MODELS AND PREDICTIVE FACTORS OF FINANCIAL DISTRESS ACROSS VARIOUS INDUSTRY SECTORS: A SYSTEMATIC LITERATURE REVIEW

Abstract

Financial distress is a critical issue in accounting and finance literature, as it represents
the early stage preceding corporate bankruptcy. This study employs a Systematic
Literature Review (SLR) approach on articles published between 2019 and 2025 to
identify the most accurate prediction models across different sectors and to analyze both
financial and non-financial factors influencing financial distress. The findings indicate
that no single prediction model is universally superior across all sectors. The Grover
model demonstrates higher accuracy in the retail industry, the Zmijewski model
performs better in manufacturing and transportation sectors, while the Springate model
shows advantages in specific cases. In terms of financial factors, profitability emerges as
the most consistent indicator, followed by liquidity as a short-term protective factor and
firm size as a buffer against financial distress. Conversely, leverage and non-financial
factors such as corporate governance and credit risk exhibit inconsistent results across
studies. This research concludes that financial distress prediction models must be tailored
to sector characteristics and specific contexts. These results contribute theoretically and
offer practical recommendations for strengthening corporate early warning systems in
detecting financial difficulties.