The Business-Automated Data Economy Model Shifted Towards Sustainability, 2018 Update

Authors

  • Dumitru-Alexandru Bodislav
  • Amelia Diaconu
  • Marcela Mitriță

DOI:

https://doi.org/10.14207/ejsd.2018.v7n4p333

Abstract

This research is based on an algorithm developed for the American stock market for increasing the efficiency of closed funds, which had as secondary output a suitable and sustainable model that could be partially scaled to fit issues regarding automated decision making at government level, similar to a basic Business Intelligence solution (follows similar procedures like the workflow of IBM Cognos), which offers a solution in cutting to best suitable path for making a governmental decision, e.g.: if a country needs investment in roads infrastructure, healthcare or education, by using the principles behind this simple model you could yield the results and come to the best solution or best fitted regarding the global economic output. The model is based on companies traded on NASDAQ and LSE because they offer the best suitable cases for transparency, credible auditing and also it emulates the economic sectors that form a nationwide economy. The synergy between Big Data analysis, BI practices and processing power could lead to new business designed by investment banks and complex software developers: the business of automated decision making to reduce the paths that could be followed in developing a country or a private investment. In the following paper we highlight an update developed on a research started 8 years ago which developed into a fully operational economic model.

 

Keywords: Emerging markets, bubble, model performance, artifical intelligence, macroeconomic output.

Downloads

Download data is not yet available.

Downloads

Published

2018-10-01

How to Cite

Bodislav, D.-A., Diaconu, A., & Mitriță, M. (2018). The Business-Automated Data Economy Model Shifted Towards Sustainability, 2018 Update. European Journal of Sustainable Development, 7(4), 333. https://doi.org/10.14207/ejsd.2018.v7n4p333

Issue

Section

Articles