Digest Finance
 

A Neural Network Analysis of the Fixed Capital Investment Trends in Regions of the Russian Federation

Vol. 22, Iss. 3, SEPTEMBER 2017

PDF  Article PDF Version

Received: 27 January 2017

Received in revised form: 10 February 2017

Accepted: 27 February 2017

Available online: 21 September 2017

Subject Heading: INNOVATION AND INVESTMENT

JEL Classification: С15, С45, E22, R11

Pages: 258-273

https://doi.org/10.24891/el.22.3.258

Kuznetsov Yu.A. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation Kuznetsov_YuA@iee.unn.ru

Perova V.I. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation perova_vi@mail.ru

Lastochkina E.I. National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation mmep@iee.unn.ru

Importance The article considers the changes in and characteristics of the investment activities and behavior of the Russian Federation regions.
Objectives The article aims to analyze and describe the trends and characteristics of fixed capital investment behavior of the Russian Federation regions to ensure the economic growth and socio-economic development of the country and regions.
Methods We examine the regions' investment activities for the period from 2012 through 2014 using the neural modeling methodology on the basis of thirteen indicators characterizing the investment activities of the regions and defining their socio-economic development prospects. We also apply the Self Organizing Map using the STATISTICA software. Data of the Federal State Statistics Service of Russia on fixed investment by type of economic activity in the regions underlie our study.
Results The paper shows certain characteristics and peculiarities of the investment performance and behavior of the Russian Federation regions.
Conclusions and Relevance The cluster analysis of the Russian Federation regions' investment activities shows their uneven nature. The findings indicate the need for comprehensive measures to help change the structure of the investments involved and stimulate investment activity in all regions of the Russian Federation.

Keywords: investment behavior, cluster analysis, neural networks, STATISTICA software

References:

  1. Soboleva I.V. [Paradoxes of human capital measurement]. Voprosy Ekonomiki, 2009, no. 9, pp. 51–70. (In Russ.)
  2. Soboleva I. Paradoxes of the Measurement of Human Capital. Problems of Economic Transition, 2010, vol. 52, no. 11, pp. 43–70.
  3. Kuznetsov Yu.A., Michasova O.V. [Formalization of the task of identifying and analyzing the main economic growth determinants in Russia]. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo. Ser. Sotsial'nye nauki = Vestnik of Lobachevsky State University of Nizhny Novgorod. Ser. Social Sciences, 2015, no. 3, pp. 9–19. (In Russ.) URL: http://www.vestnik.unn.ru/ru/nomera?anum=9375
  4. Kuznetsov Yu.A., Umilina A.Yu. [Some features of economic growth in developing countries and a mathematical model of economic growth of the Nelson–Phelps type]. Vestnik Nizhegorodskogo universiteta im. N.I. Lobachevskogo. Ser. Sotsial'nye nauki = Vestnik of Lobachevsky State University of Nizhny Novgorod. Ser. Social Sciences, 2015, no. 4, pp. 36–44. (In Russ.) URL: http://www.vestnik-soc.unn.ru/en/nomera?anum=9471
  5. Lajili K. Embedding Human Capital into Governance Design: A Conceptual Framework. Journal of Management & Governance, 2015, vol. 19, iss. 4, pp. 741–762. URL: https://doi.org/10.1007/s10997-014-9295-8
  6. Ibarra C.A. Investment, Asset Market, and the Relative Unit Labor Cost in Mexico. Economic Change and Restructuring, 2016, vol. 49, iss. 4, pp. 339–364. URL: https://doi.org/10.1007/s10644-015-9175-5
  7. Haykin S. Neironnye seti: polnyi kurs [Neural Networks and Learning Machines]. Moscow, Vil'yams Publ., 2006, 1104 p.
  8. Baestaens D.-E., van den Berg W.-M., Wood D. Neironnye seti i finansovye rynki: prinyatie reshenii v torgovykh operatsiyakh [Neural Network Solutions for Trading in Financial Markets]. Moscow, TVP Publ., 1997, 236 p.
  9. Kruglov V.V., Borisov V.V. Iskusstvennye neironnye seti. Teoriya i praktika [Artificial neural networks. Theory and practice]. Moscow, Goryachaya liniya – Telekom Publ., 2002, 382 p.
  10. Osovskii S. Neironnye seti dlya obrabotki informatsii [Neural networks for information processing]. Moscow, Finansy i Statistika Publ., 2002, 344 p.
  11. Deboeck G.J., Kohonen T. Analiz finansovykh dannykh s pomoshch'yu samoorganizuyushchikhsya kart [Visual Explorations in Finance: With Self-Organizing Maps (Springer Finance)]. Moscow, AL'PINA Publ., 2001, 317 p.
  12. Ignat'eva E.D., Mariev O.S. [The methodology and tools of structural-functional analysis of regional development]. Ekonomika regiona = Economy of Region, 2013, no. 1, pp. 227–239. (In Russ.)
  13. Rastunkov V.S., Petrov A.K., Panov V.A. Neironnye seti. Statistica Neural Networks: Metodologiya i tekhnologiya sovremennogo analiza dannykh [Neural networks. STATISTICA Neural Networks: The methodology and technology of modern data analysis]. Moscow, Goryachaya liniya – Telekom Publ., 2008, 392 p.
  14. Medvedev V.S., Potemkin V.G. Neironnye seti. MATLAB 6 [Neural networks. MATLAB 6]. Moscow, DIALOG-MIFI Publ., 2002, 122 p.
  15. Kohonen Т. Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 1982, vol. 43, iss. 1, pp. 59–69. URL: https://doi.org/10.1007/BF00337288
  16. Kohonen T. The Self-Organizing Map. Proceedings of the Institute of Electrical and Electronics Engineers, 1990, vol. 78, no. 9, pp. 1464–1480. URL: https://doi.org/10.1109/5.58325
  17. Kohonen Т., Oja E., Simula O., Visa A.J.E., Kangas J. Engineering Applications of the Self-Organizing Map. Proceedings of Institute of Electrical and Electronics Engineers, 1996, vol. 84, iss. 10, pp. 1358–1384. URL: https://doi.org/10.1109/5.537105
  18. Rende S., Donduran M. Neighborhoods in Development: Human Development Index and Self-Organizing Maps. Social Indicators Research, 2013, vol. 110, iss. 2, pp. 721–734. URL: https://doi.org/10.1007/s11205-011-9955-x
  19. Carboni O.A., Russu P. Assessing Regional Wellbeing in Italy: An Application of Malmquist–DEA and Self-Organizing Map Neural Clustering. Social Indicators Research, 2015, vol. 122, iss. 3, pp. 677–700. URL: https://doi.org/10.1007/s11205-014-0722-7
  20. Martinetz T.M., Berkovich S.G., Schulten K.J. “Neural-Gas” Network for Vector Quantization and Its Application to Time-Series Prediction. IEEE Transactions on Neural Networks, 1993, vol. 4, iss. 4, pp. 558–569.

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