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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.* Offe

Finance --- Neural networks (Computer science). --- Decision making --- Data processing. --- Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Funding --- Funds --- Economics --- Currency question --- Finances --- Architecture neuronale

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Zonder onderwerpscode: financiewezen

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AA / International- internationaal --- 331.05 --- 330.3 --- 331.31 --- Econometric models --- Macroeconomics --- -339.015195 --- Economics --- Econometrics --- Mathematical models --- Econometrische analyse van de economische bewegingen en cycli. --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics. --- Economisch beleid. --- Econometric models. --- Mathematical models. --- 339.015195 --- Methode in staathuishoudkunde. Statische, dynamische economie. Modellen. Experimental economics --- Econometrische analyse van de economische bewegingen en cycli --- Economisch beleid

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Zonder onderwerpscode: economie