Journal of Applied Mathematics and Decision Sciences
Volume 8 (2004), Issue 4, Pages 219-233
Improving prediction of neural networks: a study of tow financial prediction tasks
1Department of Accounting and Information Systems, Pamplin College of Bussiness, Virginia Tech, Falls Church 22043, VA, USA
2Department of Bussiness Information Technology, Pamplin College of Bussiness, Virginia Tech, Falls Church 22043, VA, USA
3Department of Accounting, Chirstopher Newport University, Newport news 23606, VA, USA
Copyright © 2004 Tarun K. Sen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.