/12/93 :31/12/94 :31/12/95 :30/12/96 :30/12/97 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : : GROWTH RATIOS : : : : : : : : : : : : : : : : Net Sales Growth : : 94.17 : 28.88 : 58.19 : 58.19 : 58.19 : : Net Income Growth : : 3,133.02 : -347.58 : 108.29 : 488.08 : 157.28 : : Total Assets Growth : : 31.79 : 24.64 : 50.66 : 46.99 : 52.70 : : Total Liab Growth : : 26.99 : 34.46 : 54.75 : 47.30 : 51.18 : : Net Worth Growth : : 66.42 : -29.44 : 7.74 : 42.25 : 76.43 : : : : : : : : : : PROFITABILITY RATIOS.: : : : : : : : : : : : : : : : Gross Margin : 34.83: 36.20 : 35.12 : 35.45 : 35.45 : 35.45 : : SG & A : 32.69: 31.39 : 34.44 : 33.03 : 33.03 : 33.03 : : Cushion (Gmarg-SG&A) : 2.14: 4.80 : 0.68 : 2.42 : 2.42 : 2.42 : : Depreciation, Amort : 1.76: 1.52 : 1.83 : 0.98 : 0.60 : 0.21 : : Opera Profit Margin : 0.37: 3.27 : -1.15 : 1.44 : 1.82 : 2.20 : : Interest Expense : 1.96: 2.61 : 3.28 : 1.82 : 1.57 : 1.41 : : Operating Margin : -1.58: 0.66 : -4.44 : -0.38 : 0.24 : 0.79 : : Net Margin : -0.08: 1.36 : -2.61 : 0.13 : 0.50 : 0.82 : : Return on Avg Assets : -0.17: 4.64 : -9.01 : 0.53 : 2.12 : 3.64 : : Return on Avg Equity : -1.45: 33.21 : -77.19 : 7.45 : 34.88 : 55.29 : : : : : : : : : : COVERAGE RATIOS : : : : : : : : : : : : : : : : Interest Coverage : 1.85: 2.10 : 0.76 : 1.73 : 1.96 : 2.10 : 91 : Net Incom+Depr/CMLTD : 1.15: 3.85 : -1.49 : 1.95 : 3.07 : 4.58 : : Interest/AvgInt Debt : 0.08: 0.15 : 0.16 : 0.14 : 0.21 : 0.31 : : : : : : : : : : ACTIVITY RATIOS : : : : : : : : : : : : : : : : Avg Receivables Days : 74.69: 43.74 : 37.82 : 36.13 : 39.57 : 39.57 : : Avg Inventory Days : 88.62: 52.19 : 54.67 : 54.20 : 54.63 : 54.63 : : Avg Payables in Days : 58.48: 29.45 : 23.81 : 24.30 : 26.63 : 26.63 : : Receivables in Days : 74.69: 49.02 : 37.60 : 48.49 : 48.49 : 48.49 : : Inventory in Days : 88.62: 57.77 : 65.26 : 66.95 : 66.95 : 66.95 : : Payables in Days(YE) : 89.75: 45.12 : 38.97 : 50.56 : 50.56 : 50.56 : : Total Asset/Net Sale : 0.49: 0.33 : 0.32 : 0.30 : 0.28 : 0.27 : : : : : : : : : : LIQUIDITY RATIOS : : : : : : : : : : : : : : : : Working Capital : -3,263.00: 31,241.00 : -582,113.00 : -392,411.00 : 35,990.00 : 783,116.00 : : Quick Ratio : 0.54: 0.55 : 0.39 : 0.49 : 0.51 : 0.53 : : Current Ratio : 0.99: 1.00 : 0.91 : 0.96 : 1.00 : 1.03 : : Sales/Net WorkingCap : -3,090.68: 626.83 : -43.35 : -101.75 : 1,755.12 : 127.60 : : : : : : : : : : LEVERAGE RATIOS : : : : : : : : : : : : : : : : Total Liab/Net Worth : 7.21: 5.50 : 10.49 : 15.07 : 15.60 : 13.37 : : T Sr Debt/Nworth&SbD : 7.21: 5.50 : 10.49 : 15.07 : 15.60 : 13.37 : 92 CTY THUONG MAI PHU NHUAN FINANCIAL RATIO Page: : Borrow Fund/TNW+SubD : 4.07: 3.50 : 7.16 : 8.32 : 8.14 : 6.70 : : LT Debt/Net Fix Asse : 0.67: 0.60 : 0.56 : 0.55 : 0.54 : 0.48 : : : : : : : : : : CASH POSITION : : : : : : : : : : : : : : : : Cash Margin : 34.83: 30.99 : 32.28 : 28.73 : 29.50 : 29.50 : : Cash Coverage : -1.85: -0.32 : 0.14 : 0.49 : 1.17 : 1.52 : : Net Cash Income : 169,023.00: -294,762.00 : -966,042.00 : -1,198,248.00 : -2,431,265.00 : -3,909,642.00 : : Net Income+Deprciatn : 169,023.00: 565,968.00 : -196,812.00 : 446,355.00 : 701,628.00 : 1,046,573.00 : : : : : : : : : : SUS GROWTH&BANKRUPTCY: : : : : : : : : : : : : : : : Sustainanle Growth : -0.00: 0.07 : -0.15 : 0.00 : 0.02 : 0.03 : : Z Score : 2.28: 3.60 : 2.97 : 3.43 : 3.79 : 4.04 : : ====================== : ============ : ============ : ============ : ============ : ============ : ============ : 93 ... and user interface 22 Chapter Design and methodology 4.1 Methodology 4.1.1 Research Methodology Data relevant to the research will be collected from both primary and secondary sources This requires... analysis in evaluating loan applicants and to improve the quality of the evaluation 1.3 Scope of the research The system is designed to analyze commercial loans for industrial or retail borrowers The... examined, the loan officers accumulate loan covenants, or restrictions, which become part of the final loan agreement Gilliam (1990) describes the financial analysis procedure for the estimation