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Finance, Accounting and Business Analysis

Volume 6 Issue 2, 2024

http://faba.bg/

ISSN 2603-5324

DOI:

https://doi.org/10.37075/FABA.2024.2.10

Using Artificial Intelligence to Improve the Efficiency of the Market

Valuation Method

Stoyan Stoyanov

Department of Finance, University of National and World Economy, Sofia, Bulgaria

Info Articles

Abstract

History Article:

Submitted 15 October 2024

Revised 23 November 2024

Accepted 10 December 2024

Purpose: Advances in technology inevitably come with new

potential methods for performing already established activities.

Artificial intelligence, in turn, is one of the most talked-about

technological innovations. Its impact on the financial sphere is still

being analyzed and explored. This article examines the effect of

these tools on the established market valuation methodology. The

purpose of this paper is to show how digitalization and

improvements in the usage of new digital technologies could prove

to be useful in increasing the efficiency of already established

processes such as the selected methodology for enterprise valuation:

The Market approach. More specifically it focuses on artificial

intelligence as a tool which can be used to improve said efficiency.

Design/Methodology/Approach: The research method used in

this paper is a case study, based on a practical execution of the

chosen valuation method in three different scenarios, which differ

depending on the usage of AI technologies. All of the executions of

the methodology are timed using a stopwatch. A subsequent

comparison of results is carried out, based on the findings, and the

three executions are analyzed based on speed, accuracy of results,

relevancy of results and relevancy of peers.

Findings: The analysis displayed a concrete result, in which the AI

used, although proving to be extremely useful in shortening the

execution time of the chosen valuation method, the accuracy of the

results provided by it remained very far from the truth, as is the

relevance of the peers provided by the Artificial intelligence. This

shows that the usage of AI could be an integral part of financial

analysis in the future and could significantly improve the efficiency

of the market valuation method. However, at this point in time, it

should be used as a tool to facilitate analysis but not to replace it

altogether.

Practical Implications: In practice, this would be able to help

execute valuations significantly faster and easier than ever before,

but with the necessity of the valuator to make sure the peers provided

are relevant to the company being valuated.

Originality/Value: No similar study has been done regarding the

implications of AI in enterprise valuation methodologies and

therefore this would bring significant added value to this area of

study.

Paper Type: Case study

Keywords:

market method, artificial

intelligence, finance,

valuation, efficiency

JEL: G39

Address Correspondence:

E-mail : Stoyan.b.stoyanov@unwe.bg

Stoyan Stoyanov/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

218

INTRODUCTION

The rapid development of technology reveals a trend of necessity and dependence on it. This

dependence, in turn, leads to the need for adaptability and the use of these technologies to improve and build

on already accepted methodologies and approaches used both professionally and personally.

One area of technology that is gradually becoming an integral part of everyone's daily life is artificial

intelligence. Its effects and usefulness have been widely discussed but are currently still unclear and subject

to research and comment. The purpose of this paper is to reveal whether the use of artificial intelligence

could improve the efficiency of applying the market method to enterprise valuation. For the purpose of the

analysis, two types of artificial intelligence are considered.

Two hypotheses are considered, which are:

1) H0 - Artificial intelligence can help make market valuations significantly easier and faster.

2) H1 - Artificial intelligence could not adequately support the application of the market valuation

method.

The topic is modern and up to date, because the development of technology implies its inclusion and

use in the daily professional needs of each person. This is only possible with a thorough understanding of

the benefits and negatives of the respective technologies. Artificial intelligence is one of the most relevant

fields of development in the field of digitalization and in modern society, and new and improved benefits

related to it are constantly emerging. Because of this, the subject of this research is the effect AI has on the

use of the market valuation approach, which takes the role of the object.

The main task, which has been realized in this work, consists in the implementation of the selected

valuation model and the subsequent comparison of the obtained results in order to draw conclusions and

inferences regarding the described hypotheses.

