Neural Networks in Finance: Gaining Predictive Edge in the Market
Paul D. McNelis
Neutral Networks in Finance 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. The text shows that these networks are easy to implement and interpret once the time-honored quest for closed form solutions is reconsidered.
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. Numerical illustrations use MATLAB code and the book is accompanied by a website.
Paul McNelis is Professor of Economics, Georgetown University, Washington, DC.
年:
2005
出版:
1st
出版社:
Elsevier Academic Press
语言:
english
页:
243
ISBN 10:
0124859674
ISBN 13:
9780124859678
系列:
Academic Press Advanced Finance Series
文件:
PDF, 3.53 MB
IPFS:
,
english, 2005
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