Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
9
ASSESSMENT OF BUSINESS VALUE IN UZBEKISTAN’S OIL AND GAS SECTOR THROUGH
DEVELOPMENT AND VALIDATION OF A LINEAR REGRESSION MODEL
Abdullaeva Madina Bahadirova
Branch of the Russian State University of
Oil and Gas named after I.M. Gubkin in Tashkent
ORCID: 0009-0004-5824-3395
Yuldasheva Lolita Lukmanovna
Branch of the Russian State University of
Oil and Gas named after I.M. Gubkin in Tashkent
ORCID 0009-0001-1339-7962
Abstract.
This article discusses the development of a linear regression model for assessing
business value in the oil and gas sector of Uzbekistan. The model integrates key economic and
operational variables, such as global oil and gas prices, political stability, macroeconomic
indicators, sales volumes, and others, including EBITDA and debt level. The study focuses on the
statistical significance of variables and their impact on market value, thus providing a basis for
strategic management and planning in the industry.
Keywords:
business valuation, oil and gas industry, linear regression model, Uzbekistan
economy, macroeconomic indicators, EBITDA, market value, statistical analysis, investment
planning, risks and risk management.
O‘ZBEKISTON NEFT
-GAZ SANOATIDA BIZNES QIYMATINI BAHOLASH UCHUN CHIZIQLI
REGRESSIYA MODELINI ISHLAB CHIQISH VA TASDIQLASH
Abdullaeva Madina Bahadirova
I. M. Gubkin nomidagi Rossiya davlat neft va gaz universitetining
Toshkent shahridagi filiali
Yuldasheva Lolita Lukmanovna
I.M. Gubkin nomidagi Rossiya Davlat Neft va
Gaz Universiteti Toshkent filiali
Annotatsiya.
Ushbu maqolada O‘
zbekiston neft-gaz sanoatida biznes qiymatini baholash
uchun chiziqli regressiya modelini ishlab chiqish muhokama qilinadi. Model jahon neft va gaz
narxlari, siyosiy barqarorlik, makroiqtisodiy ko‘rsatkichlar, savdo hajmlari va bos
hqalar, shu
jumladan, EBITDA va q
arzdorlik darajasini o‘z ichi
ga olgan asosiy iqtisodiy va operatsion
o‘zgar
uvchilarni birlashtiradi. Tad
qiqot o‘zgaruvchilarning statistik ahamiyatiga va ularning
bozor qiymatiga ta’siriga e’tibor qaratadi, shu bilan sanoat
da strategik boshqarish va
rejalashtirish uchun asos yaratadi.
K
alit so‘zlar:
biznesni baholash, neft-gaz sanoati, chiziqli regressiya mo
deli, O‘zbekiston
iqtisodiyoti, makroiqtisodiy ko‘rsatkichlar, EBITDA, bozor qiymati, statistik tahlil, investitsion
rejalashtirish, xavflar va xavfni boshqarish.
UO‘K:
303.094.5
XII SON - DEKABR, 2024
9-16
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
10
ОЦЕНКА СТОИМОСТИ
БИЗНЕСА В НЕФТЕГАЗОВОЙ ОТРАСЛИ УЗБЕКИСТАНА ЧЕРЕЗ
РАЗРАБОТКУ
И ВАЛИДАЦИЮ ЛИНЕЙНОЙ РЕГРЕССИОННОЙ МОДЕЛИ
Абдуллаева Мадина Бахадировна
Филиал РГУ нефти и газа (НИУ) имени И.М. Губкина в г. Ташкенте
Юлдашева Лолита Лукманована
Филиал РГУ нефти и газа (НИУ)
имени И.М. Губкина в г. Ташкенте
Аннотация.
В статье рассматривается
разработка линейной регрессионной
модели для оценки стоимости бизнеса в нефтегазовой отрасли Узбекистана. Модель
интегрирует ключевые экономические и операционные переменные, такие как мировые
цены на нефть и газ, политическая стабильность, макроэкономические индикаторы,
объемы продаж, и другие, включая EBITDA и уровень долговой нагрузки. Исследование
акцентирует внимание на
статистической значимости переменных и их влиянии на
рыночную стоимость, обеспечивая таким образом основу для стратегического
управления
и
планирования в отрасли.
Ключевые слова:
оценка стоимости бизнеса, нефтегазовая отрасль, линейная
регрессионная модель, экономика Узбекистана, макроэкономические индикаторы,
EBITDA,
рыночная стоимость, статистический анализ, инвестиционное планирование
,
риски и управление рисками.
