Expert credit recommendation method and system

An expert floorplan credit recommendation method and system, comprising a database and stored program for arriving at a credit recommendation by processing data on a computer by means of a decision matrix tree that emulates the thought processes of credit experts.

BACKGROUND AND SUMMARY OF THE INVENTION 
Expert systems for making credit recommendations are generally based on 
numeric scoring and do not particularly reflect the decision process of a 
credit expert. This invention, on the other hand, comprises an expert 
credit method and system which use a decision matrix tree to emulate the 
decision process of credit experts in analyzing a credit applicant and 
recommending whether to extend the credit sought. 
The method used is to construct and implement, for example, in a 
software-controlled general purpose digital computer, a decision matrix 
tree. The tree is constructed so as to emulate the thought processes of 
human credit experts. Each matrix in the tree compares two pertinent 
characteristics and in turn represents, and provides as output, another 
pertinent characteristic which is then available as input to a subsequent 
matrix in the tree. In each matrix, each of the two characteristics being 
compared is depicted on one axis of the matrix. Each of the two axes of 
the matrix has provision for a number of different values of the 
characteristic depicted on that axis. Each box in the matrix represents a 
different combination of values of those two input characteristics. For 
each of these possible combinations, the matrix determines the value of 
the characteristic represented by the matrix itself. Thus, each matrix 
accepts as input two values, one on each axis. Each matrix delivers as 
output a single value, determined by the value of the matrix box defined 
by the two values on the axes. 
Each input value to a matrix emanates from one of two sources. One or both 
input values for a matrix may be output from preceding matrixes in the 
matrix decision tree. Other input values are supplied from the "knowledge 
base," which is a database resident in or accessible to the computer and 
which has been supplied with information derived from credit experts. 
The form of the decision matrix tree, as well as the identity of the 
characteristics compared in the various matrixes, the output 
characteristic from each matrix, the order of the matrices, and the 
designations of values in each box of each matrix are all the product of 
extensive contribution by credit experts. 
A preferred embodiment of the invention employs a general purpose computer 
under the control of rule-based expert system artificial intelligence 
software that emulates the decision process of experts in the field of 
floorplan inventory lending. Industry characteristics and lending 
practices unique to floorplan lending play a fundamental role in the 
underlying decision matrix tree and characteristics ("criteria") used in 
this method and system. As opposed to a numeric scoring method or system, 
this invention actually compares various factors used by experts in making 
decisions, to emulate more closely the experts' decision process in 
reaching a recommendation on whether to extend credit. 
In addition to recommending a determination on whether to extend the credit 
sought, the method and system of the invention may provide output, usually 
in the form of computer print-out or computer screen displays, on matters 
supporting the ultimate recommendation. For example, the method and system 
may provide as output a summary of the criteria input, and the 
recommendation, in a prescribed format. Alternatively or in addition, the 
method and system may provide as output a sequence of observations 
regarding criteria perceived as positive, and a sequence of observations 
regarding criteria perceived as negative. The output may include 
observations made directly by the user (and input to the computer) as well 
as those generated by the invention. The output from the invention may 
also include detailed listings and analyses of various criteria deemed 
significant to the recommendation (e.g., information relating to financial 
statements). This aspect of the invention is of assistance in making 
manifest the rationale behind the recommendation. It is also useful in 
training personnel in the credit determination process. 
The expert method and system is designed to aid and train floorplan credit 
analysts in making credit decisions while ensuring consistency in credit 
policy. The system is designed for use on personal computers. The 
preferred embodiment operates in connection with IBM.RTM. and 
IBM-compatible personal computers having at least a 640K byte memory, a 20 
megabyte disk drive and a printer. Analysts input information into the 
system, which provides them with results of an extensive analysis both on 
screen and through several printed reports. 
