ARTIFICIAL INTELLIGENCE-DRIVEN TRADE FORECASTING MODELS: ENHANCING PREDICTIVE ACCURACY IN INTERNATIONAL TRADE FLOWS AND MARKET VOLATILITY
DOI:
https://doi.org/10.5281/zenodo.17447908Keywords:
Artificial Intelligence, Predictive Accuracy, International Trade Forecasting, Market Volatility, Regression– Correlation Analysis, SWOT–SEM Hybrid Modeling, Sustainable Trade ManagementAbstract
This study investigates how the application of artificial intelligence manifests itself in traders’ perceptions of their
decision-making in the context of international trade flows. The rise of AI-driven forecasting has enabled new forms of
prediction, but the effectiveness of these models, particularly the accuracy in shaping expectations of market volatility,
is not well understood. This research aims at examining the task of forecasting trade flows based on data deriving
from statistical indicators, for instance regression outputs and other correlation measures, with the development of a
relevant model for predictive accuracy. We employ the SWOT analysis method to analyze trade strategies conducted
with international datasets, and we identify six key mechanisms of model performance, namely data integration, variable
selection, risk assessment, forecasting precision, adaptability, and robustness. Our results illustrate that from an
empirical perspective, accuracy in predictive modeling is a key positive element of sustainable trade management. The
study furthers understanding of the implications from regression analysis and correlation testing on international trade
forecasting. In this paper, a methodological and instrumental solution to the current problem of creating the most effective
predictive framework in a volatile global market is proposed
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