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A Neural Network approach for integrating banks’ decision in shipping finance

Author

Listed:
  • Marina Maniati
  • Emeritus Sambracos
  • Sokratis Sklavos

Abstract

Forecasting refers to the process of predicting future trends by lying on data from the past. An error in forecasting can lead to significant business losses especially in banking industry where decisions are taken in a highly volatile and uncertain environment due to the dynamic changes in world economy. In this paper, we study both the effectuations of the exogenous factors in the tanker shipping-related financial market and the modulation of the credibility coefficient as an internal factor in shipping banks that may affect their decision to either increase or decrease loans within tanker shipping sector by adopting the artificial neural network technique. Within this context, we modeled a unique network that adjusts 88 macroeconomic indices to the real data of 89 shipping banks within a period of T = 5 years time. The main contribution of this study is the understanding of the relation between bias and either exogenous or unpredictable factors in the market as a key factor in the financing decision policy of a shipping bank for the forthcoming year T + 1.

Suggested Citation

  • Marina Maniati & Emeritus Sambracos & Sokratis Sklavos, 2022. "A Neural Network approach for integrating banks’ decision in shipping finance," Cogent Economics & Finance, Taylor & Francis Journals, vol. 10(1), pages 2150134-215, December.
    • Handle: RePEc:taf:oaefxx:v:10:y:2022:i:1:p:2150134
    DOI: 10.1080/23322039.2022.2150134
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