Applying Classifier Systems in Financial Distress Prediction Modeling
|關鍵字:||財務危機;財務比率;分類元系統;XCS分類元系統;Financial Distress;Financial Ratio;Classifier Systems;XCS Classifier Systems|
本研究的主要目的為應用XCS分類元系統來建置公司財務危機預警模型。由於XCSR模型(一種XCS分類元系統的延伸模型)結合了增強式學習(Reinforcement learning)與演化式計算(Evolutionary computation)，因此具備優良的預測能力。且模型中的規則對於預測的結果具可讀性，公司的利害關係人因而較容易了解預測的結果。
The prediction of financial distress is an important and active topic since it is critical to all stakeholders both internal and external to the company. Earlier studies of financial distress prediction used statistical approaches such as multiple discriminant analysis, logistic regression and probit model. Recently, however, several studies have demonstrated that artificial intelligence methodology such as neural networks (NNs), has the superior abilities on classification problems. Even though some of the studies using NNs to the prediction of financial distress have reported its usefulness, there are still several drawbacks in developing and using these models. The sensitivity of financial data would affect building an appropriate model and the learning results could not be read comprehensibly. The purpose of this paper is to propose XCS classifier systems approach and illustrate how the XCSR model (one model extended from XCS classifier systems) can be applied to financial distress. The exploitation of reinforcement learning and evolutionary computation constitutes a considerably advantage for the XCSR model to provide the superiorly predictive ability. Also, the obtained regularities are a means of easily understanding for the stakeholders of a firm. The results obtained with the XCSR model showed to be significantly superior to those obtained from the benchmark model (the logistic regression model). The XCSR model has a better accuracy, it is 86.8% accuracy compared to logistic regression model, which only has 79.9% accuracy. Moreover, the extracted regularities were discussed for the increased understanding when comparing to the logistic regression model.
|Appears in Collections:||Thesis|
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