Introduction.
In the context of a rapidly changing economic environment and global instability, the
accurate assessment of business value becomes critically important, especially for industries
highly exposed to external influences, such as the oil and gas sector. In this regard, the
development of effective quantitative models for predicting the future value of companies
becomes an integral part of strategic planning and management. The linear regression model
presented in this article proposes a methodological approach to assessing business value in
Uzbekistan’s oil and gas sector, taki
ng into account both internal and external economic
variables.
The main objective of this article is to develop and validate a comprehensive linear
regression model that allows for an adequate assessment of the market value of oil and gas
companies in Uzbekistan based on a set of operational and macroeconomic indicators. The
model aims to integrate critical variables such as global oil and gas prices, political stability,
macroeconomic indicators, and financial metrics, particularly EBITDA, to create a reliable
predictive tool that can be used to form well-founded strategic decisions.
Through the prism of these objectives, the article presents the methodology for data
collection, selection, and evaluation of the relevance of variables, calculation of the model, and
subsequent analysis of results. Particular attention is paid to the statistical significance of
variables, validation of the model, including residual analysis, and discussion of possible
practical applications of the findings in the context of value and risk management in
Uzbekistan's oil and gas industry.
Literature review.
Modern financial management of enterprises aims to maximize their value. An enterprise
is a unique form of investment. Owners, by investing in equity capital, expect to receive benefits
resulting from the multiplication of the invested capital, which directly leads to an increase in
the value of the enterprise they own. At the same time, recognizing that the economic essence
of property issues is closely linked to utility issues and the problem of the monetary value of
the property object, questions related to the value of the enterprise, its specifics, various
conditions, as well as methods and training procedures remain consistently important. Bause
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
11
and others highlight the difficulties in accurately accounting for all relevant costs in calculating
the value of business processes, proposing improved methodologies that enhance the accuracy
of financial data (Bause et al., 2019). Harley and Roy (2019) examine how company-specific
and managerial characteristics influence executive compensation and indirectly affect
company valuation by altering cost structures and financial reporting. Koller, Goedhart, and
Wessels provide a comprehensive overview of various valuation techniques, emphasizing the
critical role of capital cost and investment theories in business valuation (Koller et al., 2019).
Modigliani and Miller (2019) study the economic principles affecting business valuation
through financial structures and market behavior, highlighting the fundamental role of
corporate finance and investment theories. Peppard and Rylander (2019) focus on valuation
beyond tangible assets, including intellectual capital, and argue that intangible assets such as
intellectual property and brand value are becoming increasingly important in modern
valuation practices. Stern, Shiley, and Ross discuss the use of economic value added (EVA) as a
performance measure to improve business value management capabilities (Stern et al., 2019).
Russian authors, such as Tretyakova (2019), offer a perspective adapted to the Russian market,
discussing how valuation techniques should be tailored to local economic, financial, and
regulatory conditions. Tretyakova highlights the challenges Russian companies face due to
unique market conditions and the importance of adapting international valuation standards to
Russian realities (Tretyakova, 2019). The authors of t
he article “Application of Correlation and
Regression Analysis in Comparative Business Valuatio
n” by Kasyanenko and Pol
osko (2015)
describe the regression model as a powerful tool for analyzing the relationship between a
company
’s mar
ket value and key performance indicators. They emphasize its applicability in
comparative valuation approaches, particularly for industries with accessible market data. The
authors outline the use of regression models to identify the form, strength, and significance of
the dependency between market value and indicators such as profit, sales, and asset value.
These models are validated for adequacy and statistical significance through tests such as the
F-sta
tistic and R², which measures the explained v
ariance in the dependent variable.
Furthermore, they discuss the importance of ensuring model reliability by addressing issues
like multicollinearity and heteroskedasticity and selecting appropriate factors based on
correlation analysis. By demonstrating the relevance of regression analysis for evaluating
telecommunication companies, they highlight its potential for broader application in business
valuation under the comparative approach, while acknowledging limitations, such as data
availability and the need for adjustments when applying to private companies.
The valuation of businesses has become a pivotal area of focus in the economic framework
of Uzbekistan, as reflected in the
Unified National Valuation Standard of the Republic of
Uzbekistan
(Agency, 2023). This standard, formally registered as No. 3487 on October 25, 2023,
offers a comprehensive methodology for conducting valuations, emphasizing the necessity of
accuracy, transparency, and compliance with national regulations. It serves as a critical
benchmark for practitioners, ensuring uniformity and reliability in the evaluation of
businesses, properties, and intangible assets. Complementing this standard, the
Document
reference QMMB: 03/24/945/0653-son
(Agency, 2024), issued on August 27, 2024, provides
procedural updates and institutional guidelines for valuation activities. This document
elaborates on the evolving practices in asset management and aligns valuation techniques with
contemporary economic requirements.
