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Wavelets Analysis on Structural Model for Default Prediction

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Abstract

In recent years, to improve predictive ability of corporate defaults has become an important problem. In this paper, regarding on characteristics of listed companies, we sampled 100 companies according to industry types, constructed wavelet structural model, experimented with wavelet decomposition proceeds to get low frequency and high frequency sequence, built the prediction model for both sequences, and then using the prediction of future returns to reconstruct predictive returns, thus avoiding accumulated prediction process with earnings volatility of time series model, therefore enhanced the precision of default prediction. Finally we compared wavelet structural model with time series structural model based on the predictive default distance of China’s listed companies.

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Notes

  1. Fourier transform: Let \(f\in L(R)\),then fourier transform can be defined as \(\hat{{f}}(w)=\left\langle {f(x),e^{iwx}} \right\rangle =\int _{R} {f(x)\overline{e^{iwx}} } dx=\int _{R} {f(x)e^{-iwx}dx} \).

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Acknowledgments

The work was supported by the National Social Science Foundation of China under Grant No. 13CTJ004, the National Natural Science Foundation of China under Grant No. 71232003, and the fundamental research funds for the Central Universities.

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Correspondence to Lu Han.

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Han, L., Ge, R. Wavelets Analysis on Structural Model for Default Prediction. Comput Econ 50, 111–140 (2017). https://doi.org/10.1007/s10614-016-9584-1

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  • DOI: https://doi.org/10.1007/s10614-016-9584-1

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