Application of modified MLP input weights’ matrices: an analysis of sectorial investment distribution in the emerging markets

This paper aims to explore the modified multilayer perceptron (MLP) input weights’ (IW) matrices relating them with the weights of the constituent input determinants. Non-traditional MLP topologies were designed, optimized and compared with other neural networks (NN) and multidimensional linear regression methods and statistically tested. The chosen NN topology directly related the MLP IW matrices with the relative contribution of each input variable. The contribution (weights) of each input variable was estimated in a non-linear manner, which is a novel approach in the investment research domain. This approach was applied to an investigation of sectorial investment distribution in emerging investment markets. To our knowledge, there is no experiment in the field that would focus on the NN mechanisms of sectorial indices (SI) weights estimation in such an experimental setting. In summary, we found apparent correlations between multivariate linear and other NN estimates (like Garson’s, Tchaban’s and SNA methods) having some new results not revealed in the previous research.