JP2023058857A - System, method, and program for evaluating enterprise value by digitizing non-financial information - Google Patents

Abstract

To objectively and properly evaluate an enterprise value by digitizing non-financial information.SOLUTION: A data acquisition unit 32 of an evaluation system 10' acquires ESG data released by evaluation institutions or nonfinancial risk-related data on individual enterprises from an external terminal (not illustrated). An element analysis unit 33 performs analysis for each of the evaluation institutions and analysis for each of ESG elements by using the data acquired by a data acquisition unit 22. A change calculation unit 34 obtains a variety of parameter values in a prescribed period before and after a date on which a specific event occurs, and compares the obtained parameters values. An output unit 35 displays the analysis result obtained by the element analysis unit 33 and the calculation result obtained by the change calculation unit 34 in accordance with a user's instruction.SELECTED DRAWING: Figure 10

Description

The present invention relates to a system, method, and program for quantifying non-financial information and evaluating corporate value.

In recent years, there has been a movement to evaluate non-financial information and disclose ESG ratings in order to expand ESG investment (see, for example, Non-Patent Document 1). Here, ESG investment means analyzing and evaluating a company's future potential and sustainability from the three perspectives of Environment, Social, and Governance. It is a method of sorting.

MSCI, ESG score posting site, Internet <https://www.msci.com/our-solutions/esg-investing/esg-ratings/esg-ratings-corporate-search-tool>

However, although ESG ratings are useful as a method of scoring non-financial information and evaluating companies based on the status of ESG compliance, etc., it is difficult to objectively assess how the ratings affect a company's economic value. In addition, there is a problem that it is difficult to grasp it properly.

The present invention has been made in view of the circumstances described above, and one of its purposes is to provide a technology that can objectively and appropriately evaluate corporate value by quantifying non-financial information. do.

A system according to an embodiment of the present invention is a corporate value evaluation system comprising a control unit and a storage unit. a obtaining step of obtaining ESG score data including E-score, S-score, and G-score representing environmental (E), social (S), and corporate governance (G) scores of the target company; The gist is that an analysis step of performing a multiple regression analysis with the G-score as an explanatory variable and a non-financial risk ratio as an explained variable, and an output step of outputting the results of the multiple regression analysis are executed.

Here, in the analysis step, multiple regression analysis of the non-financial risk ratio using the ESG score data of the evaluation institution is performed using the multiple regression formula shown in formula (1),
Y0=α*X01+β*X02+γ*X03+δ (1)
Y0: Non-financial risk ratio X01: E-score X02: S-score X03: G-score In the output step, the predicted value of the non-financial risk ratio is output using the ESG score data of the evaluation agency obtained by multiple regression analysis. You may do so.

According to the embodiment of the present invention, by quantifying non-financial information, it becomes possible to objectively and appropriately evaluate corporate value.

1 is a configuration diagram schematically showing the configuration of a network including an evaluation system according to one embodiment of the present invention; FIG. It is a functional block diagram of an evaluation system. It is the figure which illustrated the setting screen displayed on a display apparatus. FIG. 10 is an explanatory diagram of multiple regression analysis of index numbers; FIG. 10 is an explanatory diagram of simple regression analysis of company A; FIG. 4 is an explanatory diagram for calculating the intrinsic market capitalization of company A; FIG. 4 is an explanatory diagram for calculating the non-financial risk amount and the non-financial risk ratio of company A; It is the figure which illustrated the output screen displayed on a display apparatus. It is the figure which illustrated the graph displayed on a display apparatus. It is a block diagram which shows the functional structure of the evaluation system which concerns on 2nd Embodiment. It is the figure which illustrated the setting screen displayed on a display apparatus. It is the figure which illustrated the ESG data acquired from each evaluation agency, and a non-financial risk ratio. It is the figure which illustrated the result of the multiple regression analysis when using the ESG data of the evaluation institution. An example of output when multiple regression analysis is performed for each evaluation agency is shown. It is the figure which illustrated the company group for comparison. FIG. 10 is a diagram showing transition of each parameter value when the ESG rating changes; FIG. 10 is a diagram showing a comparative example of non-financial risk ratios of a group of companies with the same ESG rating as the target company;

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

A. First embodiment (outline)
Conventionally, as a response to non-financial information risks (hereafter referred to as non-financial risks), a method of extracting those that can be quantitatively grasped from individual risks, calculating the amount of risk, and conducting risk assessment has been widely known. However, there is still no known method of adopting an approach to grasp the amount of non-financial risk as a whole.