The method and system involve two interacting components. The first 
component is a database management program which also comprises a user 
interface (generally a screen display) that prompts an individual analyst 
for all information pertinent to a credit decision. In a preferred 
embodiment of the invention, ADOX.RTM. relational data base software 
(version 3.01), marketed by Borland International, is used as the database 
manager. The information input by the user in response to prompts includes 
the type of credit line requested, the level of management experience, 
types of collateral and inventory, type of business or industry, credit 
history and financial information. Mathematical. calculations are 
performed on certain of this information by stored software which 
generates financial and other ratios pertinent to a decision. This data is 
then maintained in the database for retrieval at any time. The software 
may also provide for the electronic transfer of client data to other 
locations, e.g., regional offices. 
The second component of the method and system is a computer program written 
in a language suited to artificial intelligence expert systems, such as 
Prolog Version 5.x, marketed by Arity Corporation. This program is the 
"brain" of the method and system, analyzing client information that it 
obtains from the database manager. The analysis performed by the program 
emulates the reasoning patterns of floorplan credit experts by using a 
decision matrix tree, comprising a plurality of decision matrices arranged 
in branches. To arrive at the final recommendation to grant or deny 
credit, or further review the matter, the program must perform each of the 
operations in each branch of the decision tree. 
At the initial stage, the process and system compare each of a number of 
specific criteria to levels or ranges characterized in a particular manner 
by credit experts for the specific business or industry. Some of the 
relevant criteria are: direct payment experience of the user with the 
borrower, payment experience of other creditors, industry experience of 
the borrower's management, personal character of management, strength of 
industry, strength of product sold, strength of suppliers, type and 
strength of collateral, type and strength of documentation, sales level 
and trend, gross margins and trend, net margins and trend, leverage 
position and trend, and liquidity position and trend. 
With the information obtained from this analysis, the process proceeds 
through several series of matrices arranged in branches whereby the 
strengths and weaknesses of each criterion category are compared with 
strengths and weaknesses of one or more other criterion categories. The 
result of each such comparison is then used in another comparison in a 
subsequent decision. Consideration is given to the significance of each 
category relative to the overall decision by its position in the tree. 
Those categories deemed more important in the final decision are dealt 
with last. This analysis process is unlike credit scoring systems because 
numeric weights are not used. Instead, the program actually considers the 
strength of one criterion in relation to another criterion, making many 
such decisions throughout the analysis process. For example, the program 
may compare components of a balance sheet and reach its decision based on 
the overall strength of the balance sheet alone. At the same level, the 
program is comparing other components to decide the strength of the 
balance sheet trend, income statement and the income statement trend. The 
analysis continues by then comparing these new decisions to one another. 
This method is duplicated for all aspects of the decision process. 
As the analysis progresses, and based on the criteria and results of 
criteria comparisons, the program constructs comments unique to each 
client indicating the status of each important element of floorplan 
lending. Upon completion these comments are transferred to the database 
management system for storage and retrieval. 
Output from the method and system may include an "Executive Summary" 
designed to provide a quick look at the condition of the most significant 
factors, positive and negative, which pertain to the credit decision. 
Comments presented in this summary are generated through the analysis 
process and are unique to each client. The output from the method and 
system may also include a standard report indicating the result of each 
major step in the analysis and a summary of data input. Additionally, the 
method and system may provide standardized financial spreadsheets which 
include financial ratios relating to inventory financing. 
The output from this method and system is used as a tool in making credit 
decisions and can also be used for training credit analysts in the proper 
requirements of floorplan lending. Use of the method and system helps a 
lending organization ensure a uniform standard in the credit evaluation 
process and helps to eliminate arbitrary considerations from the decision. 
It also provides a means for better and more thorough supervision of the 
credit decision processes of subordinates. The system further highlights 
key issues, both positive and negative, and by inference indicates those 
areas that need to be addressed in order to make a transaction more 
favorable.

DETAILED DESCRIPTION OF THE INVENTION 
The specific floorplan expert credit method and system embodiment of the 
invention works essentially as follows. A PC-based program written in 
Paradox application language guides the credit analyst user through the 
data input phase with screen prompts. Necessary calculations may also be 
performed by this program on the data. The input data and the results of 
those calculations are then stored in the database, which in this case is 
the relational database, Paradox. The Paradox application language program 
then stores the data and, upon command by the user, formats the required 
data in a separate ASCII file. That program then transfers control to the 
logic or artificial intelligence expert program, which in this embodiment 
is written in Prolog. The logic then retrieves from the ASCII file the 
data for analysis, and calls on its knowledge base for the specific 
product line-dependent standard data, which is updated for economic or 
policy changes periodically, e.g., semi-annually. With the standard data, 
the input data and the modified input data the logic then performs its 
analysis according to the decision matrix tree depicted in FIGS. 1-4. 
