<![CDATA[South Korea’s trade agency, KOTRA, publishes annual trade data under the Ministry of Trade, Industry and Energy that reports gross domestic product (GDP), customs tariffs, an ease-of-business score, and the previous- and current-year price for each traded item, broken down by item and by country. The sheer volume of items and years this data accumulates over makes it hard to extract a meaningful signal by hand, and manual analysis every year is costly in both time and expertise. This paper implements and evaluates a multilayer-perceptron (MLP) model that learns from a single year of this large-scale data to forecast next-year item-level export prices at low computational and human cost. The deep-learning model is first compared against a conventional multivariate-regression model, where it shows a statistically better prediction error and shorter training time. Because export-price data plausibly carries time-series structure, the same task is also modeled with a fully connected MLP and with a recurrent neural network (RNN). While the recurrent model generalizes somewhat better to unseen data, the MLP achieves the stronger overall prediction accuracy on this single-year dataset. Given a longer time series in future work, a recurrent or Transformer-based deep-learning model is expected to improve export-price prediction further.]]>
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