By adopting such an advanced approach, the method disclosed in this disclosure is characterized by the ability to grasp information related to non-financial risks (non-financial risk-related information), such as the risk amount and risk ratio of non-financial risks. be.

As will be described in more detail below, the method of the present disclosure generates non-financial risk-related information for individual companies by considering their performance relative to their parent index. The “parent index” refers to a representative stock index of the stock market, such as TOPIX.

(composition)
FIG. 1 is a configuration diagram schematically showing the configuration of a network including an evaluation system 10 for evaluating corporate value according to one embodiment of the present invention. As illustrated, the evaluation system 10 is communicably connected to the financial information terminal 20 via a communication network NW such as the Internet. The financial information terminal 20 is configured by, for example, a server computer or the like, and provides the evaluation system 10 with data relating to various financial information (eg, index numbers, stock indices, exchange rates, financial information of individual companies, etc.). The evaluation system 10 acquires necessary data from the financial information terminal 20 and generates non-financial risk-related information.

The evaluation system 10 has a configuration as a general computer. Specifically, the evaluation system 10 exchanges information between a computer processor 11 configured as a CPU or GPU, a main memory 12 configured by a DRAM or the like for temporarily storing data and programs, and a user or the like. , a communication interface 14 for controlling wired or wireless communication, and a storage 15 configured by a magnetic disk, flash memory, or the like for storing data and programs. The computer processor 11 loads a program stored in the storage 15 or the like into the main memory 12 and executes instructions included in the program.

The input/output interface 13 includes, for example, input devices such as a keyboard, mouse, touch panel, microphone, and camera, display devices such as a display, and audio output devices such as speakers. The communication I/F 14 is implemented as hardware such as a network adapter, various types of communication software, or a combination thereof. The storage 15 stores various data and various programs obtained from the financial information terminal 20 .

Also, the evaluation system 10 implements the functions of the units shown in FIG. 2 by the computer processor 11 executing instructions included in programs stored in the storage 15 or the like.

FIG. 2 is a block diagram showing the functional configuration of the evaluation system 10. As shown in FIG.
The evaluation system 10 includes a setting unit 21, a data acquisition unit 22, a multiple regression analysis unit 23, a simple regression analysis unit 24, a non-financial risk amount calculation unit 25, an output unit 26, and the like.

The setting unit 21 sets individual companies for which the non-financial risk amount is to be calculated, the observation period, the output target period and calculation method, explanatory variables for each regression analysis, etc. (see FIG. 3). ). In addition, in this embodiment, it is assumed that the following preconditions are set.

<Prerequisites>
・Company A is the issue of an individual company (individual issue) for which the non-financial risk amount is to be calculated.
・The multiple regression analysis and single regression analysis in the calculation process are based on date and time data, the observation period is 10 years (calendar base), the output period is 3 years, and the output calculation method is monthly basis.
・In the multiple regression analysis of the parent index, the dependent variable is the parent index (closing price), and the explanatory variables are the stock index, the exchange rate, and the net assets of the parent index.
・Regarding the simple regression analysis of individual companies, regarding the first adjusted market value (net assets portion), the dependent variable is the net assets of the individual company, and the explanatory variable is the net assets of the parent index.
・Regarding the second modified market price (PBR (Price Book-value Ratio) portion) in the simple regression analysis of individual companies, the dependent variable is the PBR of the individual company, and the explanatory variable is the PBR of the parent index.

