Authors :
Mukhtar Abubakar Yusuf
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3WG6k0x
DOI :
https://doi.org/10.5281/zenodo.7470972
Abstract :
:- This study examines the factors that influence
individual foreign direct investment decisions and predicts
using various Artificial Intelligence (A.I.) Algorithms models. The study also gives us an in-depth insight into the dynamics of complex FDI decisions using those A.I. predictive models. We use structural equation modeling in the
prescriptive strand. Return on Investment (ROI), Security/Personal Safety, and Investment Facilitation Services
significantly affect individual FDI decisions. On predictive
strand analysis, we used various Machine Learning models
to evaluate the accuracy of predicting classes of individual
FDI risk decisions and the ARIMA model for prediction.
We find that Random Forest and Ada Boosting Trees have
substantial classification accuracies despite the "No free
lunch" theorem. The result also indicates that a better prediction could be made by applying multiple classes of FDI
inflow decisions rather than binary classes.
Keywords :
Foreign Direct Investment, Artificial Intelligence, Investment Facilitation, Return-On-Investment, Investment Decisions, Predictive Modeling, Random Forest, Gradient Boosting
:- This study examines the factors that influence
individual foreign direct investment decisions and predicts
using various Artificial Intelligence (A.I.) Algorithms models. The study also gives us an in-depth insight into the dynamics of complex FDI decisions using those A.I. predictive models. We use structural equation modeling in the
prescriptive strand. Return on Investment (ROI), Security/Personal Safety, and Investment Facilitation Services
significantly affect individual FDI decisions. On predictive
strand analysis, we used various Machine Learning models
to evaluate the accuracy of predicting classes of individual
FDI risk decisions and the ARIMA model for prediction.
We find that Random Forest and Ada Boosting Trees have
substantial classification accuracies despite the "No free
lunch" theorem. The result also indicates that a better prediction could be made by applying multiple classes of FDI
inflow decisions rather than binary classes.
Keywords :
Foreign Direct Investment, Artificial Intelligence, Investment Facilitation, Return-On-Investment, Investment Decisions, Predictive Modeling, Random Forest, Gradient Boosting
