Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Mining Implicit Knowledge to Predict Political Risk by Providing Novel Framework with Using Bayesian Network

Authors: Siavash Asadi Ghajarloo

Abstract:

Nowadays predicting political risk level of country has become a critical issue for investors who intend to achieve accurate information concerning stability of the business environments. Since, most of the times investors are layman and nonprofessional IT personnel; this paper aims to propose a framework named GECR in order to help nonexpert persons to discover political risk stability across time based on the political news and events. To achieve this goal, the Bayesian Networks approach was utilized for 186 political news of Pakistan as sample dataset. Bayesian Networks as an artificial intelligence approach has been employed in presented framework, since this is a powerful technique that can be applied to model uncertain domains. The results showed that our framework along with Bayesian Networks as decision support tool, predicted the political risk level with a high degree of accuracy.

Keywords: Bayesian Networks, Data mining, GECRframework, Predicting political risk.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1054925

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1787

References:


[1] Carl B.McGowan, Jr. & Susan E. Moeller, 2009. "A model for making foreign direct investment decisions using real variables for political and economic risk analysis," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 7(1), pages 27-44
[2] Edward Easop, Vice President Andrea Keenan, James Gillard, Assessing country risk, April 8, 2009, Copyright ┬® 2009 by A.M. Best Company, Inc.
[3] Caroline Nganga and Elizabeth Curo, Assessing extreme values in political risk estimates, IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 25, 2008
[4] J. E. Moody, Economic forecasting: challenges and neural network solutions, Proc. Of the International Symposium on Artificial Neural Networks, Hsinchu, Taiwan, 1995.
[5] Milam Aiken, Using a neural network to forecast inflation, industrial management & data systems, Publisher: MCB UP Ltd, Year:1999
[6] J.S. Ide, E.C. Colla and F.G. Cozman. Bayesian network classifiers for short period country risk forecasting. Proceedings of the Workshop em Algoritmos e AplicaçÃμes de Mineração de Dados (WAAMD), held jointly with 21st Brazilian Symposium on Databases, pp. 109-112, 2006.
[7] COLLA, Ernesto Coutinho ; IDE, Jaime Shinsuke ; COZMAN, F. G. . Bayesian network classifiers for country risk forecasting. In: Workshop on Practical Data Mining: Applications, Experiences and Challenges, 2006, Berlin. ECML/PKDD - Workshop on Practical Data Mining: Applications, Experiences and Challenges, 2006. p. 35-42.
[8] Aiken M. (1999) - Using a neural network to forecast inflation, Journal of Industrial Management & Data Systems,. Volume 99, Issue 7, Page 296-301, ...
[9] Harvey, Campbell R., Country risk components, the cost of capital, and returns in emerging markets. Available at SSRN: http://ssrn.com/abstract=620710 or doi:10.2139/ssrn.620710
[10] Erb,C. B., C. R. Harvey, and T. E. Viskanta. 1996b. Political risk, economic risk, and financial risk. Financial Analysts Journal 52 (6): 29- 46.
[11] E. Turban, Neural networks finance and investment: using artificial intelligence to improve real-world performance, McGraw-Hill, 1995.
[12] J. E. Moody, Economic forecasting: challenges and neural network solutions, Proc. Of the International Symposium on Artificial Neural Networks, Hsinchu, Taiwan, 1995.
[13] Jean-Claude Cosset and Jean Roy, The determinant of country risk ratings, Journal of International Business Studies 22 (1991), no. 1, 135- 142.
[14] D. Heckerman, A tutorial on learning with bayesian networks, Tech. Report MSR-TR- 95-06, Microsoft Research, Redmond, Washington, 1995.
[15] D. Geiger N. Friedman and M. Goldszmidt Bayesian network classifiers, Machine Learning 29 (1997), 131-163.
[16] LI Bing, Research on MNC-s Political Risk Management, International Business and Economics, Beijing, P.R.China, 2007
[17] Caroline Nganga and Elizabeth Curo, Assessing extreme values in political risk estimates, IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 25, 2008
[18] Matthias Busse and Carsten Hefeker, Political risk, institutions and foreign direct investment Hamburg Institute of International Economics (HWWA), Germany February 2006
[19] PRS Group, 2005. About ICRG: The political risk rating. Internet Posting: http://www.icrgonline.com/page.aspx?page=icrgmethods.
[20] Joop T.V.M. De Jong, A public health framework to translate risk factors related to political violence and war into multi-level preventive interventions, published in science direct journal, 31 October 2009