The data acquisition unit 22 acquires from the financial information terminal 20 data necessary for the multiple regression analysis of the parent index, data necessary for the single regression analysis of individual companies, and the like. In this embodiment, it is assumed that the non-financial risk amount of Company A as of the end of December 2019 is calculated. Get from
<Details of date and time data>
• Acquire the parent index (closing price), parent index (net worth), stock index, and exchange rate for multiple regression analysis.
・For simple regression analysis, obtain individual companies (market capitalization), individual companies (net assets), and individual companies (stock prices).

Any parent index, stock index, exchange rate, or net worth can be adopted. For example, "TOPIX" and "JPX400" are used as parent indices, "MSCI World Index" and "FTSE All-World Index" are used as stock indices, and "dollar-yen exchange rate" is used as exchange rate. It is possible to use "total net assets" and "shareholders' equity" as net assets, but it is not intended to be limited to these.

The multiple regression analysis unit 23 and the single regression analysis unit 24 use the data acquired by the data acquisition unit 22 to perform multiple regression analysis and single regression analysis, respectively, and send the analysis results to the non-financial risk amount calculation unit 25 and Output to the output unit 26 . The non-financial risk amount calculator 25 calculates the non-financial risk amount and the like using the analysis results of each regression analysis. The output unit 26 displays the calculated non-financial risk amount and the like on the display device according to the user's instruction.

Details of the operations of the multiple regression analysis unit 23, the single regression analysis unit 24, the non-financial risk amount calculation unit 25, and the output unit 26 will be described below in the case of calculating the non-financial risk amount of company A as of December 30, 2019. will be described in detail.

<Multiple regression analysis unit 23>
FIG. 4 is an explanatory diagram of the multiple regression analysis of the index index.
The multiple regression analysis unit 23 first calculates the PBR of the parent index by regarding the parent index (closing price) as the market capitalization.
Specifically, the multiple regression analysis unit 23 divides the parent index (closing price) by the parent index (net assets) to calculate the parent index (PBR). In the example shown in FIG. 4, the index number (PBR)=1.15675 (=1721.36/1488.1) (see F1 shown in FIG. 4). Parent index (PBR) is utilized in performing simple regression analysis.

Next, the multiple regression analysis unit 23 performs multiple regression analysis of the parent index using the multiple regression equation shown in formula (1) in FIG. calculate. In the example shown in FIG. 4, the index (predicted value)=1771.185 (=0.474939*2353.25+10.73352*108.88+0.25236*1488.1-890.669).

Further, the multiple regression analysis unit 23 divides the parent number (predicted value) by the parent number (net assets) to calculate the parent number (corrected PBR). Here, since the non-financial risk amount related to the parent index (predicted value) is zero, the parent index (PBR) is corrected on the premise that the parent index (net assets) remains unchanged. In the example shown in FIG. 4, the index (corrected PBR)=1.19023 (=1771.185/1488.1) (see F2 shown in FIG. 4). The multiple regression analysis unit 23 sends the results of the multiple regression analysis to the simple regression analysis unit 24, the non-financial risk amount calculation unit 25, and the output unit 26.

<Single regression analysis unit 24>
FIG. 5 is an explanatory diagram of simple regression analysis of company A (individual issue).
The simple regression analysis unit 24 first calculates the PBR of the company A by dividing the market capitalization of the company A by the net assets of the company A. In the example shown in FIG. 5, Company A (PBR)=0.78983 (=2398903/3037254) (see F3 shown in FIG. 5). Company A (PBR) will be used when performing the subsequent simple regression analysis.

Next, the simple regression analysis unit 24 performs the first simple regression analysis of Company A using the simple regression equation (on the net assets side) shown in Equation (2) in FIG. Calculate the predicted value of the net assets of Company A. In the example shown in FIG. 5, company A (net assets (predicted value))=1771.185 (=0.474939*2353.25+10.73352*108.88+0.25236*1488.1-890.669).

