Neural networks in finance: gaining predictive edge in the market
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier
2005
|
| Schriftenreihe: | Academic Press advanced finance series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 243 S. graph. Darst. |
| ISBN: | 0124859674 9780124859678 |
Internformat
MARC
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| 245 | 1 | 0 | |a Neural networks in finance |b gaining predictive edge in the market |c Paul D. McNelis |
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2005 | |
| 300 | |a XV, 243 S. |b graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804137974684712960 |
|---|---|
| adam_text | Contents
Preface xi
1 Introduction 1
1.1 Forecasting, Classification, and Dimensionality
Reduction................................................ 1
1.2 Synergies................................................ 4
1.3 The Interface Problems................................... 6
1.4 Plan of the Book ........................................ 8
1 Econometric Foundations 11
2 What Are Neural Networks? 13
2.1 Linear Regression Model................................. 13
2.2 GARCH Nonlinear Models.................................. 15
2.2.1 Polynomial Approximation.......................... 17
2.2.2 Orthogonal Polynomials............................ 18
2.3 Model Typology.......................................... 20
2.4 What Is A Neural Network?............................... 21
2.4.1 Feedforward Networks.............................. 21
2.4.2 Squasher Functions................................ 24
2.4.3 Radial Basis Functions............................ 28
2.4.4 Ridgelet Networks................................. 29
2.4.5 Jump Connections.................................. 30
2.4.6 Multilayered Feedforward Networks................. 32
vi Contents
2.4.7 Recurrent Networks................................ 34
2.4.8 Networks with Multiple Outputs.................... 36
2.5 Neural Network Smooth-Transition Regime Switching
Models................................................... 38
2.5.1 Smooth-Transition Regime Switching Models ... 38
2.5.2 Neural Network Extensions......................... 39
2.6 Nonlinear Principal Components: Intrinsic
Dimensionality........................................... 41
2.6.1 Linear Principal Components....................... 42
2.6.2 Nonlinear Principal Components.................... 44
2.6.3 Application to Asset Pricing...................... 46
2.7 Neural Networks and Discrete Choice...................... 49
2.7.1 Discriminant Analysis............................. 49
2.7.2 Logit. Regression................................. 50
2.7.3 Probit Regression................................. 51
2.7.4 Weibull Regression ............................... 52
2.7.5 Neural Network Models for Discrete Choice .... 52
2.7.6 Models with Multinomial Ordered Choice............ 53
2.8 The Black Box Criticism and Data Mining ........ 55
2.9 Conclusion............................................... 57
2.9.1 MATLAB Program Notes.............................. 58
2.9.2 Suggested Exercises............................... 58
3 Estimation of a Network with Evolutionary Computation 59
3.1 Data Preprocessing....................................... 59
3.1.1 Stationarity: Dickey-Fuller Test.................. 59
3.1.2 Seasonal Adjustment: Correction for Calendar
Effects........................................... 61
3.1.3 Data Scaling...................................... 64
3.2 The Nonlinear Estimation Problem ........................ 65
3.2.1 Local Gradient-Based Search: The Quasi-Newton
Method and Backpropagation ....................... 67
3.2.2 Stochastic Search: Simulated Annealing............ 70
3.2.3 Evolutionary Stochastic Search: The Genetic
Algorithm......................................... 72
3.2.4 Evolutionary Genetic Algorithms................... 75
3.2.5 Hybridization: Coupling Gradient-Descent,
Stochastic, and Genetic Search Methods............ 75
3.3 Repeated Estimation and Thick Models..................... 77
3.4 MATLAB Examples: Numerical Optimization and
Network Performance...................................... 78
3.4.1 Numerical Optimization............................ 78
3.4.2 Approximation with Polynomials and
Neural Networks................................... 80
Contents vii
3.5 Conclusion............................................... 83
3.5.1 MATLAB Program Notes............................... 83
3.5.2 Suggested Exercises................................ 84
4 Evaluation of Network Estimation 85
4.1 In-Sample Criteria................................... 85
4.1.1 Goodness of Fit Measure............................ 86
4.1.2 Hannan-Quinn Information Criterion ................ 86
4.1.3 Serial Independence: Ljung-Box and McLeod-Li