After the analysis the logic arrives at a result, i.e., a credit 
recommendation, and also generates unique comments according to each 
analysis. The results are then processed into a form which is imported 
into the database where it is stored and may be displayed or printed. 
Referring to FIG. 1, the expert system computer program is comprised of a 
decision matrix tree, which is organized into branches. Each branch 
comprises one or more decision matrices positioned in sequence according 
to the weight or influence on the final decision of the criteria analyzed 
in that matrix. 
In the invention's preferred embodiment, which concerns floorplan credit 
financing, the decision matrix tree is organized into two major branches, 
the financial branch FIGS. 2A and 2B and the dealer branch FIGS. 3 and 4. 
The financial branch in turn is further differentiated into an interim 
financial branch and a fiscal-year-end financial branch, while the dealer 
branch is differentiated into a character branch FIG. 4 and a collateral 
branch FIG. 3. 
In the preferred embodiment each decision matrix in is depicted as a 
5.times.5 grid with an "x" and "y" axis. Each axis presents one of the two 
criteria compared in that decision matrix. Each axis provides space for 
five ratings for the criterion depicted: "good", "satisfactory", 
"marginal", "poor" and "unknown". The predetermined values for each rating 
for each criterion or factor is stored in the program's knowledge base. 
Each box in the grid is designated by one of the same five ratings, 
thereby assigning that rating to a situation characterized as having the 
criteria ratings specified on the "x" and "y" axes corresponding to that 
box. I.e., once the value of the two characteristics being compared is 
determined, a box in the grid is defined, and the value of that box is the 
value assigned to the output characteristic. 
The financial branch of the decision tree, depicted in FIGS. 2A and 2B, is 
comprised of decision matrices 1-13. In the preferred embodiment of this 
system the financial branch divides into the interim financial sub-branch, 
which comprises the decision matrices 1-6, and the fiscal-year-end ("FYE") 
sub-branch, comprising matrices 7--12. These two separate sub-branches 
have similar form. However, the FYE sub-branch of the decision matrix is 
constructed by using data from fiscal year end financial statements, 
whereas the interim sub-branch is constructed by using data from interim 
financial statements. The operations on the data by the expert program, 
however, are the same for both sub-branches. In order to simplify this 
description the financial branch operation will be described generically, 
with the understanding that the sequence and operations apply both to the 
interim and FYE branches. 
Balance Sheet decision matrix 1 (for FYE, or 7 for Interim) compares 
Liquidity (or Interim Liquidity) for each specific product line with the 
Leverage (or Interim Leverage). The term "liquidity" is defined as the 
ratio of current assets to current liabilities. The value for the 
criterion "Liquidity" is obtained by comparing the specific dealer's 
liquidity for the product line in question with standard liquidity ranges 
stored in the logic's knowledge base, to obtain a value of "good," 
"satisfactory," "marginal," "poor" or "unknown." The term "leverage" is 
defined as debt to tangible net worth. The value of the criterion leverage 
is determined by comparing the dealer's leverage with the standard 
leverage ranges stored in logic's knowledge base. When values have been 
determined for Liquidity and Leverage, the Balance Sheet matrix (which is 
stored in the knowledge base) assigns a value to the criterion Balance 
Sheet. 
