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


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Kara A.

FINANCIAL INTERNET QUARTERLY, cilt.21, sa.1, ss.15-26, 2025 (ESCI)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 21 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.2478/fiqf-2025-0002
  • Dergi Adı: FINANCIAL INTERNET QUARTERLY
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
  • Sayfa Sayıları: ss.15-26
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

The purpose of this paper is to (i) demonstrate that the behavior of the relative profit rates at f inancial crisis junctures in a dual financial system could be different than that of the other peri ods, (ii) show that relative profit rates (and their dynamics) at crisis junctures could be forecast ed 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 finan cial policy makers aiming to formulate and put in practice effective policies at crisis junctures.