Research of investment attractiveness based on cluster analysis

Authors

  • Dongfang Qi St Petersburg State University, 7-9, Universitetskaya nab., St Petersburg, 199034, Russian Federation
  • Vladimir M. Bure St Petersburg State University, 7-9, Universitetskaya nab., St Petersburg, 199034, Russian Federation

DOI:

https://doi.org/10.21638/11701/spbu10.2023.206

Abstract

The continued economic development of various countries or regions has resulted in increased competition in global markets, leading to a concentration of investors and skilled labour in locations with high investment attractiveness. The investment attractiveness of a given country or region is determined by its investment potential and risk, which are characterized by a combination of various significant factors.This paper seeks to develop an econometric model to estimate the amount of investment in fixed capital in a specific region, taking into consideration the linear relationship between the observed results, in order to determine the main conditions that are necessary for achieving stable and high economic growth. These conditions include the acceleration of investment activity and the implementation of major national reforms to ensure the effectiveness of the investment process. To assess the overall influence of the financial and economic indicators studied on the volume of investment, multiple regression analysis was utilized as the primary mathematical tool of the study. Furthermore, assumptions were made regarding the rank of the observations. To validate this hypothesis, a cluster analysis was conducted, grouping the observations into four clusters based on their results, depending on the volume of investment or the geographical characteristics of the region.

Keywords:

investment attractiveness, cluster analysis, hierarchical regression model, multiple regression models, correlation analysis, least squares method

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References

References

Wang Qian. Environmental regulation and foreign direct investment attractiveness: Evidence from China Provinces. Review of Development Economics, 2022, vol. 26, no. 2. https://doi.org/10.1111/rode.12871

Qi D., Bure V. M. Statistical analysis of investment attractiveness of China's regions. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2022, vol. 18, iss. 1, pp. 189-195. https://doi.org/10.21638/11701/spbu10.2022.116

Granato D. Use of principal component analysis (PCA) and hierarchical cluster analysis (HCA) for multivariate association between bioactive compounds and functional properties in foods: A critical perspective. Trends in Food Science and Technology, 2018, vol. 72, no. 72, pp. 83-90. https://doi.org/10.1016/j.tifs.2017.12.006

Bure V. M., Parilina E. M., Sedakov A. A. Applied statistics methods in R and Excel. 3 ed. St. Petersburg, Lan’ Publ., 2019, 196 p.

The World Bank. Available at: http://data.worldbank.org/indicator (accessed: January 18, 2023).

National Bureau of Statistics. Available at: http://www.stats.gov.cn/tjsj/ndsj/ (accessed: February 8, 2023). (In Chinese)

Govender P., Sivakumar V. Application of K-means and hierarchical clustering techniques for analysis of air pollution: A review (1980-2019). Atmospheric Pollution Research, 2020, vol. 11, no. 1, pp. 40-56. https://doi.org/10.1016/j.apr.2019.09.009

Olilingo F. Z., Aditya H. P., Kusuma P. How Indonesia economics works: correlation analysis of macroeconomics in 2010-2019. The Journal of Asian Finance, Economics and Business, 2020, vol. 7, no. 8, pp. 117-130.

Senthilnathan S. Usefulness of correlation analysis. SSRN Electronic Journal, 2019. https://doi.org/10.2139/ssrn.3416918

Iakushev V. P., Bure V. M., Mitrofanova O. A., Mitrofanov E. P. Theoretical foundations of probabilistic and statistical forecasting of agrometeorological risks. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2021, vol. 17, iss. 2, pp. 174-182. https://doi.org/10.21638/11701/spbu10.2021.207

Iakushev V. P., Bure V. M., Mitrofanova O. A., Mitrofanov E. P. K voprosu avtomatizatsii postroeniia variogramm v zadachakh tochnogo zemledeliia [On the issue of semivariograms constructing automation for precision agriculture problems]. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2020, vol. 16, iss. 2, pp. 177-185. https://doi.org/10.21638/11701/spbu10.2020.209 (In Russian)

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Published

2023-07-27

How to Cite

Qi, D., & Bure, V. M. (2023). Research of investment attractiveness based on cluster analysis. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 19(2), 199–211. https://doi.org/10.21638/11701/spbu10.2023.206

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Section

Computer Science