DIGITALIZATION IN THE FINANCIAL SECTOR

The digitalization of the financial sector is a topic addressed by a number of authors. The integration

of technology into the banking and insurance sectors and its daily use by both consumers and the institutions

themselves is clear evidence of the significant benefits that technology brings to the financial sphere. It is

also important to mention the potential downsides of the technological boom, namely the "cyber" risks it

brings with it. Their importance is also noted by the authors Aleksandrova et al. (2023) who state that terms

such as "cyber security", "cyber risk", etc. are progressive entrants, across all industries, terms that are

evolving at a pace no slower than technology. In terms of artificial intelligence, they maintain that it can be

used to manage risks as well as enable rapid computing capabilities, gradually making this tool more

common in financial institutions (Aleksandrova et al. 2023).

Implementing artificial intelligence in the financial sphere has several benefits, many of which are

automation of certain tasks and facilitated analysis of markets and historical data (Bonaparte 2023). Other

authors advocate the idea that artificial intelligence could help identify risks, weaknesses in processes, etc.

(Kumar et al. 2019). This is further corroborated by authors Bahoo et al. (2024) who summarize several

benefits of artificial intelligence in the financial domain, including: forecasting systems, early warning

systems, and analysis of large data sets .

The authors described above agree around the general idea that artificial intelligence has significant

benefits for the financial sphere and the functions performed in it. Some of these benefits could also be

directly linked to methods of valuing companies, namely forecasting systems and analytics systems.

For the purpose of the study, an explanation of what constitutes a company's valuation is necessary.

This process is extremely complex because the true value of companies is defined as "hidden and invisible"

(Nenkov and Hristozov 2023). In order to determine the value of a company, it is important to understand

when a company actually creates value. Theoretical frameworks on this issue are mixed. Damodaran (2002)

views value as the set of a company's growth prospects, as well as its risk profile and the free cash flows

available to it. On the other hand, Koller et al. (2015) view it as the difference between the cash inflows a

company receives from an investment and its ability to keep its earnings constant. A third perspective on

company value views it as the benefits derived from an investment, which in turn lead to an increase in

capital and a corresponding increase in value (Miciuła et al. 2020). To summarize the above, the value of a

company should be defined as its ability to generate income, derive benefits from its activities and its ability

to manage, maintain and increase them in the future as measurement is done precisely through valuation

methods. It is important to note that the extent to which the value obtained through the models approximates

the actual value depends mainly on the quality and durability of the valuation process, as well as the

valuation approaches and methods used (Nenkov and Hristozov 2022).

The determination of value can be done in many ways, one of which is through DCF valuation

models, comparative valuation models and the like (Nenkov and Hristozov 2023). The approach chosen for

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this study is the market valuation method, which is part of the comparative models. It is also considered as

one of the most popular valuation methods, which is supported by the research of Bancel and Mitoo (2014).

Company value under this approach is a comparison of a company's stock price with that of a selected group

of similar "peer" companies (Damodaran 2006). The Corporate Finance Institute defines it as a method that

reveals the value of a company using financial metrics such as market multiples "EV/EBITDA,

EV/Revenue, P/E etc." comparing them to similar companies in the market (CFI team). Nenkov (2015)

defines them as an approach where assets are valued based on the market price of similar assets. In

international valuation standards, it is defined as a method of determining the value of an asset by comparing

it to identical or comparable assets for which price information is available (IVS 2023). This method was

chosen for the analysis because of its ability to reveal the usefulness of artificial intelligence in providing

necessary financial information, while testing its ability to provide up-to-date and accurate data that would

be useful to any valuator who put this digital tool into practice.

The use of artificial intelligence in the process of assessing the value of companies could increase the

efficiency of execution and could save significant time. An example of this is a study done by several

researchers at Harvard University who use this type of machine learning software to determine the potential

success of startups. They came to the conclusion that thanks to these software, they were able to predict with

a reasonable degree of confidence the value and potential success of these startups through a set of variables

(Ang et. al. 2022). This suggests that these and similar AI-based algorithms should be potentially useful in

other aspects of financial analysis. Something similar can be seen in a study by Hoang and Weigratz (2023),

who used a machine learning algorithm to forecast property market prices in Germany. The results of their

study showed that the models that used machine learning algorithms to predict prices came significantly

closer to the actual value of properties than using the standard linear regression model.

