Company’s value evaluation through modifying its capital structure with bonds and stocks issuance simulation using machine learning and statistical analysis approaches

Proceeding
DOI: 10.31483/r-109423
Open Access
International Research-to-practice conference «Relevant issues of management, economics and economic security»
Creative commons logo
Published in:
International Research-to-practice conference «Relevant issues of management, economics and economic security»
Authors:
Daria I. Nazarova 1 , Natalia S. Semina 1 , Leonid R. Nikulin 1
Scientific adviser:
Iuliia S. Tsertseil1
Work direction:
Анализ и прогнозирование основных тенденций современной экономики на макро-, мезо- и микроуровне
Pages:
154-166
Received: 13 December 2023

Rating:
Article accesses:
1146
Published in:
РИНЦ
1 FSBEI of HE "Plekhanov Russian University of Economics"
For citation:
Nazarova D. I., Semina N. S., & Nikulin L. R. (2023). Company’s value evaluation through modifying its capital structure with bonds and stocks issuance simulation using machine learning and statistical analysis approaches. Relevant issues of management, economics and economic security, 154-166. Чебоксары: PH "Sreda". https://doi.org/10.31483/r-109423

Abstract

The present financial landscape is complexly woven with the interplay of financial instruments and technologies, reshaping the way companies manage their value. Financial instruments act as the cornerstone of capital structure, influencing cash flows and overall valuation. Meanwhile, financial technologies introduce innovation and efficiency via presenting unprecedented capabilities in assessing and forecasting the impact of these instruments on a company's worth. The ability to make timely decisions on resource allocation and capital raising is a key determinant of success in a rapidly evolving business landscape. Thus, the financial sector undergoes transformative changes, reshaping how companies navigate complexities and make informed decisions in an increasingly dynamic environment. The symbiosis of these elements is reshaping the future of finance, offering new avenues for creating and managing value in an interconnected world.

References

  1. 1. Berkshire Hathaway Inc. Annual Reports [Electronic resource]. – Access mode: https://www.berkshirehathaway.com/reports.html (date of application: 11.11.2023).
  2. 2. Berkshire Hathaway B (BRKb) [Electronic resource]. – Access mode: https://www.investing.com/equities/berkshire-hathaway (date of application: 12.11.2023).
  3. 3. Brazhnikov, F. Multifactor formation of company value. – 1st Edition. – Moscow: LitRes, 2021. – 230 p.
  4. 4. Khan Sh., Alghulaiakh H. ARIMA Model for Accurate Time Series Stocks Forecasting // International Journal of Advanced Computer Science and Applications. – 2020. – №7–11. – pp 524–528.
  5. 5. Mahajan S., Chen L-J, Tsai T-C. Short-Term PM2.5 Forecasting Using Exponential Smoothing Method: A Comparative Analysis // Sensors. – 2018. – №18 (10). – pp 38–53.
  6. 6. Mills T. C. Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. – 1st Edition. – London: Elsevier Inc., 2019. – 339 p.
  7. 7. Moiseev N.A. Simultaneous prediction of functionally dependent random variables by maximum likelihood estimation // Model Assisted Statistics and Applications. – 2021. – vol. 16, №2. – pp. 143–150. https://doi.org/10.3233/MAS-210526. EDN: AQHMMS
  8. 8. Siroteeva Yu. D., Shitov V.N. Shares and bonds as the main financial instruments in managing equity and debt capital // Current problems of development of socio-economic systems: theory and practice. – 2019. – №9–2. – pp 159–161.
  9. 9. sklearn.linear_model.LinearRegression [Electronic resource]. – Access mode: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html (date of application: 15.11.2023).
  10. 10. Trisnaningsih, S., Hendra, F. H. Optimization of capital structure with cost of capital as a measurement // 5th International Seminar of Research Month 2020. – 2021. – №1. – С. 252–256.
  11. 11. XGBoostRegressor [Electronic resource]. – Access mode: https://docs.getml.com/1.1.0/api/getml.predictors.XGBoostRegressor.html (date of application: 15.11.2023).

Comments(0)

When adding a comment stipulate:
  • the relevance of the published material;
  • general estimation (originality and relevance of the topic, completeness, depth, comprehensiveness of topic disclosure, consistency, coherence, evidence, structural ordering, nature and the accuracy of the examples, illustrative material, the credibility of the conclusions;
  • disadvantages, shortcomings;
  • questions and wishes to author.