Design and application of a wavelet neural network program for evaluation of goodwill value in corporate intellectual capital

Design and application of a wavelet neural network program for evaluation of goodwill value in corporate intellectual capital

Bei Yuan Fusheng Wang  Di Bao 

School of Management, Harbin Institute of Technology, Harbin 150001, China

School of Economic and Management, Hunan University of Science and Engineering, Yongzhou 425199, China

Corresponding Author Email:
31 October 2018
| Citation



Considering the poor applicability of existing wavelet neural network (WNN) methods in the termination decision of R&D projects, this paper applies the WNN in the evaluation of the value of goodwill in corporate intellectual capital (CIC) through computer programming. Specifically, the author designed a WNN program that combines the merits of both neural network and wavelet analysis. Then, evaluation of CIC goodwill value was elaborated in details from the aspects of wavelet transform and multi-resolution analysis, the learning algorithm and training process, as well as the non-stationary time series analysis and prediction. The comparison of the predicted curve and the original series curve shows that the WNN-based program outperformed the traditional analytical methods in the accuracy of CIC goodwill value evaluation. The research findings shed new light on the application of the WNN program in the setting of the index system for goodwill value evaluation.


 wavelet neural network (WNN), corporate intellectual capital (CIC), goodwill value

1. Introduction
2. WNN-based evaluation model for CIC goodwill value
3. Analysis and prediction of non-stationary time series
4. Conclusions

Carvalho C., Rodrigues A., Ferreira C. (2016). The recognition of goodwill and other intangible assets in business combinations–the portuguese case. Australian Accounting Review, Vol. 26, No. 1, pp. 4-20.

Casta J., Paugam L., Stolowy H. (2010). An explanation of the nature of internally generated goodwill based on aggregation of interacting assets. Economics Papers from University Paris Dauphine, Vol. 12, pp. 46-53.

Chauhan N., Ravi V., Chandra D. K. (2009). Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems with Applications, Vol. 36, No. 4, pp. 7659-7665.

Christopher X., Avleen B., Juan L. F. (2016). An online prediction framework for non-stationary time series. Statistics, No. 11, pp. 55-57+65.

Davanipoor M., Zekri M., Sheikholeslam F. (2012). Fuzzy wavelet neural network with an accelerated hybrid learning algorithm. IEEE Transactions on Fuzzy Systems, Vol. 20, No. 3, pp. 463-470.

Dhibi N., Elkefi A., Bellil W., Amar C. B. (2016). Multi-layer compression algorithm for 3D deformed mesh based on multi library wwavelet neural network architecture. Multimedia Tools & Applications, No. 3, pp. 1-19.

Efendi R., Deris M. M., Ismail Z. (2016). Implementation of fuzzy time series in forecasting of the non-stationary data. International Journal of Computational Intelligence and Applications, Vol. 15, No. 2, pp. 105-108+121.

Ergur H. S., Oysal Y. (2015). Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network. Journal of Intelligent Manufacturing, Vol. 26, No. 2, pp. 1-11.

Giovanni L., Francesco M. (2010). Goodwill write-off and financial market behaviour: an analysis of possible relationships. Advances in Incorporating Advances in International Accounting, Vol. 9, No. 5, pp. 333-339.

Linhares S., Fonte A., Martins M., Araújo F., Silveira L. (2015). Fuzzy wavelet neural network using a corr-entropy criterion for nonlinear system identification. Mathematical Problems in Engineering, Vol. 2015, No. 5, pp. 1-12.

Mikhailov L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, No. 6, pp. 365-385.

Mikhailov L. (2004). Group prioritization in the AHP by fuzzy preference programming method. Computers and Operations Research, pp. 293-301.

Paugam L. (2011). Valuation and reporting of goodwill: Theoretical and empirical issues. Dissertations & Theses–Gradworks, Vol. 1, No. 5, pp. 58-58.

Pih L. (2014). When organizations in the cultural industries seek new business models: A case study of the French online press. International Journal of Arts Management, Vol. 16, No. 3, pp. 147-54.

Porpora D. V. (2016). The meaning of culture and the culture of empiricism in American sociology. American Sociologist, Vol. 47, pp. 1-12.

Schenten D., Kracker S. G., Franco S., Klein U., Murphy M. (2016). Location patterns and location factors in cultural and creative industries. Quaestiones Geographicae, Vol. 34, No. 2, pp. 7-27.

Shoaib M., Shamseldin Y., Melville W., Khan M. (2016). Hybrid wavelet neural network approach. Springer International Publishing, No. 2, pp. 127-143.

Wen H., Stephen R. (2016). Moehrle accounting for goodwill:An academic literature review and analysis to inform the debate. Research in Accounting Regulation, Vol. 28, No. 1, pp. 11-21.

Wójcik-Jurkiewicz M. (2009). Enterprise value and goodwill. Theoretical Journal of Accounting, Vol. 53, No. 109, pp. 303-320.

Yilmaz S., Oysal Y. (2010). Fuzzy wavelet neural network models for prediction and identification of dynamical systems. IEEE Transactions on Neural Networks, Vol. 21, No. 10, pp. 1599-1609.

Zanoni A., Vernizzi S. (2014). Goodwill reduction:The competitive analysis of enterprise value. International Business Research, Vol. 11, pp. 86-92.