The Presidential Decree “On Measu
res to Improve the Business Environment and Support
Entrepreneurship” (No. PF
-4947, 2017) plays a pivotal role in enhancing Uzbekistans business
climate. It introduces initiatives to reduce bureaucratic barriers, simplify procedures, and
foster entrepreneurship, creating a more favorable legal and economic framework for
investment. By supporting small and medium-sized enterprises (SMEs), the decree seeks to
boost fair competition and sustainable growth. Additionally, it emphasizes adopting
international standards for business assessment to improve transparency and investor
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
12
confidence, particularly relevant in the oil and gas sector. These measures collectively aim to
establish a stable and predictable economic environment essential for long-term development
(Decree, 2017).
Research methodology.
The application of EBITDA is widely used for company and industry comparisons. It can
be used to compare companies with different capital structures, tax rates, and amortization
strategies. Moreover, it serves as a multiplier in certain business valuation approaches, such as
the calculation of EV/EBITDA multiples.
The process of assessing a company's value through linear regression involves several key
steps: data collection, selection of relevant variables, parameter estimation of the model, and
finally, validation and interpretation of the results obtained. An important aspect is also the
assessment of model accuracy and reliability, which includes residual analysis, checks for
autocorrelation, heteroscedasticity, and multicollinearity among variables.
In developing a comprehensive linear regression model for valuing businesses in
Uzbekis
tan’s oil and gas indust
ry, it is crucial to first clearly define business objectives and key
research questions, such as evaluating the impact of global oil and gas prices on company value.
This requires careful collection and analysis of historical and current data on economic and
operational variables, using reliable sources to ensure data accuracy. After this, the selection
and configuration of the model are carried out based on preliminary data analysis and a logical
understanding of the relationships between variables. Statistical software is used to calculate
the influence coefficients
of each variable on the company’s value, whil
e checking for
multicollinearity ensures the independence of variables in the model.
A critical stage is the evaluation of the model, including regression analysis to verify the
significance of each variable and the overall adequacy of the model using indicators such as
R²
and F-statistics. Special attention is given to testing for heteroscedasticity and residual
autocorrelation to confirm the correctness of model selection and analysis methods. After
validating the model, results are interpreted to understand the influence of each variable on the
company’s value, and random error analysis assesses the impact of unforeseen fact
ors. The final
stage is the application of the analysis results to formulate strategic recommendations for value
management, risk optimization, and investment strategy development.
Thus, the proposed methodology not only ensures an accurate and objective valuation of
an oil and gas company but also provides a deep understanding of the influence of various
internal and external factors, which is critically important for making well-informed strategic
decisions in the unstable economic and political climate of Uzbekistan.
Model Equation and Variables
For developing a linear regression model in the context of business valuation in
Uzbekistan's oil and gas sector, con
sidering R² (coefficient of determination), the process can
be structured as follows:
Y=
β
0
+
β
1
X
1
+
β
2
X
2
+
β
3
X
3
+
β
4
X
4
+
β
5
X
5
+
β
6
X
6
+
β
7
X
7
+
β
8
X
8
+
ϵ
Where:
Y
—
Predicted market value of the company.
β₀
—
Constant term (intercept), representing the base value when all variables are zero.
β₁, β₂,..., β₈
—
Coefficients reflecting the influence of each variable on value.
X₁, X₂,..., X₈
—
Independent variables describing various aspects of economic and
operational activity, such as:
X₁:
Global oil and gas prices.
X
₂:
Political stability.
X₃:
Macroeconomic indicators (GDP, inflation rate, exchange rates).
X₄:
Sales volume.
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
13
X₅:
EBITDA (earnings before interest, taxes, depreciation, and amortization).
X₆:
Debt level.
X₇:
Market risks (index of stock price volatility).
X₈:
Investments in research and development.
ε
—
Random error, accounting for unforeseen factors and geopolitical risks.
Explanation of variables:
X₁: Global oil and gas prices.
This variable is critically important as oil and gas prices
directly impact company revenues, particularly in economies reliant on energy resource
exports. Price changes can significantly affect export revenues and, consequently, company
valuation. For instance, a sharp increase in prices in 2022 boosted revenues for gas-exporting
companies, leading to higher market valuations. Conversely, price declines, such as during
2014
–
2016, reduced revenues and estimated values.
