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E-grāmata: Neural Networks in Finance: Gaining Predictive Edge in the Market

3.71/5 (14 ratings by Goodreads)
(Robert Bendheim Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.)
  • Formāts: PDF+DRM
  • Sērija : Academic Press Advanced Finance
  • Izdošanas datums: 20-Jan-2005
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780080479651
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Neural Networks in Finance: Gaining Predictive Edge in the MarketPalielināt
  • Formāts: PDF+DRM
  • Sērija : Academic Press Advanced Finance
  • Izdošanas datums: 20-Jan-2005
  • Izdevniecība: Academic Press Inc
  • Valoda: eng
  • ISBN-13: 9780080479651
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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.

* 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

Recenzijas

"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

Papildus informācija

Provides a thorough and applied view of neural networks and the genetic algorithm in finance
Preface xi
Introduction
1(10)
Forecasting, Classification, and Dimensionality Reduction
1(3)
Synergies
4(2)
The Interface Problems
6(2)
Plan of the Book
8(3)
I Econometric Foundations
11(102)
What Are Neural Networks?
13(46)
Linear Regression Model
13(2)
GARCH Nonlinear Models
15(5)
Polynomial Approximation
17(1)
Orthogonal Polynomials
18(2)
Model Typology
20(1)
What Is A Neural Network?
21(17)
Feedforward Networks
21(3)
Squasher Functions
24(4)
Radial Basis Functions
28(1)
Ridgelet Networks
29(1)
Jump Connections
30(2)
Multilayered Feedforward Networks
32(2)
Recurrent Networks
34(2)
Networks with Multiple Outputs
36(2)
Neural Network Smooth-Transition Regime Switching Models
38(3)
Smooth-Transition Regime Switching Models
38(1)
Neural Network Extensions
39(2)
Nonlinear Principal Components: Intrinsic Dimensionality
41(8)
Linear Principal Components
42(2)
Nonlinear Principal Components
44(2)
Application to Asset Pricing
46(3)
Neural Networks and Discrete Choice
49(6)
Discriminant Analysis
49(1)
Logit Regression
50(1)
Probit Regression
51(1)
Weibull Regression
52(1)
Neural Network Models for Discrete Choice
52(1)
Models with Multinomial Ordered Choice
53(2)
The Black Box Criticism and Data Mining
55(2)
Conclusion
57(2)
MATLAB Program Notes
58(1)
Suggested Exercises
58(1)
Estimation of a Network with Evolutionary Computation
59(26)
Data Preprocessing
59(6)
Stationarity: Dickey-Fuller Test
59(2)
Seasonal Adjustment: Correction for Calendar Effects
61(3)
Data Scaling
64(1)
The Nonlinear Estimation Problem
65(12)
Local Gradient-Based Search: The Quasi-Newton Method and Backpropagation
67(3)
Stochastic Search: Simulated Annealing
70(2)
Evolutionary Stochastic Search: The Genetic Algorithm
72(3)
Evolutionary Genetic Algorithms
75(1)
Hybridization: Coupling Gradient-Descent, Stochastic, and Genetic Search Methods
75(2)
Repeated Estimation and Thick Models
77(1)
MATLAB Examples: Numerical Optimization and Network Performance
78(5)
Numerical Optimization
78(2)
Approximation with Polynomials and Neural Networks
80(3)
Conclusion
83(2)
MATLAB Program Notes
83(1)
Suggested Exercises
84(1)
Evaluation of Network Estimation
85(28)
In-Sample Criteria
85(9)
Goodness of Fit Measure
86(1)
Hannan-Quinn Information Criterion
86(1)
Serial Independence: Ljung-Box and McLeod-Li Tests
86(3)
Symmetry
89(1)
Normality
89(1)
Neural Network Test for Neglected Nonlinearity: Lee-White-Granger Test
90(1)
Brock-Deckert-Scheinkman Test for Nonlinear Patterns
91(2)
Summary of In-Sample Criteria
93(1)
MATLAB Example