Taking these examples into account, it is safe to assume that considering machine learning models in

terms of improving the efficiency of financial valuations is a topic that requires consideration. For this

purpose, two artificial intelligence models are used and analyzed:

 A language model that provides information in the form of chat (OpenAI 2023)

 A platform integrating machine learning algorithms and data analytics to deliver market intelligence

(Comparables.ai 2023).

RESEARCH METHODOLOGY

The increasing use of artificial intelligence and its corresponding application in various aspects of

both finance in general and valuation models, as described in the previous section, raises the need for a

practical analysis of its effects. To this end, a detailed methodology of the study and the constraints placed

on it are constructed and described in order to maximize objective results. The results are then evaluated

based on a number of measurable criteria set in place.

For the purpose of the study, a public company was randomly selected, which is an active enterprise

and the shares of which are actively traded on the relevant stock exchange for the company. The selection

of the company was made on the basis of a lottery principle, out of 50 listed companies 1 was drawn to be

the subject of the study. The only restriction regarding the industry in which the company operates is that

credit institutions are avoided due to their specificity of activity and the specifics in their financial

information. An additional constraint placed is for the company to not be Bulgarian since, based on the

research of Nenkov (2023), the confidence of Bulgarian experts in the chosen method is not particularly

high. He notes that the reason for this is the small stock market in Bulgaria, which limits both the number

of analogues and the reliability of their multiples. The chosen company is the Hungarian pharmaceutical

company - Richter Gedeon Nyrt. Using publicly available information, three valuations of the selected

company were performed. The Market Valuation Method was applied, and the choice of analogues was

limited to 5 for each of the valuations. The financial multiples used are limited to 3, namely the

Price/Earnings (P/E) ratio, the Enterprise Value/EBITDA (EV/EBITDA) and the Enterprise

Value/Revenue (EV/Revenue). They were chosen because they most clearly represent a company's ability

to generate earnings and present an objective picture of its condition, while also being among the most

widely used valuation multiples under the chosen methodology, which is supported by the empirical

research of Bancel and Mitoo (2014). This is further supported by the research of Fernandez (2023), who

identifies them as the most relevant when valuing companies in almost any sector of the economy. The date

as of which the valuations were carried out is 31.12.2023, as this is the last completed fiscal year and the

traceability of the data is significantly more correct and facilitated. The valuation method has been applied

as follows:

1) A market valuation method performed using specialized artificial intelligence that provides market

analogs based on a given company and predefined filters. This software also provides the multiples

of these market analogues and their financial information.

Stoyan Stoyanov/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

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2) A market valuation method carried out using a chat bot type artificial intelligence. A set of

parameters were created, which include:

a. Which is the company being valuated

b. What analogues are sought in concreteness

c. A requirement to provide the maximum information necessary to implement the method.

For this purpose, the following question to the software is built: “I will perform a valuation using the

market valuation method. The company I will be valuating is Richter Gedeon Nyrt. For this purpose, I need

market analogues that are as close as possible financially to the company I have chosen. These analogues

must have similar: financial ratios, scale of operations and possibly be in similar areas of activity. Please

provide me with up to 5 market analogues that have the above characteristics. I would also like the maximum

amount of publicly available financial information to be provided for each of these peers (Total Revenue,

EBITDA, Net Profit, Cash, Debt, Enterprise value, Market capitalization, Shares outstanding) as well as

their market multiples P/E, EV/EBITDA, EV/Revenue. Let the data be limited to 31.12.2023. In tabular

form."

3) A market-based valuation method performed by applying an already established valuation

methodology where the valuator chooses a list of analogues that are as close as possible in financial

terms to the selected company, including financial data, market multiples and other information

necessary for the application of the model.

The measurement of potential efficiency improvement is based on five main criteria. These criteria

are as follows:

 Speed of execution - this criterion aims to show to what extent the use of the particular AI would

save time for the application of the Market valuation method.