Subsequently, the simple regression analysis unit 24 performs a second simple regression analysis of Company A using the simple regression equation (PBR side) shown in Equation (3) in FIG. Calculate the predicted value of PBR of Company A. In the example shown in FIG. 5, company A (PBR (predicted value))=0.8799 (=0.973356*1.15675-0.24602). The simple regression analysis unit 24 sends the results of the simple regression analysis to the non-financial risk amount calculation unit 25 and the output unit 26 .

<Non-financial risk amount calculation unit 25>
FIG. 6 is an explanatory diagram for calculating the intrinsic market capitalization of Company A. FIG.
As shown in Figure 6, the intrinsic market capitalization of Company A consists of a first adjusted market value (intrinsic value of the net worth portion) and a second adjusted market value (intrinsic value of the PBR difference portion).

(Calculation of first adjusted market value)
First, the non-financial risk amount calculation unit 25 multiplies the company A (net assets (predicted value)) obtained by the simple regression analysis unit 24 by the parent index (corrected PBR) obtained by the multiple regression analysis unit 23, Calculate the first corrected market price of A (see formula (4) shown in FIG. 6). In the example shown in FIG. 6, Company A's first corrected market price=3664553 (=3078861*1.19023).

(Calculation of second adjusted market value)
Next, the non-financial risk amount calculation unit 25 applies (substitutes) the parent index (corrected PBR) to the simple regression equation (PBR side) shown in Equation (3) in FIG. (see N1 shown in FIG. 6). The reason why the adjusted PBR of Company A (hereinafter referred to as Company A (adjusted PBR)) is obtained in this way is as follows. That is, since the company A (PBR) obtained by the simple regression analysis unit 24 is the PBR of the actual value of the parent index, consideration is given to how the PBR of the company A will change when the parent index is the predicted value. There is a need to. Here, since the parent index (predicted value) = 1771.185, the non-financial risk amount calculation unit 25 obtains the PBR of Company A when the parent index is 1771.185, that is, Company A (adjusted PBR). . In the example shown in FIG. 6, Company A (adjusted PBR)=0.9125 (=0.973356*(1.19023-0.24602)).

The non-financial risk amount calculation unit 25 subtracts the parent index (corrected PBR) from the company A (net assets (predicted value)) obtained by the simple regression analysis unit (= PBR difference) to calculate the second adjusted market price of company A (see formula (5) shown in FIG. 6). In the example shown in FIG. 6, the second corrected market price of Company A=▴855092 (=3078861*(0.9125−1.19023)) (▴ means minus).

(Generation of non-financial risk-related information)
FIG. 7 is an explanatory diagram for generating non-financial risk-related information of company A (here, calculating non-financial risk amount and non-financial risk ratio).
Since the market capitalization of Company A can be said to be the sum of the non-financial risk amount added to the intrinsic market capitalization of Company A (see formula (6) shown in FIG. 7), first, the non-financial risk amount calculation unit 25 The intrinsic market capitalization of Company A is obtained by adding the second adjusted market value to the first adjusted market value (see formula (7) shown in FIG. 7). Then, the non-financial risk amount calculation unit 25 subtracts the intrinsic market capitalization (= first adjusted market value + second adjusted market value) from the market capitalization of company A as of December 30, 2019, Calculate the non-financial risk amount of Company A. Further, the non-financial risk amount calculation unit 25 calculates the non-financial risk ratio of company A by dividing the non-financial risk amount of company A by the market capitalization of company A.

In the example shown in FIG. 7, since the intrinsic market capitalization of Company A = 2809461 (= 3664553 - 855092) and the market capitalization = 2398903, the non-financial risk amount = -410558 (= 2398903 - 2809461). Financial risk ratio=▲17.11% (=(▲410558/2398903)*100).