The value resulting from the Balance Sheet matrix 1 (or 7) is then further 
processed in the Balance Sheet and Trend matrix 2 (or 8), where the 
criterion Balance Sheet is compared to the criterion Leverage Trend. The 
term "leverage trend" is defined as the change in leverage. Logic obtains 
a value for the criterion Leverage Trend by comparing the leverage trend 
of this specific dealer with the leverage trend ranges stored in the 
knowledge base. The resulting rating of "good", "satisfactory", 
"marginal", "poor" or unknown is then compared to the Balance Sheet value 
from matrix 1 (or 7). The resulting Balance Sheet Trend value of matrix 2 
(or 8) is then used in a comparison in decision matrix 3 (or 9), the 
Financial matrix, where it is compared with the value for Income Statement 
and Trend, which results from matrix 6 (or 12). 
To arrive at a decision from matrix 6 (or 12) the logic program must first 
start with processing a decision from matrices 4 and 5 (or 10 and 11). The 
Income Trend decision matrix 4 (or 10) consists of a comparison of Sales 
Trend, to percent change in Net Profit to Sales ratio. The Sales Trend 
criterion is specific to each different product line, and the value is 
based on ranges of percentage increases calculated or expected in this 
specific product line. These values are stored in the logic's standard 
knowledge base. The percent change in the net profit to sales ratio is 
calculated based on the dealer's net profit before tax. The specific value 
of the criterion Net Profit to Sales for the dealer is assigned based on 
the standard ranges stored in the logic's knowledge base. This value is 
then compared in matrix 4 (or 10) to Sales Trend to give the value of 
Income Trend Criterion. The Income Trend criterion of matrix 4 (or 10) is 
then used, as already described, in a comparison in matrix 6 (or 12) with 
the Income Statement criterion to determine Income Statement and Trend. 
The Income Statement decision matrix 5 (or 11) compares the criterion of 
Gross Profit on Sales, to the criterion of Net Profit on Sales. The ratio 
of gross profit on sales is calculated for the distinct product line, and 
compared to the standard ranges for that product line stored in the 
logic's knowledge base, to assign a value to the criterion Gross Profit on 
Sales. Once a value is assigned, it is inserted into decision matrix 5 (or 
11) and compared to the Net Profit on Sales criterion for the dealer. Net 
profit on sales is a percentage ratio calculated for the specific dealer 
and compared to the standard ranges for the specific product line stored 
in the logic's knowledge base to assign a value to the Net Profit on Sales 
criterion. The value of the Income Statement decision matrix 5 (or 11) is 
then used in a comparison with the Income Trend criterion from matrix 4 
(or 10) in decision matrix 6 (or 12), to determine the Income Statement 
and Trend criterion. The criterion derived from the Income Statement and 
Trend matrix 6 (or 12) is then used in the Financial decision matrix 3 (or 
9), which compares it against the Balance Sheet and Trend criterion in 
decision matrix 2 (or 8). 
As discussed previously, the decision matrices 1, 2, 3, 4, 5 and 6 for 
fiscal year data are in form identical to decision matrices, 7, 8, 9, 10, 
11 and 12 for interim data. The values of the Interim Financial matrix 9 
and the Fiscal Year End Financial matrix 3 are used to make the comparison 
in the Financial matrix 13 to arrive at the Financial criterion. The value 
of Financial matrix 13 is then further processed by the logic in 
connection with the value determined by the dealer branch, as described 
below. 
With respect to the Dealer branch, the decision matrix tree is divided into 
the Character sub-branch FIG. 4 and the Collateral sub-branch FIG. 3. The 
Character sub-branch is comprised of matrices 14, 15, and 21-24. Matrix 
21, Owner Experience, compares the criterion of Management Experience at 
dealer location to the criterion of Additional Management Experience. 
Logic compares the number of years of management experience at the 
particular dealer location to the ranges stored in its knowledge base, 
assigning the Management Experience criterion a value "good," 
"satisfactory," "marginal," "poor" or "unknown". The same function is also 
performed on the Additional Management Experience criterion, which is a 
measure of how many additional years of experience current dealer 
management has had in a similar industry. Both these values are evaluated 
by the logic for this dealer from the standard ranges in the logic 
knowledge base, and inserted into the matrix. Logic then evaluates matrix 
21, arriving at the criterion of Owner Experience. The resulting value is 
then processed in matrix 22, Business Experience. 
