JP2008046835A - Creditability calculation system and calculation program for enterprise - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a technology for calculating a value showing the creditability of an enterprise which has not issued any public bond. <P>SOLUTION: This creditability calculation system is provided with: an input information registration part 14; a basic information storage part 16; a regression model generation part 18; a regression model storage part 20; a loan information storage part 14; a spread calculation part 24; a spread storage part 26; a default result storage part 28; a regression formula calculation part 30; a regression formula storage part 32; a default probability calculation part 34; a calculation result storage part 36; and a calculation result output part 38 or the like. This creditability calculation system is configured to derive the regression model of every point of time, and to calculate the spread of the past point of time of each debt by using the regression model of the past point of time, the residual period of each debt and the financial data of each enterprise, and to classify it into 10 groups according to the value of the spread of each debt, and to calculate the default probability of the group unit, and to derive the regression formula based on the mean spread and default probability of each group, and to calculate the spread of the current point of time of the debt by using the regression model of the current point of time, the residual period of the debt and the financial data for calculating the default probability of the enterprise. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a company's creditworthiness calculation system and calculation program, and more particularly to a technique for calculating the creditworthiness (default of debt) of a company that has not issued publicly offered bonds based on various financial data.

Financial institutions such as banks and institutional investors such as insurance companies need to know in advance the default probability of the borrower or investee company in order to diversify the risk. Based on this, the probability of bankruptcy has been roughly grasped.
However, credit information from rating agencies has the following problems.
(1) Renewal is slow because it takes time to interview the target company.
(2) Evaluation criteria and information sources are unclear and poor in objectivity. As a result, ratings are often divided among rating agencies.
(3) The number of target companies by one rating company is limited to several hundred.

  On the other hand, in Patent Document 1, the price of a bond, which is a borrowing document of a company, is credited with the price and attributes of the bond on the assumption that a market evaluation regarding the probability of bankruptcy of the issuing company is incorporated. A technique for calculating a company's creditworthiness by comparing with the price and attributes of a government bond whose risk is almost zero is disclosed.

JP 2001-125953 A

As claimed by the inventor in Patent Document 1, there remains a question as to whether the default recovery rate information is incorporated in bond prices, but at least traders trade in bond transactions based on the credit risk of the issuer. Since the price is set, the idea of estimating creditworthiness based on bond prices can be supported.
In this way, calculating creditworthiness based on public information such as the market price of bonds provides objective judgment results compared to credit information provided by traditional rating agencies, and provides credit for a wider range of issuers. The force can be calculated.

However, the number of companies issuing publicly offered bonds is limited in the first place, and the fact is that there is no objective measure for creditworthiness such as market interest rates for the majority of companies. For this reason, the technique of Patent Document 1 has a problem that it cannot be applied to the purpose of estimating the creditworthiness of companies that have not issued publicly offered bonds. There is also room for reconsideration of the fact that the bankruptcy data of actual results is not used at all, despite the intention of estimating the creditworthiness of the company.
In recent years, corporate debts, such as corporate loans, that do not have market interest rates are attracting attention as transactions, and there is a need for a corporate loan market interest rate estimation method (pricing) and a consistent credit risk management method. Therefore, the establishment of technology to objectively calculate the numerical value indicating the creditworthiness of companies that have not issued publicly offered bonds is recognized as an urgent need by financial institutions.

  The present invention has been devised in view of the above-mentioned problems related to calculation of corporate creditworthiness, and provides a technique capable of calculating with high accuracy a value indicating the creditworthiness of a company not issuing publicly offered bonds. It is aimed.