The non-financial risk amount calculation unit 25 may calculate the non-financial risk amount and the non-financial risk ratio of the parent index. Specifically, the non-financial risk amount calculator 25 calculates the non-financial risk amount of the parent number by subtracting the predicted value of the parent number from the parent number. Furthermore, the non-financial risk amount calculation unit 25 divides the non-financial risk amount of the parent number by the parent number to calculate the non-financial risk ratio of the parent number.

Following the above example, the non-financial risk amount of the parent index = -49.825 (= 1721.36 - 1771.185), and the non-financial risk ratio of the parent index = -2.89% (= (-49.825 /1721.36)*100). The non-financial risk amount calculator 25 sends the calculated result to the output unit 26 .

<Output unit 26>
FIG. 8 is a diagram exemplifying an output screen P1 displayed on the display device.
On the output screen P1, in addition to basic setting information I1 related to the individual company (Company A) for which the non-financial risk amount was calculated, the parent index, etc., result information I2 including the non-financial risk amount, and other user-operable A regression analysis result button B1, a data verification button B2, and a graph creation button B3 are displayed.

The regression analysis result button B1 is a button for downloading (and/or displaying) the results of daily multiple regression analysis and single regression analysis (explained variables, explanatory variables, etc.) in the observation period in a tabular file. be.
The data verification button B2 is a button for verifying each calculation result received by the output unit and verifying from a statistical point of view.
The graph creation button B3 is a button for graphically displaying the result information (non-financial risk-related information) I2.

FIG. 9 is a diagram exemplifying a graph displayed on the display device.
In the example shown in FIG. 9, a graph showing changes in non-financial risk ratios of company A and the parent index over a set output target period (three years in this case) is displayed on the display device. By referring to this graph, the user can speculate that there was an event that shook the world, for example, from the sharp change in the non-financial risk ratio at the beginning of 2019. By appropriately setting the display items of the graph, the user can not only change the non-financial risk ratio of company A and the parent index, but also, for example, change the first and second adjusted market prices of company A, , a variety of non-financial risk-related information related to the non-financial risk of company A, such as changes in the non-financial risk ratio and parent index (stock price) of company A, can be displayed.

As explained above, according to the present embodiment, the amount of non-financial risk of individual companies is calculated in consideration of their performance relative to the parent index. It will be possible to evaluate it objectively and appropriately.

B. Second Embodiment In the second embodiment described below, ESG data disclosed by an external evaluation organization is acquired, the non-financial risk ratio of each element of E, S, and G of an individual company is estimated, and each element It is possible to numerically grasp the degree of influence in another. Also, in the second embodiment, it is possible to confirm changes in the non-financial risk ratio before and after the specific event occurs.
ESG data is data that represents ESG scores (including ESG ratings) published by each evaluation agency, and evaluation scores for each element of environment (E), society (S), and corporate , E-score, S-score, G-score).

The evaluation system 10′ according to the second embodiment has the same hardware configuration as the evaluation system 10 according to the first embodiment, and the instructions included in the program stored in the storage 15 or the like are executed by the computer processor 11. implements the function of each unit shown in FIG. Note that the evaluation system 10' may be configured by adding functions to the evaluation system 10 according to the first embodiment, but may be configured separately.

FIG. 10 is a block diagram showing the functional configuration of the evaluation system 10'.
The evaluation system 10' includes a setting unit 31, a data acquisition unit 32, an element analysis unit 33, a change calculation unit 34, an output unit 35, and the like.

The setting unit 31 sets the evaluation agency, sets the target time, sets the comparison target, etc. according to the user's instruction (input operation) (see FIG. 11).

The data acquisition unit 32 acquires ESG data published by each evaluation agency, data related to non-financial risk of each individual company (hereinafter referred to as non-financial risk-related data), and the like from an external terminal (not shown). Examples of non-financial risk-related data include parent index closing price (market capitalization), individual company market capitalization, individual company intrinsic market capitalization, individual company non-financial risk ratio, parent index non-financial risk ratio, etc. However, it is not intended to be limited to these.

The element analysis unit 33 uses the data acquired by the data acquisition unit 22 to perform analysis for each evaluation institution and for each ESG element.

