By
Paul McNelis, Robert Bendheim Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.
Description
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used
in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and
dimensionality reduction.
McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread,
to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor
volatilities in New York and Hong Kong.
Included in series
Academic Press Advanced Finance
Audience:
Upper division undergraduates and MBA students, as well as the rapidly growing number of financial engineering programs, whose curricula
emphasize quantitative applications in financial economics and markets
