Prediction of Financial Distress in Retail Companies  Using Long-Short Term Memory (LSTM)

Wahyuni Windasari; Tuti Zakiyah

doi:10.29207/resti.v9i3.6217

Wahyuni Windasari Universitas Putra Bangsa
Tuti Zakiyah Universitas Putra Bangsa

DOI: https://doi.org/10.29207/resti.v9i3.6217

Keywords: deep learning, LSTM, prediction, financial distress, retail

Abstract

Financial distress is a condition in which an entity struggles to meet its debt and operating obligations.. Financial distress can lead to bankruptcy or company closure if corrective action is not taken. This study aims to forecast financial distress in retail companies by utilizing key financial ratios, including Total Asset Turnover (TATO), Current Ratio (CR), Return on Assets (ROA), and Debt-to-Equity Ratio (DER). The analysis is based on secondary data from Indonesian retail companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. The dataset exhibited missing values and class imbalance, which were addressed using mean imputation and the Synthetic Minority Oversampling Technique (SMOTE), respectivelyTo perform predictions, a Long Short-Term Memory (LSTM) model was implemented. The integration of SMOTE contributed to enhanced detection of the minority class; however, it was accompanied by a slight reduction in overall predictive accuracy. The model demonstrated a performance accuracy of 86%, with a recall rate of 85%, a precision of 100%, and an F1-score of 92%.

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