  In order to achieve the above-mentioned object, the creditworthiness calculation system for a company described in claim 1 is characterized in that the remaining period of a plurality of publicly offered bonds, a spread indicating the difference between the yield of each publicly offered bond and the yield of a government bond, Storage means for storing specific financial data relating to the issuing company of the bonds at each of the first time point and each second time point that has passed a predetermined period from the first time point, and the remaining time period and financial data described above Means for executing regression analysis as an explanatory variable of the spread and deriving regression models for each of the first time point and the second time point; means for storing these regression models in the regression model storage means; and debts of a plurality of companies With respect to each of the remaining periods at the first time point, means for inputting financial data of the same type as the above for each company, the regression model at the first time point, the remaining period of each liability and the goods for each company Means for calculating the spread of each liability at the first time using the data, means for arranging the spreads of each debt in order of their size, and classifying them into a predetermined number of groups according to their values; Means for calculating the average spread of the group, storage means for storing the default performance data of each company spanning the first time point and the second time point, and the default rate of the group unit based on the default performance data , A means for deriving a regression equation indicating the correlation between the spread and the default based on the average spread and default rate of each group, and the remaining period for the liability of the specific company at the second time point , Means for inputting financial data of the same type as the above, the regression model at the second time point, the second time point of the liability Means to calculate the spread of the debt at the second time point using the remaining period and the financial data of the company, and by substituting the spread into the regression equation above, the predetermined time from the second time point of the company And a means for calculating a default probability during a period up to a third time point.

  In addition, the creditworthiness calculation system for a company described in claim 2 is based on the remaining period of a plurality of publicly offered bonds, the spread indicating the difference between the yield of each publicly offered bond and the yield of government bonds, Storage means for storing the code and specific financial data relating to each issuing company at each of the first time point and the second time point after a predetermined period of time from the first time point; Regression analysis using period and financial data as explanatory variables for the above spread, means for deriving industry-specific regression models at the first time point and second time point, and storing these regression models in the regression model storage means For the debts of multiple companies, the remaining period at the first point in time, the industry code of each company, the means for inputting financial data of the same type as above for each company, and the above for the industry of each company First time Means for extracting the regression model in the regression model storage means, means for calculating the spread of each liability at the first time point using each regression model, the remaining period of each liability and the financial data relating to each company; Means for arranging the spreads of each debt in order of their size and classifying them into a predetermined number of groups according to their values; means for calculating the average spread of each group; and the first time point and the second time point A storage means for storing the default actual data of each company, a means for calculating a default rate for each group based on the default actual data, a spread based on the average spread and the default ratio of each group, A means of deriving a regression equation showing a correlation with the default, and the remaining period of the liability of the specific company at the second time point Means for inputting the industry code of the company, financial data of the same type as the above for the company, means for extracting a regression model corresponding to the industry of the company at the second time point from the regression model storage means, Using the regression model, the remaining period of the liability at the second time point and the financial data for the company, the means for calculating the spread of the liability at the second time point, and substituting the spread into the regression equation above By means of the above, there is provided a means for calculating a default probability between a second time point of the company and a third time point after a predetermined period.

  The system for calculating creditworthiness of a company according to claim 3 is the system according to claim 1 or 2, further comprising means for converting the spread of the debt into a spread of discount bonds. .

  The company's creditworthiness calculation program according to claim 4 uses a computer to calculate the remaining period of a plurality of publicly offered bonds, spreads representing the difference between the yield of each publicly offered bond and the yield of government bonds, and the issuing company of each publicly offered bond. Storage means for storing the specific financial data at each of the first time point and every second time point that has passed a predetermined period from the first time point, and the remaining period and the financial data as explanatory variables of the spread Means for performing regression analysis and deriving regression models for each of the first time point and the second time point; means for storing these regression models in the regression model storage means; By using each remaining period, means for inputting financial data of the same type as above for each company, regression model at the first point in time, remaining period of each liability and financial data for each company Means for calculating the spread of each liability at the first point in time, means for arranging the spreads of each debt in order of their sizes, classifying them into a predetermined number of groups according to the values, and calculating the average spread of each group Means, storage means for storing the default performance data of each company spanning the first time point and the second time point, means for calculating a default rate for each group based on the default performance data, average of each group A means for deriving a regression equation showing the correlation between spread and default based on the spread and default ratio, and the remaining period and the same kind of financial Means for inputting data, regression model at the second time point, remaining period of the liability at the second time point and the company Means for calculating the spread of the debt at the second time point using the financial data relating to the third time point after a predetermined period of time has elapsed from the second time point of the company by substituting the spread into the above regression equation It is characterized by functioning as a means for calculating a default probability in the period up to.

