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This article aims to find out if pre-exit financial (FP) and internationalization (IP) performance indicators can be used for predicting full de-internationalization (ceasing all export activities; CE). To achieve that, a theoretical concept focusing on the behavior of these predictors is built, and three research questions are postulated. Full de-internationalization is an under-researched topic in international business studies, while quantitative studies focusing on its predictors are especially rare. This study fills both gaps by providing population-level evidence for the theoretical concept. The dataset is composed of Estonian exporters that ceased or continued exporting in 2010–2022. IP variables focus on export scale, intensity and scope, while FP variables focus on liquidity, solvency, profitability and revenue-creation capability. The variables cover the timespan of three (pre-exit) years. To outline the significance of predictors and accuracies in the whole population and for different types of exporters, initially, logistic regression is applied, after which the prediction models are also composed with neural networks. Before CE, IP is in a gradual decline, while the bulk of this decline is concentrated shortly before the exit. Before CE, exporters are constantly liquidity- and solvency-constrained, while the problems with revenue creation and profitability are much shorter-lived. That population-level behavior is subject to substantial variation for different types of exporters, especially regarding FP. Prediction models incorporating the full set of variables achieve high accuracy; however, predictive performance declines as the time to exit increases and varies across exporter types. IP variables are more beneficial for predicting CE. The latter also serve as the main practical implications of the paper.