Financial engineering deals with analyzing, predicting, and understanding the behavior of financial markets. Neural networks have been at the forefront of financial engineering tools because of their inductive nature and their ability to model nonlinear relationships. However, financial engineering is a vast subject, and there are many unexplored topics that can still benefit from the application of neural networks and related techniques.

This special issue of the IEEE Transactions on Neural Networks will be devoted to the area of neural networks as applied to financial engineering. For comparison purposes, other techniques from machine learning, artificial intelligence, genetic algorithms, fuzzy systems, and related topics will also be considered. Tutorial/overview papers of significant value to the readers of the Transactions are also solicited, but authors need to have the topic approved by the guest editors before they prepare the manuscript that will be refereed. Some of the specific topics for the special issue are:

• financial time series prediction
• trading system design
• pricing of derivatives
• efficient computational algorithms for derivatives pricing
• reinforcement learning in finance
• bankruptcy prediction and credit risk
• asset allocation and portfolio management
• economic prediction
• interest rate modeling
• risk management
• stock valuation

All contributed and invited papers will be rigorously refereed to ensure the high quality of the results and relevance to the readership of the Transaction.

Guest Editors:

Dr. Yaser Abu-Mostafa
Caltech 136-93
Pasadena, CA 91125
USA
Email: yaser@caltech.edu

Dr. Amir Atiya
Caltech 136-93
Pasadena, CA 91125
USA
Email: amir@work.caltech.edu

Dr. Malik Magdon-Ismail
Caltech 136-93
Pasadena, CA 91125
USA
Email: malik@work.caltech.edu

Dr. Halbert White
Department of Economics, 0508
University of California, San Diego
9500 Gilman Drive
La Jolla, California 92093-0508
USA

A text version of the abstract must be received via electronic mail by June 1 2000. The email address to be used for the abstracts is: lucinda@sunoptics.caltech.edu. Six copies of the full length paper must be received by June 30, 2000 at the following address:

Miss Lucinda Acosta
Mail Code 136-93, Caltech
Pasadena, CA 91125
USA

Mauscipts must follow IEEE TNN guidelines (for information on the guidelines see Information for Authors in this issue or refer to the Transactions web site at http://www.ieee.org/nnc/pubs/tnn.