In the corporate creditworthiness calculation system described in claim 1 or 2, and the corporate creditworthiness calculation program described in claim 4, first, the reality of each publicly issued bond issuer company at the first time point and the second time point A regression model that shows the correlation between spread and financial data is generated at each point of time based on the spreads and various financial data that indicate the safety and profitability of each publicly issued bond issuer company. After applying the financial data of companies that have not issued publicly offered bonds to estimate the spread at the first point in time for the debt associated with each company, the spread based on this spread and the default performance data of companies that have not issued publicly issued bonds between the two points in time. The regression equation showing the correlation between the default and the default is obtained, and then the financial data and remaining years of the specific publicly issued bond unissued company at the second time Applied to definitive regression model estimates the spread of liability for the company, by substituting this into the above regression formula, and a mechanism for calculating the probability of default in the future of the public issue unissued companies.
Thus, since the creditworthiness (default probability) of companies that have not issued publicly offered bonds can be calculated as objective numerical values, it is useful as a risk management tool in financial institutions and the like.

  In the case of the corporate creditworthiness calculation system according to claim 2, a regression model for each industry is derived and a regression model corresponding to the industry of the company is applied when calculating the spread of each liability. There is an advantage that a calculation result reflecting characteristics of each industry can be obtained.

  According to the creditworthiness calculation system described in claim 3, since a spread converted into a discount bond base is obtained, an advantage of obtaining a more accurate calculation result excluding credit information for coupons (interest) of publicly offered bonds There is.

FIG. 1 is a block diagram showing a functional configuration of a corporate creditworthiness calculation system 10 according to the present invention. An input device 12 such as a keyboard and a mouse, an input information registration unit 14, a basic information storage unit 16, and a regression Model generation unit 18, regression model storage unit 20, loan information storage unit 22, spread calculation unit 24, spread storage unit 26, default performance storage unit 28, regression equation calculation unit 30, regression equation storage unit 32, a default probability calculation unit 34, a calculation result storage unit 36, a calculation result output unit 38, and a display 40.
The input information registration unit 14, regression model generation unit 18, spread calculation unit 24, regression equation calculation unit 30, default probability calculation unit 34, calculation result output unit 38, the CPU of a computer (PC or the like) 42, the OS and This is realized by executing necessary processing according to a dedicated application program or the like.
In addition, the basic information storage unit 16, the regression model storage unit 20, the loan information storage unit 22, the spread storage unit 26, the default performance storage unit 28, the regression equation storage unit 32, and the calculation result storage unit 36 are stored in the hard disk of the computer 42. Or in memory.

FIG. 2 shows a rough flow of processing in the system 10 and is roughly divided into the following five stages.
(1) Spread 50 which is the difference between the market interest rate of each publicly offered bond and the interest rate of government bonds at the past time point (first time point) that goes back for a predetermined period (for example, 2 years) from the present time, the remaining age of each publicly offered bond 52, The regression model 56 is generated at each time point by statistically processing the financial data 54 and the spread 50 of each publicly offered bond at the present time (second time point), the remaining years 52, and the financial data 54 of each issuing company The first stage to do.
(2) Estimated spread of each loan at the past point in time by substituting the remaining years 58 of loans related to multiple publicly issued bonds not yet issued and financial data 60 of each company into the regression model 56 at the past point Second stage to derive 62 (based on discounted bonds).
(3) Based on the estimated spread 62 of each corporate loan and the default actual data 64 of each publicly issued bond unissued company between the past and the present time, a regression equation (approximation) 66 showing the relationship between the spread and default is derived. The third stage to do.
(4) By substituting into the regression model 56 the current remaining loan age of a particular publicly issued bond unissued company 68 and the financial data 70 of the company, the current estimated spread of the loan (discounted bonds) Base) The fourth stage of deriving 72.
(5) By substituting the current spread 72 of the loan into the regression equation 66 above, the default probability of the company not issuing public bonds until the future time point (third time point) after two years 5th step of calculating 74.

