A Neural Network Analysis Of The Performance Of Foreign Direct Investments (Fdis) Listed On The Ghana Stock Exchange

Abstract

Globalization has liberalized international trade, expanded FDI and led to financial flows cross
the border. It has resulted in increasing competition in international trade and it is widely
accepted that the development of new technology for communication and information transfer an
enhanced international policy to reduce market barriers has accentuated the rate of FDI flow
across countries. The idea behind the paper is to do evaluation of the performance of listed FDI
stocks in Ghana based on its flexibility and responsiveness utilizing FANNC, after which the
results is compared with those from BPN based model. The test results indicate that the FANNC
require significantly lesser time to evaluate the performance of the FDI stocks than the BPN
approach. It also returned the minimum root mean square error.