Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial MarketsGuido J. Deboeck Experts from the world's major financial institutions contributed to this work and have already used the newest technologies. Gives proven strategies for using neural networks, algorithms, fuzzy logic and nonlinear data analysis techniques to enhance profitability. The latest analytical breakthroughs, the impact on modern finance theory and practice, including the best ways for profitably applying them to any trading and portfolio management system, are all covered. |
Contents
Part One Trading with Neural Networks | 1 |
3 | 45 |
Sizing Up the Problem | 52 |
Predicting the Tokyo Stock Market | 66 |
Testing and Evaluation of the System | 72 |
Summary | 78 |
System Architecture | 88 |
Performance Evaluation | 96 |
A Fuzzy System for Trading the Shanghai Stock Market | 207 |
Smart Trading with FRET | 215 |
Part Four Nonlinear Dynamics and Chaos | 263 |
Nonlinear Data Analysis Techniques | 280 |
Nonlinear Dimensions of Foreign Exchange | 297 |
Part Five Risk Management and the Impact of Technology | 315 |
The Impact of Technology on Financial Markets | 329 |
The Cutting Edge of Trading Technology | 344 |
Trading U S Treasury Notes with a Portfolio of Neural | 102 |
Model Performance and Sensitivity of Results | 116 |
Summary | 122 |
Part Two Strategy Optimization with Genetic Algorithms | 131 |
Using GAs to Optimize a Trading System | 174 |
Part Three Portfolio Management Using Fuzzy Logic | 189 |
Common terms and phrases
Advanced Technology back-propagation basis points behavior chaos analysis chaotic chapter chromosome correlation dimension crossover cycles daily returns Deboeck desired output developed distribution embedding dimension error estimate evaluation example exchange rate expert system extraction Fed Funds Figure financial markets forecasting fractal dimension fuzzy logic fuzzy rules fuzzy sets Fuzzy Systems genetic algorithm hidden layer Hurst coefficient hybrid input data Klimasauskas learning linear Lyapunov exponents market hypothesis maximum drawdown measure membership functions method moving average mutation neural net neural net models neural network neuron node nonlinear dynamic optimization output vector parameters patterns performance period phase space prediction problem profitable trades programming provides ratio rescaled range rescaled range analysis sample selection short-term spreadsheet statistical stock market strange attractor structure T-note technical indicators techniques theory TOPIX trading days trading rules trading strategies trading system trend U.S. Treasury securities variables volatility weights Wong
References to this book
Neural Networks: An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickland Limited preview - 1995 |
Biologically Inspired Algorithms for Financial Modelling Anthony Brabazon,Michael O'Neill No preview available - 2006 |