The Business Experience matrix 22 compare the criterion Company Experience 
to the criterion Owner Experience. Company Experience is a function of the 
number of years the company has been in business. The specific term for 
the dealer in question is compared by the logic to the standard ranges in 
the logic's knowledge base, and the value assigned is inserted into matrix 
22. After evaluation, the value according the Business Experience matrix 
is further processed in matrix 23, Business Character. 
In the Business Character matrix the Business Experience decision from 
matrix 22 is compared to the Business Pay Habits criterion derived from 
matrix 15. To arrive at matrix 15, the logic must first process the 
Lender's Experience decision from matrix 14. Matrix 14 compares the Pay As 
Sold ("PAS") criterion, which is defined as the percentage of monthly 
collections made at the lender's audit of the dealer, to the Schedule Pay 
Plan ("SPP") experience of the lender with this dealer, calculated in 
terms of average days late on scheduled payment. The PAS and SPP criteria 
values for the specific dealer are retrieved by the logic from the 
database and compared to the standard ranges stored in the logic's 
knowledge base. These values are inserted into the matrix 14, Lender 
Experience, and a decision value is calculated. 
The Business Pay Habit matrix 15 compares the Lender Experience from matrix 
14 to the criterion of Other Creditor Experience. Other Creditor 
Experience is established by credit ratings collected by the lender from 
other creditors, usually through telephone solicitation by the lender. The 
Other Creditor Experience value is determined by the user and input into 
the logic's knowledge base. The value is retrieved from the knowledge base 
and compared to Lender Experience from matrix 14. The resulting Business 
Pay Habit criterion is then further processed in matrix 23 Business 
Character, where it is compared to the Business Experience criterion from 
matrix 22. In matrix 23, logic calculates a value and this criterion of 
Business Character is further processed in the Character matrix 24. 
Character matrix 24 compares the criterion of Business Character to the 
criterion of Personal Credit Bureau Reports. The Personal Credit Bureau 
Reports criterion concerns the personal credit of any guarantors who will 
act as guarantors of the loan. Personal Credit Bureau Reports are usually 
required when, and only when a personal guaranty is held as additional 
collateral on the loan. The value for Personal Credit Bureau Reports is 
input into the database and retrieved by the logic. The logic then 
calculates a value according to matrix 24 and this criterion is further 
processed in the Dealer Rating matrix 20 where it is compared to the value 
of the Collateral criterion calculated in the collateral branch of the 
matrix FIG. 3, as described below. 
The Collateral branch of the matrix comprises matrices 16-19. The 
Collateral branch begins with matrix 16, Manufacturer Strength, which 
compares the criteria of Manufacturer Buy Back Agreement to Manufacturers' 
Financial Rating. These criteria concern the manufacturers of the goods on 
the basis of which the floorplan lender is lending to the dealer. The user 
determines Manufacturer Financial Rating criteria for each manufacturer of 
goods sold, and an overall value for this criterion, and inputs that value 
into the database. The value is inserted into matrix 16. The Manufacturer 
Buy Back criterion concerns whether the lender has a repurchase agreement 
with the manufacturer, and the type of repurchase agreement. The logic 
calculates a value for the Manufacturing Strength criterion based on 
matrix 16 and this criterion is further processed in matrix 18, 
Manufacturing Collateral. In matrix 18, the Manufacturing Strength is 
compared to the Product Line criterion. The Product Line criterion is a 
function of the percentage of the dealer's credit line to be used on the 
manufacturer's products. This percentage, calculated for the individual 
dealer and input to the database is compared to the ranges stored in the 
logic's knowledge base and a value is inserted into matrix 18. 
The resulting decision value is further processed in matrix 19, Collateral. 
Matrix 19 compares Manufacturing Collateral with the criterion of Dealer 
Collateral, which is derived from decision matrix 17. Matrix 17, Dealer 
Collateral, compares the criterion of Soft Collateral with that of 
Document Strength. Soft Collateral is defined as the personal tangible net 
worth of the guarantor, as a percentage of the credit line. Based on input 
to the database, the logic calculates a value for the specific guarantor, 
compares this value to the standard ranges in its knowledge base, and then 
assigns a value for the dealer, which is inserted into matrix 17. Document 
Strength is a measure of the strength of any lien that is placed upon 
inventory. The user inputs into the database the nature of the lien, and 
the database converts this information into a Document Strength criterion 
value. 
