A Research Review of Financial Distress Prediction

Abstract

This study claims that two key points must be addressed in order to increase the success rate and robustness of financial distress prediction: firstly, a simplified financial prediction indicator system must be established based on a correct understanding and clearly distinguish about the concept of financial distress. Secondly, we establish a reasonable and plausible financial distress prediction model. It has been established that machine learning models can enhance financial distress prediction models' predictive power to some degree, bu the outcomes of these models vary. This is mostly due to a lack of knowledge about financial distress and a poor choice of indicators for predicting financial distress. This paper makes the case that there are three distinct, dynamic phases of financial distress. The financial strain stage is the initial phase. The financial distress at this stage is reflected in the financial indicators, as there are now some challenges with loan repayment due to the slowdown in the main business's revenue growth rate, the slowdown in operating cash flow, and the drop in the current ratio. The second stage can be called the financial crisis stage. The enterprise's ability to generate income is still declining, along with the quality of revenue and turnover rate. It’s worth noting that gearing ratio, which will be approaching 50% or above at this stage. The third stage of financial distress is distress situation. In this stage, the firm’s balance sheet structure keeps getting worse, and the gearing ratio keeps rising at an unprecedented rate. In the meantime, with growing revenue-cost-expense ratios and negative profits, profitability is still declining. At this stage, at least two of the three cash flows from financing, investing, and operating operations were negative in terms of cash flow.