Title :
Training neural networks for deriving bond rating formulas
Author :
Surkan, Alvin J. ; Ying, Xingren
Author_Institution :
Nebraska Univ., Lincoln, NE, USA
Abstract :
Summary form only given. A practical technique is given for extracting simple formulas by first structuring and then training a neural network for bond rating by backpropagation and then reducing the number of needed features to a minimum. A database with 126 patterns was studied. Each pattern has seven features for characterizing bonds in terms of each company´s financial parameters. The feedforward network resulting from applying a parameter reduction technique has only one hidden unit, which is finally dependent on only two input features. The final reduced model network achieves a classification accuracy of 75% in assigning the seven distinct bond ratings. From the reduced network, a compact formula is derived for computing bond ratings. The formula depends on only two variables, which are systematically selected from the seven originally provided for training the neural network
Keywords :
computerised pattern recognition; database management systems; financial data processing; learning systems; neural nets; backpropagation; bond rating; classification accuracy; compact formula; database; feedforward network; financial parameters; neural network; parameter reduction technique; reduced model network; simple formulas; Backpropagation; Bonding; Computer science; Data mining; Feedforward systems; Management training; Neural networks; Space technology; Spatial databases; Technology management;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155488