 Timeliness of the information - this criterion aims to clarify whether the companies that the AI offers

as analogues are actually in the given state and to what extent there is a difference in their market

multiples, comparing the data provided by the AI against the actual market state of the company.

 Adequacy of analogues - this criterion aims to check to what extent the companies provided by the

AI can be considered as analogues. This is verified by a test of consistency of coefficients, consistency

of business area and consistency in scale.

 Adequacy of the obtained results - a criterion indicating to what extent the results obtained by the 3

methods are as close as possible to the real market value of the selected company. This is done on the

basis of a comparison of the results of the three executed point valuations with the market value of

the shares of the selected company. For the purpose of the study, it is assumed that the market value

at the time of valuation of the company coincides with its real value.

 Financial resources required - this criterion aims to verify the financial resources that would be

required by a company or an evaluator to use the relevant AI.

Timekeeping is done with a stopwatch and starts from the moment the selected web browser is opened

for use. In order to maximize objectivity, all data will be applied to the same template in MS Excel, which

is open and ready to integrate input data before the timer starts. The data for the selected company (Richter

Gedeon Nyrt.) is pre-integrated with data from the annual consolidated financial statements. Checks on the

timeliness and adequacy of the information and results are not subject to timing. Due to the involvement of

companies from different countries, the amounts presented are in US dollars for the purpose of objectivity.

RESULTS OF THE STUDY

Valuation using specialized artificial intelligence.

The time required to load and start the software is 46 seconds. The filters that are set up in the AI

include company selection and keywords to focus the search on peer companies. When a valued company

is selected, the software recommends keywords to use to find analogs. For the purpose of the valuation, 2

keywords tied to the selected company were used: 'pharmaceuticals' and 'pharmacy'. The time required to

set up the filters and load the companies is 1 minute and 26 seconds. The software provides at least 30

options for analogue companies in its free version and at least 5000 in its paid version. Companies are

selected at the discretion of the evaluator. The 5 selected analogue companies are the first 5 proposed, namely

Roche Holding AG; Merck & Co., Inc; GSK plc; Johnson and Johnson; Teva Pharmaceutical Industries

Limited (Appendix 1). A limitation of the selected software is that the free version does not provide the

ability to access the financial information of the analogue companies, forcing the collection of information

from other sources. The time taken to retrieve the required data from the respective exchanges is 23 min and

36 sec (Yahoo finance 2024). The market multiples are sourced directly from the stock exchanges and no

further calculation is required for them. Based on the market multiples and the financial data of the selected

company, intermediate market prices are derived for each of the multiples, which are subsequently weighted

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at the discretion of the valuator. The median of the market multiples of the analogue companies listed by

the specialized software is used for the valuation. The medians, the resulting intermediate prices and the

weights to the final valuation by the respective market multiples are presented in Table 1:

Table 1. Results from first methodology

P/E

EV/EBITDA

EV/Revenue

Median of each coefficient

14.10

9.80

4.39

Interim price for each coefficient

32.72

33.71

54.22

Weight of each multiple

40%

20%

Weighted price for the chosen method

37.41

Source: Personal calculations

Weighting the resulting interim market prices forms a final market valuation per share of the selected

company of $37.41. The final time to complete the valuation was 25 minutes and 48 seconds (Appendix 2).

Valuation using a "chat bot" language model AI

The time required to load and start the model is 1 minute and 15 seconds. The command input to the

AI is pre-prepared for objectivity and to remove the “writing speed” factor. Upon entering the pre-described

command into the selected language model, 5 analogues of the selected company are proposed with the

requested financial data information and market multipliers. The list of companies obtained includes Teva

Pharmaceutical Industries Limited; Hikma Pharmaceuticals PLC; Sanofi S.A.; Bayer AG; Novartis AG

(Appendix 2). The time taken for the artificial intelligence to provide the information, from the time the

query is sent until the full dataset is loaded, is 25 seconds. The data generation was done as it was set in the

prompt, in tabular form. The software allows downloading the table in ".csv" format. This greatly facilitates

the transfer of the data from the software to the prepared template. The table generated by the artificial

intelligence adjusts the columns to match the specified required information in the order it is requested.

