Intelligent approach to timing of resources exploration in the behavior of firm using ARMAX, BPNN, OR SASVR

Abstract

We have insight into the importance of resource exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes intelligent approach using auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or segmented adaptive support vector regression (SASVR) to constitute the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In such a way, the methods we build can explain the timing of resources exploration in the behavior of firm. Meanwhile, the performance between these methods is compared quantitatively.