93(1)
Out-of-Sample Criteria
94(10)
Recursive Methodology
95(1)
Root Mean Squared Error Statistic
96(1)
Diebold-Mariano Test for Out-of-Sample Errors
96(1)
Harvey, Leybourne, and Newbold Size Correction of Diebold-Mariano Test
97(1)
Out-of-Sample Comparison with Nested Models
98(1)
Success Ratio for Sign Predictions: Directional Accuracy
99(1)
Predictive Stochastic Complexity
100(1)
Cross-Validation and the .632 Bootstrapping Method
101(1)
Data Requirements: How Large for Predictive Accuracy?
102(2)
Interpretive Criteria and Significance of Results
104(5)
Analytic Derivatives
105(1)
Finite Differences
106(1)
Does It Matter?
107(1)
MATLAB Example: Analytic and Finite Differences
107(1)
Bootstrapping for Assessing Significance
108(1)
Implementation Strategy
109(1)
Conclusion
110(3)
MATLAB Program Notes
110(1)
Suggested Exercises
111(2)
II Applications and Examples
113(108)
Estimating and Forecasting with Artificial Data
115(30)
Introduction
115(2)
Stochastic Chaos Model
117(5)
In-Sample Performance
118(2)
Out-of-Sample Performance
120(2)
Stochastic Volatility/Jump Diffusion Model
122(3)
In-Sample Performance
123(2)
Out-of-Sample Performance
125(1)
The Markov Regime Switching Model
125(5)
In-Sample Performance
128(2)
Out-of-Sample Performance
130(1)
Volatality Regime Switching Model
130(5)
In-Sample Performance
132(1)
Out-of-Sample Performance
132(3)
Distorted Long-Memory Model
135(2)
In-Sample Performance
136(1)
Out-of-Sample Performance
137(1)
Black-Sholes Option Pricing Model: Implied Volatility Forecasting
137(5)
In-Sample Performance
140(2)
Out-of-Sample Performance
142(1)
Conclusion
142(3)
MATLAB Program Notes
142(1)
Suggested Exercises
143(2)
Times Series: Examples from Industry and Finance
145(22)
Forecasting Production in the Automotive Industry
145(11)
The Data
146(2)
Models of Quantity Adjustment
148(2)
In-Sample Performance
150(1)
Out-of-Sample Performance
151(1)
Interpretation of Results
152(4)
Corporate Bonds: Which Factors Determine the Spreads?
156(9)
The Data
157(1)
A Model for the Adjustment of Spreads
157(3)
In-Sample Performance
160(1)
Out-of-Sample Performance
160(1)
Interpretation of Results
161(4)
Conclusion
165(2)
MATLAB Program Notes
166(1)
Suggested Exercises
166(1)
Inflation and Deflation: Hong Kong and Japan
167(32)
Hong Kong
168(14)
The Data
169(5)
Model Specification
174(3)
In-Sample Performance
177(1)
Out-of-Sample Performance
177(1)
Interpretation of Results
178(4)
Japan
182(14)
The Data
184(5)
Model Specification
189(1)
In-Sample Performance
189(1)
Out-of-Sample Performance
190(1)
Interpretation of Results
191(5)
Conclusion
196(3)
MATLAB Program Notes
196(1)
Suggested Exercises
196(3)
Classification: Credit Card Default and Bank Failures
199(12)
Credit Card Risk
200(4)
The Data
200(1)
In-Sample Performance
200(2)
Out-of-Sample Performance
202(1)
Interpretation of Results
203(1)
Banking Intervention
204(5)
The Data
204(1)
In-Sample Performance
205(2)
Out-of-Sample Performance
207(1)
Interpretation of Results
208(1)
Conclusion
209(2)
MATLAB Program Notes
210(1)
Suggested Exercises
210(1)
Dimensionality Reduction and Implied Volatility Forecasting
211(10)
Hong Kong
212(4)
The Data
212(1)
In-Sample Performance
213(1)
Out-of-Sample Performance
214(2)
United States
216(3)
The Data
216(1)
In-Sample Performance
216(2)
Out-of-Sample Performance
218(1)
Conclusion
219(2)
MATLAB Program Notes
220(1)
Suggested Exercises
220(1)
Bibliography 221(12)
Index 233


By Paul D. McNelis
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97800804796512e.html
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