This ebook explores the intuitive enchantment of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in mixture with evolutionary computation outperform classical econometric strategies for accuracy in forecasting, classification and dimensionality discount. McNelis makes use of quite a lot of examples, from forecasting car manufacturing and company bond unfold, to inflation and deflation processes in Hong Kong and Japan, to bank card default in Germany to financial institution failures in Texas, to cap-floor volatilities in New York and Hong Kong. This ebook presents a balanced, vital overview of the neural community strategies and genetic algorithms used in finance; consists of quite a few examples and functions;and numerical illustrations use MATLAB code. The ebook is accompanied by an internet site.
“This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore. I recommend it to anyone who wants to understand methods used in nonlinear forecasting.” — Blake LeBaron, Professor of Finance, Brandeis University “An important addition to the select collection of books on financial econometrics, Paul Mcnelis’ volume, Neural Networks in Finance, serves as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets.” — Roberto S. Mariano, Dean of School of Economics and Social Sciences & Vice-Provost for Research, Singapore Management University; Professor Emeritus of Economics, University of Pennsylvania “This book represents an impressive step forward in the exposition and application of evolutionary computational tools. The author illustrates the potency of evolutionary computational tools through multiple examples, which contrast the predictive outcomes from the evolutionary approach with others of a linear and general non-linear variety. The book will be of utmost appeal to both academics throughout the social sciences as well as practitioners, especially in the area of finance.” — Carlos Asilis, Portfolio Manager, VegaPlus Capital Partners; previously Chief Investment Strategist, JPMorgan Chase “…an excellent, easy-to read introduction to the math behind neural networks.” – Financial Engineering News