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Neural Networks in Finance
 
 

Neural Networks in Finance, 1st Edition

Gaining Predictive Edge in the Market

 
Neural Networks in Finance, 1st Edition,Paul McNelis,ISBN9780124859678
 
 
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Academic Press

9780124859678

9780080479651

256

229 X 152

Provides a thorough and applied view of neural networks and the genetic algorithm in finance

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Key Features

* Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance
* Includes numerous examples and applications
* Numerical illustrations use MATLAB code and the book is accompanied by a website

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.

Readership

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

Paul McNelis

Affiliations and Expertise

Robert Bendheim Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.

Neural Networks in Finance, 1st Edition

Preface
1 Introduction
1.1 Forecasting, Classification and Dimensionality Reduction
1.2 Synergies
1.3 The Interface Problems
1.4 Plan of the Book
Econometric Foundations
2 What Are Neural Networks
2.1 Linear Regression Model
2.2 GARCH Nonlinear Models
2.2.1 Polynomial Approximation
2.2.2 Orthogonal Polynomials
2.3 Model Typology
2.4 What Is A Neural Network
2.4.1 Feedforward Networks
2.4.2 Squasher Functions
2.4.3 Radial Basis Functions
2.4.4 Ridgelet Networks
2.4.5 Jump Connections
2.4.6 Multilayered Feedforward Networks
2.4.7 Recurrent Networks
2.4.8 Networks with Multiple Outputs
2.5 Neural Network Smooth-Transition Regime-Switching Models
2.5.1 Smooth Transition Regime Switching Models
2.5.2 Neural Network Extensions
2.6 Nonlinear Principal Components: \ Intrinsic Dimensionality
2.6.1 Linear Principal Components
2.6.2 Nonlinear Principal Components
2.6.3 Application to Asset Pricing
2.7 Neural Networks and Discrete Choice
2.7.1 Discriminant Analysis
2.7.2 Logit Regression
2.7.3 Probit Regression
2.7.4 Weibull Regression
2.7.5 Neural Network Models for Discrete Choice
2.7.6 Models with Multinomial Ordered Choice
Criticism and Data Mining
2.9 Conclusion
2.9.1 Matlab Program Notes
2.9.2 Suggested Exercises
3 Estimation of a Network with Evolutionary Computation
3.1 Data Preprocessing
3.1.1 Stationarity: Dickey-Fuller Test
3.1.2 Seasonal Adjustment: Correction for Calendar Effects
3.1.3 Scaling of Data
3.2 The Nonlinear Estimation Problem
3.2.1 Local Gradient-Based Search: \ The Quasi- Backpropagation 46
Simulated Annealing 48
3.2.3 Evolutionary Stochastic Search: The Genetic Algorithm
Population creation
Selection
Crossover
Mutation
Election tournament
Elitism
Convergence
3.2.4 Evolutionary Genetic Algorithms
3.2.5 Hybridization: Coupling Gradient- and Genetic Search Methods
3.3 Repeated Estimation and Thick Models
3.4 Matlab Examples: Numerical Performance 53
3.4.1 Numerical Optimization
3.4.2 Approximation with Networks 54
3.5 Conclusion
3.5.1 Matlab Program Notes
3.5.2 Suggested Exercises
4 Evaluation of Network Estimation
4.1 In-Sample Criteria
4.1.1 Goodness of Fit Measure
4.1.2 Hannan-Quinn Information Criterion
4.1.3 Serial Independence and Homoskedasticity: and McLeod-Li Tests
4.1.4 Symmetry
Normality
4.1.6 Neural Network Test for Neglected Nonlinearity: Lee-White-Granger Test
4.1.7 Brock-Deckert-Scheinkman Test for Nonlinear Patterns
4.1.8 Summary of in-sample criteria
4.1.9 Matlab Example
4.2 Out-of-Sample Criteria
4.2.1 Recursive Methodology
4.2.2 Root Mean Squared Error Statistic
4.2.3 Diebold-Mariano Test for Out of Sample Errors
4.2.4 Harvey, Leybourne, and Newbold "Size Correction" of Diebold-Mariano Test
4.2.5 Out-of-Sample Comparison with Nested Models
4.2.6 Success Ratio for Sign Predictions: Directional Accuracy
4.2.7 Predictive Stochastic\ Complexity
subsection \numberline 4.2.8 Cross-Validation and the Method 69
How Large for Predictive Accuracy
4.3 Interpretive Criteria and Significance of Results
4.3.1 Analytic Derivatives
4.3.2 Finite Differences
4.3.3 Does It Matter
4.3.4 Matlab Example: Analytic and Finite Differences
4.3.5 Bootstrapping for Assessing Significance
4.4 Implementation Strategy
4.5 Conclusion
4.5.1 Matlab Program Notes
4.5.2 Suggested Exercises
1em Applications and Examples
5 Estimation and Forecasting with Artificial Data
5.1 Introduction
5.2 Stochastic Chaos Model
5.2.1 In-Sample Performance
5.2.2 Out-of-Sample Performance
5.3 Stochastic Volatility/Jump Diffusion Model
5.3.1 In-Sample Performance
5.3.2 Out-of-Sample Performance
5.4 The Markov Regime Switching Model
5.4.1 In-Sample Performance
5.4.2 Out-of-Sample Performance
5.5 VRS Model
5.5.1 In-Sample Performance