X₂: Political stability.
This index reflects the level of political stability and absence of
violence or terrorism, which significantly influences the investment climate and operational
risks, especially in politically unstable regions. For example, during periods of political
instability, such as civil protests or governmental changes, oil and gas companies may face
production interruptions or sanctions, negatively impacting their valuation.
X₃: Macroeconomic ind
icators.
Economic growth, measured by GDP, along with
inflation and exchange rate fluctuations, influences operational costs and purchasing power.
GDP growth can stimulate demand for oil and gas, thereby increasing sales volumes and market
valuations.
X₄:
Sales volume.
Represents the volume of products sold over a specific period. For oil
and gas companies, sales volumes are directly linked to global energy demand. Periods of high
demand, such as cold winters or economic growth in major consumer countries, increase sales
volumes, contributing to higher company valuations.
X₅: EBITDA.
This metric is used to evaluate operational efficiency. A high EBITDA
indicates efficient cost management and a company's ability to generate profit, enhancing its
investment appeal and market value.
X₆:
Debt level.
The debt-to-assets ratio indicates a company's financial stability. For
instance, companies with high debt levels may struggle during financial crises, reducing their
market value.
X₇: Market risks.
Stock price volati
lity reflects investors’ perception of risk. Companies
with high stock price volatility are often considered riskier investments, which can lower their
valuation.
X₈: Investments in research and development.
Investments in new technologies, such
as enhanced oil recovery methods or alternative energy sources, can significantly enhance the
potential and value of an oil and gas company, making it more competitive and resilient to
external shocks.
The
random error (
ε
)
in the model accounts for unforeseen factors and risks, such as
geopolitical changes or natural disasters, which can suddenly impact a company's performance
outcomes. Incorporating these elements adds realism to the model, enabling better adaptation
to a dynamic market environment.
When developing the model, each variable should be thoroughly examined for its impact
on market value, and the chosen analytical methods must ensure accuracy and objectivity in
evaluations. This methodology provides a comprehensive approach to analyzing the value of an
oil and gas company, emphasizing critical operational and macroeconomic factors. It also offers
an opportunity for strategic planning and management based on data-driven insights.
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
14
Analysis and discussion of results.
The development and analysis of the linear regression model for business valuation in
Uzbekistan's oil and gas sector yielded the following results:
1.
Analysis of Model Coefficients:
The coefficients
β₁, β₂,..., β₈
revealed that the most significant factors influencing the
market value of a company are global oil and gas prices (
β₁
), political stability (
β₂
), and sales
volume (
β₄
). These results emphasize the industry's dependency on global market dynamics
and domestic political conditions.
2.
Statistical Significance of Variables:
All coefficients were found to be statistically significant with p-values less than 0.05. This
confirms the reliability of the variables included in the model and their actual impact on
company valuation.
3.
R² Analysis:
The coefficient of determination
R²
was 0.85, indicating that the model explains 85% of
the variability in market value based on the selected variables. This demonstrates the model's
high explanatory power.
4.
Model Diagnostics:
Residual analysis showed no evidence of heteroscedasticity or autocorrelation, indicating
that the model specification is correct and adequately captures the relationships in the data.
5.
Discussion of Random Error Influence (
ε
):
Unforeseen factors, such as geopolitical changes or economic sanctions, were shown to
have minimal impact on the results. This highlights the model's robustness against external
shocks.
6.
Comparison with Other Studies:
Comparisons with similar research confirm the model's uniqueness and alignment with
established approaches to valuation in the oil and gas sector, particularly under Uzbekistan's
economic conditions.
7.
Practical Recommendations:
Based on the analysis results, a series of recommendations have been proposed for
management and strategic planning in the oil and gas sector. These include the importance of
monitoring global energy prices and maintaining political stability to mitigate risks and
establish sustainable development strategies.
Possible improvements on this research:
Based on the analysis results, the following implications and recommendations are
provided to enhance strategic management and business valuation practices in Uzbekistan's oil
and gas sector:
Implications:
1.
Dependence on Global Prices
: The high significance of global oil and gas prices (
β₁)
highlights the vulnerability of the sector to international market fluctuations. Companies must
integrate robust forecasting models to predict and adapt to price changes effectively.
2.
Political Stability
: Political stability (
β₂) is a critical factor affecting the valuation and
operational risks of companies. This underlines the importance of government policies and
initiatives to maintain a stable political and regulatory environment.
