Bankruptcy Type Prediction Using A Hybrid Artificial Neural Networks Model

Abstract

Nam-ok JoㆍHyun-jung KimㆍKyung-shik Shin 80The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy.In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

Research on corporate financial performance prediction based on self‐organizing and convolutional neural networks

“…Hosaka (2019) also used convolutional neural networks for financial analysis and found it to be more accurate than traditional methods. This paper combines unsupervised and supervised learning, using combining CNN with SOM, to greatly improve the accuracy of corporate performance model prediction, which is also in line with the findings of Jo et al (2015). This study also confirms the view of Kainulainen et al (2011) that combining the right methods may lead to better results.…”

Understanding Corporate Bond Defaults in Korea Using Machine Learning Models*

“…However, corporate bond default in Korea has not been extensively studied due to a lack of comprehensive default information. This data limitation has prompted researchers to use delisting events in the Korean stock market (Kang and Cho 2011;Lee and Kim 2015;Oh et al 2017) or focus on a specific set of firms, such as those subject to an external audit (Ok and Kim 2009). For the same reason, some studies make only regime-specific arguments.…”