Hereinafter, specific processing procedures according to the first to third stages will be described with reference to the flowchart of FIG. 3.
First, the operator inputs a large number of patterns of combinations of basic information including the issuing company code of the publicly issued bond issuer, the industry code, various financial data, the issue code of the publicly offered bond, the spread, and the remaining years via the input device 12 (S10 ). The basic information is input for both the past time and the current time.

  The spread represents the difference between the yield of publicly offered bonds and the yield of government bonds. JGBs can be considered as bonds with no credit risk, so the larger the spread, the better the yield, but the higher the credit risk.

In addition, as the above-described financial data, for example, indicators that strongly indicate the safety (risk) and profitability of the company, such as the total asset amount of the company, the capital adequacy ratio, and the total return on capital, are selected.
This financial data is specifically selected through the following procedure.
(1) List a large number of financial data as candidates.
(2) The companies that existed at a certain point in time in the past (time A) are classified into companies that have been defaulted and those that have not been defaulted.
(3) From the financial data of each company at time A, select a predetermined number that greatly differs between the default group and the non-default group.
Evaluation of this difference is judged by using as an index the difference between the average values of each group divided by the standard deviation of the default group.

The input basic information is converted into a necessary format by the input information registration unit 14, and then stored in the basic information storage unit 16 at each time point (S12).
Instead of inputting basic information via the input device 12, a basic information file shaped into a predetermined format is stored in a recording medium such as a memory card, and the basic information storage unit 16 is connected via a reader. The basic information file stored in another server (not shown) may be received via a communication line (not shown).

Next, the regression model generation unit 18 is activated, and performs the multiple regression analysis for each industry by using the input remaining years of each publicly offered bond and the financial data of the publicly issued bond issuing company as explanatory variables, and the spread as an objective variable, A regression model (proportional hazard model) for each industry is derived for each time point (S14).
Specifically, by generating many samples by substituting the spread of each publicly offered bond, remaining years, and financial data into the relational expression (regression formula), and performing regression analysis on these samples, λ (constant term) , Γ (regression coefficient of remaining years), βi (regression coefficient of each financial data) are estimated. An example of the relational expression is shown in Equation 1.

FIG. 4 is a table exemplifying a calculation result when using the relational expression of Equation 1, and λ (constant term), γ (remaining years) for each industry of “material”, “transport”, and “automobile”. ), Β1 to βn (regression coefficient of each financial data) are stored.
In addition, the closer the value of “R-Square” (multiple coefficient of determination) is to 1, the better the correlation between items, and the higher the accuracy of prediction in multiple regression analysis, but relatively good for each industry It can be evaluated that the correct numerical value is derived.
The values of λ, γ, and βi are stored by the regression model generation unit 18 in the regression model storage unit 20 for each time point / industry (S16).

Next, the operator inputs, via the input device 12, the industry codes of a large number of companies that have not issued publicly issued bonds, the remaining loan years in the past, and financial data of the same type as above (S18).
The input information related to the corporate loan is converted into a necessary format by the input information registration unit 14, and then stored in the loan information storage unit 22 for each company (S20).

Thereafter, the spread calculation unit 24 is activated, and the regression model (combination pattern of λ, γ, βi) corresponding to the business type of each company at the past time is extracted from the regression model storage unit 20 (S22).
Next, the spread calculation unit 24 calculates the remaining loan years in t of equation 1, zik each financial data, λ a constant term, γ regression coefficient of remaining years, βi regression coefficient of each financial data. By substituting, the spread Sk (t) of each corporate loan is calculated (S24).

By the way, corporate bonds are accompanied by coupons (interest), and it is considered that the spread obtained above contains credit information for coupons that are generated regularly (many every six months).
For this reason, the spread calculation unit 24 executes a process of converting the spread into a spread based on a discount bond (zero coupon bond) where no coupon is generated.

