Abstract
Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA) and grey relational analysis (GRA). Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM) is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM) in China shows that the proposed approach has good performance.
Highlights
Financial distress is a term in corporate finance used to indicate a condition when a company has serious losses and becomes insolvent with liabilities
A novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function
We develop and implement a framework of a financial distress prediction model based on publicly available data
Summary
Financial distress is a term in corporate finance used to indicate a condition when a company has serious losses and becomes insolvent with liabilities. Sometimes financial distress can lead to bankruptcy [1]. Since the 1960s, enormous efforts have been made to construct efficient financial distress prediction models, but improving the models’ accuracy is still a challenging task. Many statistical methods such as discriminant analysis (DA) [4], logistic regression [5], and profit regression [6] have been applied to financial distress prediction in early studies [7]. A number of studies about financial distress prediction have been announced concerning support vector machine (SVM), since the SVM has better performance in nonlinear approximation and local optimal solutions
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