English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Financial distress prediction is a significant issue that concerns stakeholders of enterprises. Due to the limitation of the samples available, most studies of financial prediction generally catagorize the financial situations of companies by whether they are bankrupt or specially treated in the stock market. In addition, the variables for prediction are determined mainly by subjective judgments. In order to overcome the influence of these limitations, this paper establishes a reduced variable system with sufficient information by rough set discernibility matrix method, and forms a progressive classification of financial situations by hierarchical clustering. These classifications are transformed as the target value of artificial neural network output layer to enhance the interpretation ability of the whole network. Combined with the inputs determined by Rough Set reduction, a neural network model is established to predict the financial distress, especially the turning point of financial degeneration.

A Study of Financial Distress Prediction based on Discernibility Matrix and ANN