First, the remaining years are 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, Assuming 7.5, 8.0, 8.5, 9.0, 9.5, 10.0 years…
i: Time grid number (i = 1, ..., 21)
ti: Time grid
・ T1 = 0.25
・ When i ≧ 2, t1 = 0.5 * (i−1)
If we define as follows, the method for estimating the discount bond base spread for the remaining years tN is as follows.
First, in the case of N = 1 (that is, the remaining age of 0.25 years), coupons are no longer generated, so the spread calculated above is used as the discount bond-based spread as it is.
On the other hand, when N ≧ 2, the spread calculation unit 24 calculates discount bond-based spreads corresponding to the remaining years by executing the following processes.

この数式を解くことにより、以下の数３に示すように、パーイールドxk,Nが求まる。

[Calculation of per-yield (based on half year compound interest)]
First, a per-yield (half year compound interest basis) xk, N is obtained from the spread sk, i of the loan remaining life ti of the company k (S26).
That is, the following equation 2 is derived from the definition of par yield.

By solving this mathematical expression, the per-yield xk, N is obtained as shown in the following equation (3).

つぎに、i＝3（残存年数1.0年）とした場合も、パーイールドの定義により、以下の数５に示す通りEk,3が求められる。

i≧4以降も同様にパーイールドの定義に従い、以下の数６に示す通りEk,4〜Ek,2Nが求められる。

[Calculation of discount factor]
Next, the spread calculation unit 24 obtains a discount factor Ek, N of the company k from the par yield xk, N of the company k (S28).
First, when i = 2 (remaining years 0.5 years), Ek, 2 is obtained as shown in the following equation 4 according to the definition of par yield.

Next, even when i = 3 (remaining years 1.0), Ek, 3 is obtained as shown in the following equation 5 by the definition of par yield.

Similarly, after i ≧ 4, Ek, 4 to Ek, 2N are obtained in accordance with the definition of par yield, as shown in the following equation (6).

つぎにスプレッド算出部24は、算出した割引債ベーススプレッドを年率に換算した値を、スプレッド記憶部26に格納する（Ｓ32）。 [Calculation of discount bond base spread]
Next, the spread calculation unit 24 calculates a discount bond base spread S′k, i of the remaining loan period ti of the company k from the discount factor EN of the remaining period ti of the company k (S30).
That is, the following equation 7 is established from the definition of spread, and by developing this, a discount bond base spread is obtained as shown in equation 8.

Next, the spread calculation unit 24 stores a value obtained by converting the calculated discount bond base spread into an annual rate in the spread storage unit 26 (S32).

Next, the regression equation calculation unit 30 is activated, and the discount bond base spreads (annualized conversion) of each company stored in the spread storage unit 26 are sorted in ascending order and classified into a plurality of groups (quantiles) according to the values. (S34). For example, in this embodiment, it is classified into 10 groups.
Next, the regression equation calculation unit 30 calculates the average spread value for each group (S36).
Next, the regression equation calculation unit 30 reads the default result data (bankruptcy or survival) of each publicly offered bond unissued company stored in the default result storage unit 28, and calculates the default probability for each group. Calculate (S38).

Next, the regression equation calculation unit 30 derives a regression equation (approximate equation) based on the value of the default probability of each group (S40) and stores it in the regression equation storage unit 32 (S42).
For example, as shown in FIG. 5, a regression equation y = 0.0318 * √x is derived.
FIG. 5 also illustrates a regression value obtained by substituting the average spread of each group in the past, the default probability of companies belonging to each group, and the average spread into the regression equation.

FIG. 6 shows a graph in which the spread is set on the X-axis and the default probability is set on the Y-axis, and the diamond-shaped points in the graph plot the average spread of each group.
Further, the curve α in FIG. 6 corresponds to a regression equation of “y = 0.0318 * √x”.
From this curve α, it can be clearly seen that the higher the spread, the higher the default probability of the company.