Therefore, when the condition is set, the evaluator can set the columns that the language model should

generate for him. The total time required to transfer the data from the generated table to the already prepared

template is 12 minutes and 9 seconds. The financial coefficients provided by the AI were used and no further

calculation was performed for them. Since the template has already set formulas, no further calculation or

setting of additional formulas is needed. Based on the market multiples and the financial data of the selected

company, market prices are derived for each of the multiples, which are subsequently weighted at the

discretion of the valuator. The median of the market multiples of the analogue companies listed by the

language model is used for the valuation. The medians, the resulting intermediate prices and the weights to

the final valuation for the respective market multiples are presented in Table 2 as follows:

Table 2. Results from second methodology

P/E

EV/EBITDA

EV/Revenue

Median of each coefficient

11.60

8.10

2.33

Interim price for each coefficient

26.92

28.02

29.21

Weight of each multiple

40%

20%

Weighted price for the chosen method

27.82

Source: Personal calculations

Weighting the resulting interim market prices forms a final market valuation per share of the

selected company of $27.82. The final time to complete the valuation was 13 minutes and 49 seconds

(Appendix 3).

Valuation using a standardized methodology, without the use of AI

The standardized methodology includes an analysis of potential peer companies based on their financial

ratios. Market analogues are selected by reviewing companies operating in the same field as the selected one

(pharmaceutical industry), without limitation of the country of operation and selecting the most relevant

ones. Return on equity (ROE) and net operating margin (NOM) are the main weights in the selection. The

selected peers are Innoviva, Inc; Faes Farma, S.A.; Virbac SA; Bavarian Nordic A/S; Ipsen S.A. The time

required to analyze and select the analogue companies was 48 min and 35 sec. For the purpose of the

valuation, the financial data of the companies (Yahoo finance 2024; GFO of the analogues) were procured

and their data were plotted in the valuation template. Time required to collect and insert the financial

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information is 22 min and 21 sec. Their market multiples were calculated based on the imported data. The

template automatically calculates them, and no additional time is needed. Based on the calculated market

multiples and the financial data of the selected company, interim market prices are derived for each of the

multiples, which are subsequently weighted at the discretion of the valuator. The median of the market

multiples of the selected peer companies is used for the valuation. The medians, the resulting interim prices

and the weights for the final valuation by the respective market multiples are presented in Table 3 as follows:

Table 3. Results from third methodology

P/E

EV/EBITDA

EV/Revenue

Median of each coefficient

14.05

6.25

2.12

Interim price for each coefficient

32.60

21.83

26.62

Weight of each multiple

40%

20%

Weighted price for the chosen method

27.10

Source: Personal calculations

Weighting the resulting interim market prices forms a final market valuation per share of the selected

company of $27.1. Final time to complete the valuation was 70 minutes and 56 seconds (Appendix 4).

Measurement of potential improvements in efficiency

After reviewing the three assessments, each was evaluated using the criteria described above.

In terms of speed of execution, the second approach, using a language model, was the clear winner.

The speed of data generation and the ability to summarize the data in a table significantly shortened the time

required for valuation. The first approach is almost twice as slow, but it is important to note that its main

delay comes from the need for the software to be paid for in order to function in its fullness. The third

approach is the slowest in terms of implementation time due to the need to do a thorough analysis of the

market and all similar companies from which to sift a set of peers that have a certain level of comparability.

In terms of the timeliness of the information, the first approach cannot be evaluated since the

information acquired is from the exchanges and not from the software itself. The linguistic model, on the

other hand, provided coefficients that, although extremely close to those of the evaluated company, did not

match the current state of the analogues. For example, Hikma Pharmaceuticals PLC, according to the

artificial intelligence, has a P/E ratio=14.3, while a reference to many trading platforms as well as the

company's own reports, this ratio is equal to 26.47. Similar variances are found in other peer companies,

which calls into question the timeliness and truthfulness of the financial data provided by the model.