FIG. 12 is a diagram exemplifying ESG data and non-financial risk ratios (actual values) acquired from each evaluation institution for target company A. In FIG.

The factor analysis unit 33 substitutes the ESG data (ESG score data) of each evaluation institution into the following formula (1) to estimate each coefficient α, β, γ (multiple regression analysis).・Numerically grasp the degree of impact (t value) for each element of S and G.
Y0=α*X01+β*X02+γ*X03+δ (1)
Y0: Non-financial risk ratio (explained variable)
X01: E score (explanatory variable)
X02: S score (explanatory variable)
X03: G score (explanatory variable)

FIG. 13 is a diagram exemplifying the results of multiple regression analysis (as of 2019) when using the ESG data of the fourth evaluation agency shown in FIG.
The ESG data of the target company A in the fourth evaluation agency are E score: 27.5, S score: 28.0, G score: 9.5, so the non-financial risk ratio (theoretical value) = -0. 3454 (=0.00648×27.5−0.02749×28.0+0.04573×9.5−0.18829).
For Target Company A, the non-financial risk ratio (actual value): -17.15%, while the non-financial risk ratio (theoretical value) derived by multiple regression analysis: -34.54%. Therefore, in the scoring model of the 4th evaluation agency, it is considered that the target company A is overvalued compared to the ESG data.

By performing such a multiple regression analysis for each evaluation institution, it is possible to numerically grasp the uniqueness of each evaluation institution that uses different ESG data calculation methods (that is, ESG scoring). FIG. 14 shows an output example when multiple regression analysis is performed for each evaluation agency. In the example shown in FIG. 14, the year of ESG data release (here, 2019) is set as the time of evaluation, and the case in which Japan is selected as the survey target is exemplified, but settings and changes can be made arbitrarily. In addition, the survey target may be configured so that, for example, either "global" (regardless of domestic or overseas) or "domestic" can be selected as a group of companies to be compared (see FIG. 15). In addition to performing analysis for each evaluation institution, a synthetic analysis may be performed by adding up the ESG scores of a plurality of evaluation institutions. In the case of performing a synthetic analysis, it is sufficient to select the item "Others" in the setting of evaluation agencies (see FIG. 11) and select a plurality of evaluation agencies to be synthesized.

The change calculation unit 34 obtains various parameter values for a predetermined period (three months is assumed here) before and after the date on which the specific event occurred, and compares the obtained parameter values.

FIG. 16 shows changes in each parameter value of target company A when the ESG rating of the third rating agency changes. Figure 16 illustrates a case where the ESG rating of a third rating agency changes (“BBB” → “A”) as a specific event. , corporate mergers and acquisitions, new product development, etc.) may be designated as a specific event. The user may enter information on the occurrence of specific events (for example, changes in the ESG rating of the evaluation agency, the date on which the specific event occurred, etc.) can also be entered.

The change calculation unit 34 calculates the average non-financial risk ratio for each of the three months before and after the occurrence of the specific event, and also calculates the change in the non-financial risk ratio (see A in FIG. 16). In addition to the non-financial risk ratio, the change calculation unit 34 may calculate the average of the "market capitalization" and the "limited market capitalization" for each of the three months before and after the occurrence of the specific event.

In addition, when the “parent index” is selected as a comparison target, the change calculation unit 34 calculates the average non-financial risk ratio in the “parent index” for the same period as the target company A, Calculate the change in risk ratio (see B in FIG. 16).

Furthermore, when the “company group (1 notch change)” is selected as a comparison target, the change calculation unit 34 calculates a 1 notch change (1 unit rating For the group of companies that experienced a (rating) change, calculate the average non-financial risk ratio for each of the three months before and after the occurrence of the specific event, and calculate the change in the non-financial risk ratio (see C in FIG. 16).

FIG. 17 shows a comparative example of non-financial risk ratios in 2019 between target company A and a group of companies (average) with the same ESG rating (here, "A") from the third rating agency.

