本研究探討機器學習技術於財務困境預測中的應用，重點聚焦於類別不平衡資料的處理與模型預測效果之比較。研究以 2969 筆公司年度觀察值為樣本，針對多種常見分類器（如 Neural Network、XGBoost、Random Forest、Extra Trees）在原始資料與SMOTE 平衡處理條件下之表現進行評估。進一步亦建構 Stacking 集成模型，以檢視其是否優於單一模型的預測效果。研究結果顯示，在樣本極度不平衡的情況下，應用 SMOTE 可顯著提升模型整體表現，特別是在召回率（Recall）、F1 分數及AUC等關鍵指標上。Stacking 集成模型進一步優於各單一模型，不僅在各項指標皆展現穩定優勢，亦能在最長達三年前的預測視窗中維持召回率與 F1 分數皆介於 0.94 至 0.96、AUC 值穩定高於 0.98的高水準表現。整體而言，本研究證實機器學習方法在財務困境預測中具備高度可行性與穩健性，顯示其在財務預測領域具備進一步發展之潛力。

This study investigates the application of machine learning techniques to financial distress prediction, with a particular focus on addressing class imbalance and evaluating model performance. A total of 2,969 firm-year observations were used to assess the performance of several commonly used classifiers (such as Neural Network, XGBoost, Random Forest, and Extra Trees) under both original and SMOTE-balanced conditions. A stacking ensemble model was further constructed to examine whether it could outperform individual classifiers.
The results show that, under highly imbalanced conditions, applying SMOTE significantly improves overall model performance, particularly in key metrics such as recall, F1-score, and AUC. The stacking ensemble further outperformed all individual models, consistently demonstrating superior and stable results. Notably, even under long-term forecasting scenarios—up to three years in advance—the models maintained high-level performance, with recall and F1-scores ranging from 0.94 to 0.96, and AUC values consistently exceeding 0.98. Overall, this study confirms the feasibility and robustness of machine learning methods in financial distress prediction, highlighting their potential for further development in the financial forecasting domain.

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