As described above, when the derivation of the regression model and the regression equation is completed based on the data of a large number of publicly issued bond issuers and non-publicly issued bonds, the first to third preparation steps are completed. As shown in Fig. 5, the process proceeds to the default probability calculation stage (fourth and fifth stages) of the specific company.
First, the operator inputs, via the input device 12, the industry code of a specific publicly issued bond unissued company, the remaining loan years at the present time, and financial data of the same type as described above into the system 10 (S50).
These pieces of input information relating to the loan are converted into a necessary format by the input information registration unit 14, and then stored in the loan information storage unit 22 (S52).

Thereafter, the spread calculation unit 24 is activated, and the regression model (combination pattern of λ, γ, βi) corresponding to the type of business of the company at the present time is extracted from the regression model storage unit 20 (S54).
Next, the spread calculation unit 22 calculates the remaining loan years in t of Equation 1, each financial data in zik, a constant term in λ, a regression coefficient of the remaining years in γ, and a regression coefficient of each financial data in βi. Is substituted for the spread Sk (t) at the present time of the corporate loan (S56).
Next, the spread calculation unit 24 executes a process substantially equal to S26 to S32 in FIG. 3 to calculate the yield at a specific point in time for the company (S58) and the discount factor at the specific point in time for the company. The calculation process (S60) and the calculation process (S62) of the discount bond base spread at a specific time of the company are executed, and the value obtained by converting the discount bond base spread as the calculation result into the annual rate is stored in the spread storage unit 26 ( S64).

Next, the default probability calculation unit 34 is activated and substitutes the discount bond base spread (annualized conversion) of the company into (x) of the regression equation “y = 0.0318 * √x”, thereby calculating the current to future time of the company. A default probability (y) in the interval (over the next two years) is calculated (S66).
The default probability value is stored in the calculation result storage unit 36 (S68), then processed into a predetermined format by the calculation result output unit 38, and displayed on the display 40.

  As described above, this system 10 makes it possible to calculate the default probability of a company as an objective value based on the loan and financial data of a company that has not issued publicly offered bonds. There is an advantage that the risk management of the investment target becomes easy.

It is a block diagram which shows the function structure of a corporate creditworthiness calculation system. It is a schematic diagram for outlining the main processing content in this system. It is a flowchart which shows the specific process sequence in this system. It is a table which illustrates the calculation result of λ, γ, βi of Equation 1. It is explanatory drawing which shows the average spread, default probability, regression value, and regression formula of each group. It is a graph in which a spread is set on the X axis and a default probability is set on the Y axis. It is a flowchart which shows the specific process sequence in this system.

Explanation of symbols

10 Corporate creditworthiness calculation system
12 Input device
14 Input information registration section
16 Basic information storage
18 Regression model generator
20 Regression model storage
22 Spread calculation section
22 Loan information storage
24 Spread calculation section
26 Spread memory
28 Default results storage
30 Regression equation calculator
32 regression equation storage
34 Default probability calculator
36 Calculation result storage
38 Calculation result output section
40 displays
42 computers
50 spreads
52 Remaining years of publicly offered bonds
54 Financial data of publicly issued bond issuers
56 regression model
58 Loans remaining
60 Financial data of non-publicly issued bonds
62 Estimated spread of corporate loans
64 Default performance data
66 regression
68 Loan remaining years
70 Financial data of non-publicly issued bonds
72 Estimated spread of corporate loans
74 Default probability

Claims (4)