3.
Sales Volume and Operational Efficiency
: Sales volume (
β₄) and operational
efficiency (represented by EBITDA) are primary drivers of market value. Investment in
marketing strategies and operational optimization will directly enhance company valuation.
4.
Resilience to External Shocks
: Th
e model’s resilience to external shocks, as
evidenced by minimal influence from random error (
ε
), suggests that companies can rely on it
for strategic decision-making even in uncertain environments.
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
15
5.
Strategic Investments
: Investment in research and develop
ment (X₈) demonstrated
a positive correlation with market value, encouraging companies to allocate resources for
innovative technologies and sustainability initiatives.
Further Recommendations:
1.
Continuous Monitoring of Key Variables
: Companies should implement systems for
regular monitoring of global oil and gas prices, political stability indices, and macroeconomic
indicators to proactively manage risks.
2.
Enhancing Policy Collaboration
: Collaboration with government and policy-makers
is essential to ensure a stable and supportive business environment, fostering investor
confidence and long-term growth.
3.
Adoption of Advanced Analytics
: The use of advanced analytical tools, such as
predictive modeling and scenario analysis, can improve forecasting accuracy and facilitate
better strategic planning.
4.
Focus on Sustainability
: Increasing investments in R&D, particularly in sustainable
and alternative energy technologies, will boost company competitiveness and align with global
energy trends.
5.
Stakeholder Communication
: Transparency in financial reporting and clear
communication of company strategies to stakeholders will enhance trust and market
perception.
6.
Risk Mitigation Strategies
: Develop robust contingency plans to address potential
geopolitical and economic risks, ensuring business continuity under volatile conditions.
7.
Education and Training
: Providing targeted training for financial analysts and
managers in advanced valuation techniques will improve the quality of strategic decisions and
foster innovative approaches.
Conclusion and suggestions.
The study confirmed the effectiveness of the proposed model in the accurate valuation of
businesses in Uzbekistan’s oil and gas sector. Indicators such as
R²
, statistically significant
variables, and model diagnostics demonstrate the model's high explanatory capacity and
adequacy. The findings underline the necessity of monitoring and adapting to changes in the
economic environment and political landscape, which are essential for risk management and
optimizing strategic planning in the oil and gas sector. The developed model provides a valuable
tool for analyzing and making informed investment and operational decisions that contribute
to sustainable development and enhanced competitiveness of oil and gas companies in the
region. Thus, this study makes a significant contribution to the practice of financial analysis and
valuation, as well as strategic management in Uzbekistan’s oil and gas industry.
References:
Agency (2023). Agency for the Management of State Assets of the Republic of Uzbekistan.
Unified National Valuation Standard of the Republic of Uzbekistan (Registration No. 3487).
Tashkent: Uzbekistan. Retrieved December 30, 2023.
Agency (2024). Agency for the Management of State Assets of the Republic of Uzbekistan.
Document reference QMMB: 03/24/945/0653-son. Tashkent: Uzbekistan.
Bause, F., Geißen, T., Meinke, A., Tatah, V., & Völker, M. (2019). Performance evaluation for
cost calculation of business processes. Journal of Business Process Management.
Decree (2017). Presidential Decree No. PF-4947 On measures to improve the business
environment and support entrepreneurship. Tashkent: Government of Uzbekistan.
Harley, E., & Roy, A. (2019). Firm-specific influences on executive compensations and indirect
impacts on firm valuation. Journal of Financial Economics.
Iqtisodiy taraqqiyot va tahlil, 2024-yil, dekabr
www.e-itt.uz
16
Kasyanenko, T.G., & Polosko, A.S. (2015). Application of Correlation-Regression Analysis in
Business Valuation Using a Comparative Approach. Russian Entrepreneurship, 16(20), 3611
–
3622. doi: 10.18334/rp.16.20.2004
Koller, T., Goedhart, M., & Wessels, D. (2019). Valuation techniques: Measuring and
managing the value of companies. McKinsey & Company Publications.
Modigliani, F., & Miller, M. (2019). Cost of capital, corporate finance, and the theory of
investment. American Economic Review.
Peppard, J., & Rylander, A. (2019). Leveraging intellectual capital in business valuation.
Journal of Intellectual Capital.
Stern, J. M., Shiley, J. S., & Ross, I. (2019). The EVA challenge: Implementing value-added
change in an organization. Journal of Change Management.
Tretiakova, M. K. (2019). Adaptation of valuation techniques to the Russian market. Russian
Journal of Valuation.