In the example shown in FIG. 17, the annual average, maximum value, and minimum value of the non-financial risk ratio are exemplified as items to be compared, but the items are not limited to these.

The output unit 35 displays the analysis result by the element analysis unit 33, the calculation result by the change calculation unit 34, and the like on the display device according to the user's instruction.

As described above, according to this embodiment, ESG data disclosed by an external evaluation organization is acquired, the non-financial risk ratio of each element of E, S, and G of an individual company is estimated, and each element It is possible to numerically grasp the degree of influence in another. In addition, it is possible to confirm changes in non-financial risk ratios before and after specific events (for example, changes in ESG ratings) occur.

C. Others In each of the above-described embodiments, the term "unit" does not simply mean physical means, but also includes the case where the functions of the "unit" are implemented by software. In addition, even if the function of one "section" or device is realized by two or more physical means or devices, the functions of two or more "sections" or devices are realized by one physical means or device. can be

10, 10′ evaluation system 11 computer processor 12 main memory 13 input/output interface 14 communication interface 15 storage 20 financial information terminal 21 setting unit 22 data acquisition unit 23... Multiple regression analysis part, 24... Single regression analysis part, 25... Non-financial risk amount calculation part, 26... Output part, 31... Setting part, 32... Data acquisition part, 33... Element analysis part, 34... Change calculation part , 35 . . . output unit.

Claims (6)

A corporate value evaluation system comprising a control unit and a storage unit, wherein the control unit executes a program stored in the storage unit to
an obtaining step of obtaining ESG score data including E-score, S-score, and G-score representing respective environmental (E), social (S), and corporate governance (G) scores of the target company derived by an evaluation agency;
An analysis step of performing multiple regression analysis with the E score, S score, and G score as explanatory variables and the non-financial risk ratio as an explained variable;
an output step that outputs results of the multiple regression analysis; and an evaluation system configured to perform: In the analysis step, multiple regression analysis of the non-financial risk ratio using the ESG score data of the evaluation institution is performed using the multiple regression formula shown in formula (1),
Y0=α*X01+β*X02+γ*X03+δ (1)
Y0: Non-financial risk ratio X01: E score X02: S score X03: G score In the output step, the predicted value of the non-financial risk ratio using the ESG score data of the evaluation institution obtained by the multiple regression analysis 2. The evaluation system according to claim 1, which outputs: In the acquisition step, ESG score data for each evaluation agency specified by the user is acquired;
In the analysis step, multiple regression analysis is performed on the non-financial risk ratio using the ESG score data of each evaluation agency,
3. The evaluation system according to claim 2, wherein said outputting step outputs a predicted value of non-financial risk ratio using ESG score data of each evaluation agency. further comprising a setting step of setting the time of occurrence of the particular event;
In the analysis step, multiple regression analysis is performed on the non-financial risk ratio using the ESG score data of the evaluation institution for a predetermined period before and after the occurrence of the specific event,
3. The evaluation system according to claim 2, wherein said outputting step outputs an average non-financial risk ratio in a predetermined period before and after occurrence of said specific event obtained by said multiple regression analysis. A method for valuing an enterprise, implemented by one or more computers, comprising:
an obtaining step of obtaining ESG score data including E-score, S-score, and G-score representing respective environmental (E), social (S), and corporate governance (G) scores of the target company derived by an evaluation agency;
An analysis step of performing multiple regression analysis with the E score, S score, and G score as explanatory variables and the non-financial risk ratio as an explained variable;
and an output step of outputting the results of the multiple regression analysis. A program for evaluating enterprise value, comprising:
Acquisition processing for acquiring ESG score data including E-score, S-score, and G-score representing the environmental (E), social (S), and corporate governance (G) scores of the target company derived by the evaluation agency;
Analysis processing of performing multiple regression analysis with the E score, S score, and G score as explanatory variables and the non-financial risk ratio as an explained variable;
A program for executing an output process for outputting the results of the multiple regression analysis.

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