AN ARTIFICIAL INTELLIGENCE-BASED FORECASTING OF THE DYNAMICS OF RELATIVE PROFIT RATES AT A FINANCIAL CRISIS JUNCTURE: A MODEL, A CASE STUDY AND CRISIS MANAGEMENT POLICIES

Abstract

The purpose of this paper is to (i) demonstrate that the behavior of the relative profit rates at financial crisis junctures in a dual financial system could be different than that of the other periods, (ii) show that relative profit rates (and their dynamics) at crisis junctures could be forecasted with a relatively high degree of accuracy via artificial intelligence algorithms and (iii) exemplify the possibility of crisis-management policies that can smoothen the trajectory of the relative profit rates and facilitate the control of possible erratic fluctuations at the crisis junctures in such systems. We employ a series of methodological tools involving (i) statistical tests, (ii) artificial intelligence algorithms and (iii) the system dynamics simulation method to achieve the three objectives outlined in the paragraph above. The results are of practical significance to the financial policy makers aiming to formulate and put in practice effective policies at crisis junctures.