In terms of the adequacy of analogues, the first approach provides peers from the same industry as

the company being valued, but after reviewing for comparability, each of the companies has a significantly

larger scale of operations as well as higher returns. This, in turn, distorts the result under this approach. The

second approach provides both peers that are in the same industry as the valued company and significantly

more similar in financial terms of scale to the previous approach. Again, companies of larger scale are

present, but have comparable rates of return as well as margins, meaning that the companies can be

considered market analogues. The companies selected in the third approach are both comparable in scale,

industry and financial ratios, but the analysis and selection in turn took almost five times longer (Appendix

5).

The adequacy of the results obtained was tested based on a comparison of the price per share under

each of the approaches compared to the market price per share of the selected company as of 31.12.2023 of

$25.2. The first approach shows the largest deviation, which is largely due to the incomparability of the peers

and their multiples. The second approach shows an extremely close result to the market price per share, but

since the multipliers are distorted and not real, it cannot be fully accepted as correct. The third approach

shows an equally close price as a result, but with the actual coefficients and multipliers of the peers, it is the

only approach that passes this test.

The financial resources required to use the software are equally important. The first approach is the

most limited and requires the most significant resources to use, but since the paid version has not been tested,

its benefits are unclear. The free version shows benefits in terms of systemizing and suggesting potential

options that would facilitate the analysis when applying the standard approach. The second method, on the

other hand, does not require financial resources to provide the information, but does not provide up-to-date

and truthful information, so its benefits are also limited to systematizing and summarizing potential options

for analogues, which would save time in applying the 3rd approach. Resources required for the 3rd approach

are not considered.

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CONCLUSION

This research is based on the ever-increasing consumption of machine learning-based software and

platforms, namely artificial intelligence. Its aim is to reveal whether this software can improve the efficiency

of one of the well-established valuation methods, the Market Approach.

A thorough review of the analysis reveals an interesting picture. Artificial Intelligence speeds up the

execution of the valuation with the chosen method significantly. The data is available within seconds, and

anyone could have access to it. The set filters and requirements set by the user further ensure specificity and

systematicity in the information obtained. Platforms and software that are based on these machine learning

algorithms are largely free to use, albeit with limitations in some cases.

An examination of this topic also reveals negative aspects of artificial intelligence. It is very important

that when a person uses these tools, they are aware of what their goal is, what they want to achieve and how

they aim to achieve it. Otherwise, these tools would only further confuse their user and be a prerequisite for

serious mistakes. Another negative, which is of great importance, lies in the information that these software

products provide. Artificial intelligence, although an extremely fast and useful tool, is still not a sufficiently

reliable source of up-to-date and correct data. This is evident in the second valuation approach, where the

most important element of the valuation, namely the market multipliers, are distorted and show a favorable

result, but are a lot further from the actual result.

In conclusion, the 2nd hypothesis (H1) can be rejected because artificial intelligence could

significantly improve the efficiency of the market valuation method. At the same time, the first hypothesis

described in this paper (H0) can be accepted, although not in its completeness, because artificial intelligence

has its benefits in improving the efficiency of the process, by simultaneously reducing the required execution

time and facilitating the selection process. It can provide many and systematized different potential options

for market analogues needed to perform the analysis. It is important to note, however, that this type of

software should not be trusted for financial data to its fullest extent. These remain the responsibility of the

valuer to collect and calculate the necessary factors for the valuation. There is undoubtedly much scope for

further development of the subject and research into how it can be most effectively implemented. It is safe

to say that artificial intelligence could be an integral part of financial analysis in the future and could

significantly improve the efficiency of the market valuation method. However, at this point in time, it should

be used as a tool to facilitate analysis but not to replace it altogether.

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OpenAI, J. Achiam, S. Adler et. al. 2023. GPT-4: Technical Report. https://arxiv.org/abs/2303.08774.

https://doi.org/10.48550/arXiv.2303.08774.

Peer group financial data, ratios, market capitalization. https://finance.yahoo.com/.