At this point, both the Financial branch and the Dealer branch have been 
completely determined, as a value exists for the Financial criterion in 
matrix 13, and a value exists for the Dealer Rating criterion in matrix 
20. These two values are input to the Overall Rating criterion in matrix 
25, at which point the expert system has arrived at a value that can be 
used for making a determination. However, in a preferred embodiment of the 
invention further operations are performed in matrices 26 and 27 to give 
more weight to certain factors toward a recommendation decision. The value 
determined from the Overal Rating matrix 25 is transmitted to the Final 
Rating matrix 26, where it is compared with Net Hard Collateral. Net Hard 
Collateral is the portion of the credit line covered by letters of credit, 
certificates of deposit, and half the value of any mortgages used as 
collateral. The dollar amount of hard collateral held on the dealer is 
input into the database. The Paradox application language program 
calculates Net Hard Collateral as the percent of hard collateral to the 
proposed credit line discounted for any "SPP deficit." SPP deficit is 
defined as one less than the ratio of outstanding turn (if known to the 
lender) or program turn (otherwise), divided by inventory turn. Logic 
computes a numerical figure for Net Hard Collateral and compares it to 
predetermined stored ranges, whereupon a value is assigned. This value for 
Net Hard Collateral is compared with the Overall Rating criterion, to 
yield a value for the Final Rating criterion. This value for Final Rating 
is then passed on matrix 27, Recommendation. 
The Recommendation matrix is unlike any of the other matrices. Although the 
"y" axis (Final Rating) has the same five values as do the other matrices, 
the "x" axis has the following values: credit line is more than 300% of 
average inventory; net SPP deficit is greater than 10%; current ownership 
exceeds ten years; dealer has been with lender for more than five years; 
and Otherwise. Further, the values in each of the 25 boxes comprising the 
matrix are "Recommend," "Review," and "Not Recommend." This is the final 
matrix and it embodies the ultimate result of the system. The 
recommendation is then displayed on screen or by some other output means 
to the operator who uses it in making his or her credit decision. Further, 
supporting output may be printed, e.g., in the form of a summary of input 
data, a sequence of observations generated by the method and system 
regarding positive and negative criteria, financial data, or an Executive 
Summary. 
The criteria analyzed by the system and compared in the matrices depicted 
in FIGS. 1, 2, 3 and 4 are listed in Table 1. 
TABLE 1 
______________________________________ 
PRODUCT MAR- 
CRITERIA LINE GOOD SAT GINAL 
______________________________________ 
pas experience 
-- 35 50 55 
spp experience 
-- 3 5 7 
company -- 5 3 1 
experience 
additional mgt 
-- 10 5 3 
experience 
management -- 4 2 0.5 
experience 
personal -- 1 2 3 
character 
other creditor 
-- 1 2 3 
experience 
product line 
-- 75 50 40 
document strength 
-- 1 2 3 
manufacturer 
-- 1 2 3 