The remaining period of multiple publicly offered bonds, the spread that represents the difference between the yield of each publicly offered bond and the yield of government bonds, and specific financial data related to the issuer of each publicly offered bond Storage means for storing each second time point after a predetermined period of time,
Means for performing a regression analysis using the remaining period and financial data as explanatory variables of the spread, and deriving a regression model for each of the first time point and the second time point;
Means for storing these regression models in a regression model storage means;
With respect to the liabilities of multiple companies, means for inputting respective remaining periods at the first time point and financial data of the same type as above for each company;
Means for calculating the spread of each liability at the first time point using the regression model at the first time point, the remaining period of each liability and the financial data relating to each company;
Means for arranging spreads of each debt in order of their size and classifying them into a predetermined number of groups according to their values;
A means to calculate the average spread for each group;
Storage means for storing default performance data of each company between the first time point and the second time point;
Based on this default result data, a means for calculating a default probability for each group,
A means for deriving a regression equation showing the correlation between spread and default based on the average spread and default probability of each group;
With respect to the liability of a specific company, the remaining period at the second time point as above and means for inputting the same kind of financial data as above for the company;
Means for calculating the spread of the liability at the second time point using the regression model at the second time point, the remaining period of the liability at the second time point and the financial data relating to the company;
By substituting this spread into the above regression equation, a means for calculating a default probability between a second time point of the company and a third time point after a predetermined period of time;
A creditworthiness calculation system for companies characterized by comprising First, the remaining period of multiple publicly offered bonds, the spread that represents the difference between the yield of each publicly offered bond and the yield of government bonds, the industry code of the issuing company of each publicly offered bond, and specific financial data pertaining to each issuing company Storage means for storing each time point and each second time point after a predetermined period of time from the first time point;
Means for performing regression analysis with the remaining period and financial data as explanatory variables for the spread for each same industry, and deriving industry-specific regression models for each of the first time point and the second time point;
Means for storing these regression models in a regression model storage means;
With respect to the liabilities of multiple companies, means for inputting each remaining period at the first time point, the industry code of each company, and the same kind of financial data as above for each company,
Means for calculating the spread of each liability at the first time point using the regression model at the first time point for the industry of each company using the remaining period of each liability and the financial data for each company;
Means for arranging spreads of each debt in order of their size and classifying them into a predetermined number of groups according to their values;
A means to calculate the average spread for each group;
Storage means for storing default performance data of each company between the first time point and the second time point;
Based on this default result data, a means for calculating a default probability for each group,
A means for deriving a regression equation showing the correlation between spread and default based on the average spread and default probability of each group;
Regarding the liability of a specific company, means for inputting the remaining period at the second time point, the industry code of the company, and the same kind of financial data as the above for the company
Means for calculating the spread of the liability at the second time point using the regression model corresponding to the business type of the company at the second time point, the remaining period of the liability at the second time point and the financial data relating to the company. When,
By substituting this spread into the above regression equation, a means for calculating a default probability between a second time point of the company and a third time point after a predetermined period of time;
A creditworthiness calculation system for companies characterized by comprising   3. The corporate creditworthiness calculation system according to claim 1, further comprising means for converting the spread of the debt into a spread of discount bonds. Computer
The remaining period of multiple publicly offered bonds, the spread that represents the difference between the yield of each publicly offered bond and the yield of government bonds, and specific financial data related to the issuer of each publicly offered bond Storage means for storing each second time point after a predetermined period of time,
Means for performing a regression analysis using the remaining period and financial data as explanatory variables of the spread, and deriving a regression model for each of the first time point and the second time point;
Means for storing these regression models in the regression model storage means;
With respect to the liabilities of multiple companies, the means for inputting the remaining period at the first time point and financial data of the same type for each company,
Means for calculating the spread of each liability at the first time point using the regression model at the first time point, the remaining period of each liability and the financial data relating to each company;
Means to arrange spreads of each debt in order of their size and classify them into a predetermined number of groups according to their values;
A means to calculate the average spread for each group,
Storage means for storing default performance data of each company between the first time point and the second time point;
A means for calculating a default probability for each group based on the default result data,
A means of deriving a regression that shows the correlation between spread and default based on the average spread and default probability for each group;
With regard to the liability of a specific company, means for inputting the remaining period at the second time point and financial data of the same type as the above for the company,
Means for calculating the spread of the liability at the second time point using the regression model at the second time point, the remaining period of the liability at the second time point and the financial data relating to the company;
Means for calculating a default probability between the second time point of the company and the third time point after a predetermined period of time by substituting the spread into the regression equation;
A creditworthiness calculation program for companies characterized by functioning as

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