APPENDIX 1. INFORMATION PROVIDED BY AI MODELS

Comparables.ai – table

Table 4. Peer group data table as per Comparables.ai

Name

Website

Industry

Employees

Founded

Roche Holding AG

https://www.roche.com/

Biotechnology

Research

97413

1896

GSK

https://www.gsk.com/en-gb/

Pharmaceutical

Manufacturing

106892

1830

Merck & Co., Inc.

Johnson and Johnson

https://www.johnsonmedsolutions.com/

Teva Pharmaceutical

Industries Limited

https://www.tevapharm.com/

Pharmaceutical

Manufacturing

24909

1901

Source: www.comparables.ai, specialized AI

Language model – Chat GPT 4.0 – tables

Table 5. Peer group data table as per GPT

Comparable

companies’

financial data

Total

Revenue

(billion $)

EBITDA

(billion

Net

Profit

(billion

Cash

(billion

Debt

(billion

Enterprise

Value

(billion $)

Market

Capitalization

(billion $)

Outstanding

(million)

Teva

Pharmaceutical

Industries

Limited

14.93

3.52

0.417

2.2

23.4

34.8

19.26

1158

Hikma

Pharmaceutical

PLC

2.88

0.871

0.19

0.287

1.15

4.85

4.4

221

Sanofi S.A.

45.37

13.14

6.21

10.56

21.87

106.15

118.92

2516

Bayer AG

53.42

11.67

4.13

4.19

39.34

91.34

53.42

982.42

Novartis AG

52.73

16.16

11.73

13.95

21.26

227.43

2265

Source: chatgpt.com, Language model AI

Stoyan Stoyanov/ Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024

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Table 6. Market multipliers data table as per Chat GPT

Company

P/E

EV/EBITDA

EV/Revenue

Teva Pharmaceutical Industries Limited

10.9

9.88

2.33

Hikma Pharmaceuticals PLC

14.3

7.8

1.84

Sanofi S.A.

11.6

8.1

2.34

Bayer AG

9.8

7.8

1.71

Novartis AG

13.2

12.6

4.32

Source: chatgpt.com, Language model AI

APPENDIX 2. MARKET MULTIPLIERS FOR THE FIRST APPROACH VALUATION

Table 7. Market multipliers of the peer group for the first approach

Company

P/E

EV/EBITDA

EV/Revenue

Richter Gedeon Nyrt.

10.93

7.74

2.13

Roche Holding AG

17.47

11.99

3.68

GSK

9.83

7.61

2.47

Merck & Co., Inc.

60.57

24.54

5.10

Johnson and Johnson

10.73

7.34

15.56

Teva Pharmaceutical Industries Limited

N/A

Arithmetic Average

24.65

12.87

6.70

Median

14.10

9.80

4.39

Source: www.comparables.ai and personal calculations of multiples

Table 8. Valuation for the first approach

Richter Gedeon Nyrt.

P/E

EV/EBITDA

EV/Revenue

Enterprise value per multiple

5 893 000 000

6 076 000 000

9 877 500 000

Total debt

150 000 000

Cash and cash equivalents

320 000 000

Market capitalization per multiple

6 063 000 000

6 246 000 000

10 047 500 000

Total shares outstanding

185 310 000

Interim price for each coefficient

32.72

33.71

54.22

Weight of each multiple

40%

20%

Weighted price per multiple

13.09

13.48

10.84

Weighted price for the chosen method

37.41

Source: Personal calculations

APPENDIX 3. MARKET MULTIPLIERS FOR THE SECOND APPROACH VALUATION

Table 9. Market multipliers of the peer group for the second approach

Richter Gedeon Nyrt.

Company

P/E

EV/EBITDA

EV/Revenue

Richter Gedeon Nyrt.

10.93

7.74

2.13

Teva Pharmaceutical Industries Limited

10.90

9.88

2.33

Hikma Pharmaceuticals PLC

14.30

7.80

1.84

Sanofi S.A.