buyback 
manufacturer 
-- 1 2 3 
financial rating 
hard collateral 
-- 70.0 35.0 10.0 
soft collateral 
-- 50.0 0.0 -50.0 
gross profit to 
mis 25 20 17 
sales 
gross profit to 
tv 24 20 18 
sales 
gross profit to 
marine 23 18 15 
sales 
gross profit to 
mcycle 20 18 16 
sales 
gross profit to 
rv 19 15 13 
sales 
gross profit to 
mob home 18 15 13 
sales 
gross profit to 
ind eq 23 20 18 
sales 
gross profit to 
office 24 20 17 
sales 
gross profit to 
keyboard 33 27 22 
sales 
gross profit to 
unknown 25 20 18 
sales 
interim gp sa 
mis 25 20 17 
interim gp sa 
tv 24 20 18 
interim gp sa 
marine 23 18 15 
interim gp sa 
mcycle 20 18 16 
interim gp sa 
rv 19 15 13 
interim gp sa 
mob home 18 15 13 
interim gp sa 
ind eq 23 20 18 
interim gp sa 
office 24 20 17 
interim gp sa 
keyboard 33 27 22 
interim gp sa 
unknown 25 20 18 
net profit to sales 
mis 2.0 0.8 -0.2 
net profit to sales 
marine 2.0 0.8 -0.2 
net profit to sales 
tv 1.5 0.8 -0.2 
net profit to sales 
mcycle 1.4 1.0 -0.2 
net profit to sales 
rv 1.0 0.5 -0.2 
net profit to sales 
mob home 1.0 0.5 -0.2 
net profit to sales 
ind eq 2.2 1.0 -0.2 
net profit to sales 
office 1.5 0.8 -0.2 
net profit to sales 
keyboard 2.1 1.5 -0.2 
net profit to sales 
unknown 1.5 0.5 -0.2 
interim np sa 
mis 2.0 0.8 -0.2 
interim np sa 
marine 2.0 0.5 -0.2 
interim np sa 
tv 1.5 0.8 -0.2 
interim np sa 
mcycle 1.4 1.0 -0.2 
interim np sa 
rv 1.0 0.5 -0.2 
interim np sa 
mob home 1.0 0.5 -0.2 
interim np sa 
ind eq 2.2 1.0 -0.2 
interim np sa 
office 1.5 0.8 -0.2 
interim np sa 
keyboard 2.1 1.5 -0.2 
interim np sa 
unknown 1.5 0.5 -0.2 
sales trend 
mis 10 5 0 
sales trend 
marine 5 0 -5 
sales trend 
mcycle 0 -5 -7 
sales trend 
tv 5 0 -5 
sales trend 
rv 2 -3 -5 
sales trend 
mob home 4 0 -5 
sales trend 
ind eq 4 0 -5 
sales trend 
office 8 4 0 
sales trend 
keyboard 3 0 -3 
sales trend 
unknown 5 0 -5 
interim sales trend 
mis 10 5 0 
interim sales trend 
marine 5 0 -5 
interim sales trend 
mcycle 0 -5 -7 
interim sales trend 
tv 5 2 -3 
interim sales trend 
rv 2 -3 -5 
interim sales trend 
mob home 4 0 -5 
interim sales trend 
ind eq 4 0 -5 
interim sales trend 
office 8 4 0 
interim sales trend 
keyboard 3 0 -3 
interim sales trend 
unknown 5 0 -5 
net profit to sales 
-- 0.3 -0.3 -0.5 
trend 
interim mp sa 
-- 0.3 -0.3 -0.5 
trend 
leverage trend 
-- 1.2 1.5 2.0 
interim leverage 
-- 1.2 1.5 2.0 
trend 
leverage -- 4.0 6.0 12.0 
interim leverage 
-- 4.0 6.0 12.0 
liquidity mis 1.4 1.1 1.0 
liquidity marine 1.3 1.1 1.0 
liquidity mcycle 1.2 1.1 1.0 
liquidity tv 1.2 1.1 1.0 
liquidity rv 1.3 1.1 1.0 
liquidity mob home 1.2 1.0 0.9 
liquidity ind eq 1.2 1.0 0.9 
liquidity office 1.4 1.1 1.0 
liquidity keyboard 1.3 1.0 0.9 
liquidity unknown 1.3 1.1 1.0 
interim liquidity 
mis 1.4 1.1 1.0 
interim liquidity 
marine 1.3 1.1 1.0 
interim liquidity 
mcycle 1.2 1.1 1.0 
interim liquidity 
tv 1.2 1.1 1.0 
interim liquidity 
rv 1.3 1.1 1.0 
interim liquidity 
mob home 1.2 1.0 0.9 
interim liquidity 
ind eq 1.2 1.0 0.9 
interim liquidity 
office 1.4 1.1 0.9 
interim liquidity 
keyboard 1.3 1.0 0.9 
interim liquidity 
unknown 1.3 1.1 1.0 
______________________________________