5.6 Distorted Long Memory Model
5.6.1 In-Sample Performance
5.6.2 Out-of-Sample Performance
5.7 BSOP Model: Implied Volatility Forecasting
5.7.1 In-Sample Performance
5.7.2 Out-of-Sample Performance
5.8 Conclusion
5.8.1 Matlab Program Notes
5.8.2 Suggested Exercises
6 Times Series: Examples from Industry and Finance
6.1 Forecasting Production in the Automotive Industry
6.1.1 The Data
6.1.2 Models of Quantity Adjustment
6.1.3 In-Sample Performance
6.1.4 Out-of-Sample Performance
6.1.5 Interpretation of Results
6.2 Corporate Bonds: Which Spreads? 110
6.2.1 The Data
6.2.2 A Model for the Adjustment of Spreads
In-Sample Performance
6.2.4 Out-of-Sample Performance
6.2.5 Interpretation of Results
6.3 Conclusion
6.3.1 Matlab Program Notes
6.3.2 Suggested Exercises
7 Inflation and Deflation: Hong Kong and Japan
7.1 Hong Kong
7.1.1 The Data
7.1.2 Model Specification
7.1.3 In-Sample Performance
7.1.4 Out-of-Sample Performance
7.1.5 Interpretation of Results
7.2 Japan
7.2.1 The Data
7.2.2 Model Specification
7.2.3 In-Sample Performance
7.2.4 Out-of-Sample Performance
7.2.5 Interpretation of Results
7.3 Conclusion
7.3.1 Matlab Program Notes
7.3.2 Suggested Exercises
8 Classification: \ Credit Card Default and Bank Failures
8.1 Credit Card Risk
8.1.1 The Data
8.1.2 In-Sample Performance
8.1.3 Out-of-Sample Performance
8.1.4 Interpretation of Results

8.2 Banking Intervention
8.2.1 The Data
8.2.2 In-Sample Performance
8.2.3 Out-of-Sample Performance
8.2.4 Interpretation of Results
8.3 Conclusion
8.3.1 Matlab Program Notes
8.3.2 Suggested Exercises
9 Dimensionality Reduction and Implied Volatility Forecasting
9.1 Hong Kong
9.1.1 The Data
9.1.2 In-Sample Performance
9.1.3 Out-of-Sample Performance
9.2 United States
9.2.1 The Data
9.2.2 In-Sample Performance
9.2.3 Out-of-Sample Performance
9.3 Conclusion
9.3.1 Matlab Program Notes
9.3.2 Suggested Exercises

Quotes and reviews

"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; formerly Chief Investment Strategist, JPMorgan Chase

"...an excellent, easy-to read introduction to the math behind neural networks."
- Financial Engineering News
 
 
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