Yield spreads prediction using genetic neural network

In this paper, the authors aim at prediction of demanded yield spreads on primary bond market using biologically inspired algorithms. The researchers combine genetic algorithms and multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to present a genetic artificial neural network. Consequently it is estimated demanded yield spread of bonds based on parameters of individual offerings. The results indicate that compared to conventional types of artificial neural networks, genetic network reached the lowest mean squared error and highest determination coefficient on the investigated sample of 23 844 initial bond offerings and outperfomed other networks, primarily on out-of-sample data.

[1]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[2]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[3]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[4]  T. Wade,et al.  Self-Awareness , 2016, Review of Philosophy and Psychology.

[5]  Anil K. Bera,et al.  Efficient tests for normality, homoscedasticity and serial independence of regression residuals , 1980 .

[6]  M. J. D. Powell,et al.  Restart procedures for the conjugate gradient method , 1977, Math. Program..

[7]  D. Goldfarb.  A family of variable-metric methods derived by variational means , 1970 .

[8]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[9]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[10]  D. Shanno.  Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[11]  Celia Ross.  The Sage encyclopedia of quality and the service economy , 2016 .

[12]  Bharat A. Jain,et al.  Artificial Neural Network Models for Pricing Initial Public Offerings , 1995 .

[13]  A. Halim Boussabaine,et al.  The use of artificial neural networks in construction management: a review , 1996 .

[14]  Steven J. Robertson,et al.  Neural network models for initial public offerings , 1998, Neurocomputing.

[15]  Li-Chiu Chi,et al.  Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach , 2005, Expert Syst. Appl..

[16]  Yoichi Hayashi,et al.  Understanding consumer heterogeneity: A business intelligence application of neural networks , 2010, Knowl. Based Syst..

[17]  Eija Koskivaara.  Artificial neural network models for predicting patterns in auditing monthly balances , 1997, ECIS.

[18]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[19]  R. Fletcher,et al.  A New Approach to Variable Metric Algorithms , 1970, Comput. J..

[20]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[21]  M. Tkáč,et al.  FINANCIAL AND ECONOMIC ANALYSIS OF STEEL INDUSTRY BY MULTIVARIATE ANALYSIS , 2012 .

[22]  C. G. Broyden.  The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .

[23]  S. Shapiro,et al.  An analysis of variance test for normality ( complete samp 1 es ) t , 2007 .