Mining Implicit Knowledge to Predict Political Risk by Providing Novel Framework with Using Bayesian Network
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.
[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
[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
@article{"International Journal of Business, Human and Social Sciences:50430", author = "Siavash Asadi Ghajarloo", title = "Mining Implicit Knowledge to Predict Political Risk by Providing Novel Framework with Using Bayesian Network", 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.", volume = "5", number = "2", pages = "106-8", }
