@inproceedings{5fe9882371f64a9a93f00ab159b3fedb,
title = "Application of modified neural network weights' matrices explaining determinants of foreign investment patterns in the emerging markets",
abstract = "Quantitatively examining determinants of foreign direct investment (FDI) in Central and East Europe (CEE) is an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. The key tasks addressed in this research are a neural network (NN) based (i) FDI forecasting model and (ii) nonlinear evaluation of the determinants of FDI, We have explored various modified backprop NN weights' matrices and distinguished some nontraditional NN topologies. In terms of MSB and R-squared criteria, we found and checked relationship between modified NN input weights and FDI determinants weights. Results indicate that NN approaches better able to explain FDI determinants' weights than traditional regression methodologies. Our findings are preliminary but offer important and novel implications for future research in this area including more detailed comparisons across sectors as well as countries over time.",
author = "Darius Plikynas and Akbar, {Yusaf H.}",
year = "2005",
doi = "10.1007/11579427_73",
language = "English",
isbn = "3540298967",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin Heidelberg",
pages = "721--730",
editor = "Alexander Gelbukh and {\'A}lvaro Albornoz and Hugo Terashima-Mar{\'i}n",
booktitle = "MICAI 2005",
note = "4th Mexican International Conference on Artificial Intelligence, MICAI 2005 ; Conference date: 14-11-2005 Through 18-11-2005",
}