11.60

8.10

2.34

Bayer AG

9.80

8.80

1.71

Novartis AG

13.20

12.60

4.32

Arithmetic Average

11.96

9.24

2.51

Median

11.60

8.10

2.33

Source: chatgpt.com and personal calculation of averages

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Table 10. Valuation for the second approach

Richter Gedeon Nyrt

P/E

EV/EBITDA

EV/Revenue

Enterprise value per multiple

4 818 000 000

5 022 000 000

5 242 500 000

Total debt

150 000 000

Cash and cash equivalents

320 000 000

Market capitalization per multiple

4 988 000 000

5 192 000 000

5 412 500 000

Total shares outstanding

185 310 000

Interim price for each coefficient

26.92

28.02

29.21

Weight of each multiple

40%

20%

Weighted price per multiple

10.77

11.21

5.84

Weighted price for the chosen method

27.82

Source: Personal calculations

APPENDIX 4. MARKET MULTIPLIERS FOR THE THIRD APPROACH VALUATION

Table 11. Market multipliers of the peer group and valuation for the third approach

Company

P/E

EV/EBITDA

EV/Revenue

Richter Gedeon Nyrt.

10.93

7.74

2.13

Innoviva, Inc.

5.73

5.53

4.19

Faes Farma, S.A.

10.86

7.79

2.12

Virbac SA

17.30

6.25

1.59

Bavarian Nordic A/S

14.05

5.80

1.76

Ipsen S.A.

15.05

10.69

2.87

Arithmetic Average

12.60

7.21

2.50

Median

14.05

6.25

2.12

Source: Personal calculations

Table 12. Valuation for the third approach

Richter Gedeon Nyrt.

P/E

EV/EBITDA

EV/Revenue

Enterprise value per multiple

5 871 325 924

3 875 153 192

4 762 738 048

Total debt

150 000 000

Cash and cash equivalents

320 000 000

Market capitalization per multiple

6 041 325 924

4 045 153 192

4 932 738 048

Total shares outstanding

185 310 000

Interim price for each coefficient

32.60

21.83

26.62

Weight of each multiple

40%

20%

Weighted price per multiple

13.04

8.73

5.32

Weighted price for the chosen method

27.1

Source: Personal calculations

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227

APPENDIX 5. COMPATIBILITY TESTS

Table 13. Compatibility test of the peer group for the first approach

Compatibility test

ROA

Net margin

ROE

Market cap (mln. $)

Richter Gedeon Nyrt.

14.00%

19.11%

12.00%

4 700

Roche Holding AG

12.44%

19.02%

37.86%

202 110

GSK Plc

9.31%

14.59%

38.78%

75 260

Merck & Co., Inc.

10.26%

3.76%

5.31%

276 260

Johnson and Johnson

19.50%

41.28%

49.20%

340 110

Teva Pharmaceutical Industries Limited

-1.30%

N/A

-7.60%

19 260

Source: Personal calculations

Table 14. Compatibility test of the peer group for the second approach

Compatibility test

ROA

Net margin

ROE

Market cap (mln. $)

Richter Gedeon Nyrt.

14.00%

19.11%

12.00%

4 700

Teva Pharmaceutical Industries Limited

-1.30%

N/A

-7.60%

19 260

Hikma Pharmaceutical PLC

8.46%

6.61%

8.81%

4 400

Sanofi S.A.

4.30%

11.60%

7.30%

118 920

Bayer AG

-4.04%

N/A

-8.13%

53 420

Novartis AG

8.65%

31.94%

19.83%

227 430

Source: Personal calculations

Table 15. Compatibility test of the peer group for the third approach

Compatibility test

ROA

Net margin

ROE

Market cap (mln. $)

Richter Gedeon Nyrt.

14.00%

19.11%

12.00%

4 700

Innoviva, Inc.

7.46%

33.30%

16.24%

1 030

Faes Farma, S.A.

8.46%

19.66%

14.93%

998

Virbac SA

5.45%

11.50%

10.69%

31 720

Bavarian Nordic A/S

7.85%

14.82%

10.20%

13 830

Ipsen S.A.

9.02%

19.49

17.30%

9 700

Source: Personal calculations