Journal paper
Entry Year96
Journal levelSCI
Paper title (chapter)An Evolution-Based Approach with Modularized Evaluations to Forecast Financial Distress
Name of journalKnowledge Based Systems
Date of publication2006-03-00
number of chapters19
Issue No.1
Name of author (Chinese)Ping-Chen Lin
Name of author (English)Ping-Chen Lin
Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.

Keywords: Financial distress; Evolutionary computation; Particle swarm optimization; Genetic algorithm; Bankruptcy; Neural Network.
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