Tests.............................................. 86
4.1.4 Symmetry........................................... 89
4.1.5 Normality.......................................... 89
4.1.6 Neural Network Test for Neglected Nonlinearity:
Lee-White-Granger Test ............................ 90
4.1.7 Brock-Deckert-Scheinkman Test for Nonlinear
Patterns........................................... 91
4.1.8 Summary of In-Sample Criteria...................... 93
4.1.9 MATLAB Example..................................... 93
4.2 Out-of-Sample Criteria................................... 94
4.2.1 Recursive Methodology . ........................... 95
4.2.2 Root Mean Squared Error Statistic.................. 96
4.2.3 Diebold-Mariano Test for Out-of-Sample Errors . . 96
4.2.4 Harvey, Ley bourne, and Newbold Size Correction
of Diebold-Mariano Test ........................... 97
4.2.5 Out-of-Sample Comparison with Nested Models . . 98
4.2.6 Success Ratio for Sign Predictions: Directional
Accuracy........................................... 99
4.2.7 Predictive Stochastic Complexity.................. 100
4.2.8 Cross-Validation and the .632 Bootstrapping
Method............................................ 101
4.2.9 Data Requirements: How Large for Predictive
Accuracy?........................................ 102
4.3 Interpretive Criteria and Significance of Results....... 104
4.3.1 Analytic Derivatives.............................. 105
4.3.2 Finite Differences............................... 106
4.3.3 Does It Matter?................................... 107
4.3.4 MATLAB Example: Analytic and Finite
Differences....................................... 107
4.3.5 Bootstrapping for Assessing Significance.......... 108
4.4 Implementation Strategy................................. 109
4.5 Conclusion.............................................. 110
4.5.1 MATLAB Program Notes.............................. 110
4.5.2 Suggested Exercises............................... Ill
Vlll
Contents
II Applications and Examples 113
5 Estimating and Forecasting with Artificial Data 115
5.1 Introduction........................................... 115
5.2 Stochastic Chaos Model................................. 117
5.2.1 In-Sample Performance........................... 118
5.2.2 Out-of-Sample Performance....................... 120
5.3 Stochastic Volatility/Jump Diffusion Model............. 122
5.3.1 In-Sample Performance........................... 123
5.3.2 Out-of-Sample Performance....................... 125
5.4 The Markov Regime Switching Model...................... 125
5.4.1 In-Sample Performance........................... 128
5.4.2 Out-of-Sample Performance ...................... 130
5.5 Volatality Regime Switching Model...................... 130
5.5.1 In-Sample Performance........................... 132
5.5.2 Out-of-Sample Performance....................... 132
5.6 Distorted Long-Memory Model.......................... 135
5.6.1 In-Sample Performance.......................... 136
5.6.2 Out-of-Sample Performance....................... 137
5.7 Black-Sholes Option Pricing Model: Implied Volatility
Forecasting............................................ 137
5.7.1 In-Sample Performance........................... 140
5.7.2 Out-of-Sample Performance....................... 142
5.8 Conclusion............................................. 142
5.8.1 MATLAB Program Notes............................ 142
5.8.2 Suggested Exercises............................. 143
6 Times Series: Examples from Industry and Finance 145
6.1 Forecasting Production in the Automotive Industry . . . 145
6.1.1 The Data........................................ 146
6.1.2 Models of Quantity Adjustment................... 148
6.1.3 In-Sample Performance.......................... 150
6.1.4 Out-of-Sample Performance....................... 151
6.1.5 Interpretation of Results....................... 152
6.2 Corporate Bonds: Which Factors Determine the
Spreads?............................................... 156
6.2.1 The Data........................................ 157
6.2.2 A Model for the Adjustment of Spreads........... 157
6.2.3 In-Sample Performance........................... 160
6.2.4 Out-of-Sample Performance....................... 160
6.2.5 Interpretation of Results....................... 161
Contents ix
6.3 Conclusion............................................... 165
6.3.1 MATLAB Program Notes.............................. 166
6.3.2 Suggested Exercises............................... 166
7 Inflation and Deflation: Hong Kong and Japan 167
7.1 Hong Kong................................................ 168
7.1.1 The Data.......................................... 169
7.1.2 Model Specification............................... 174
7.1.3 In-Sample Performance............................. 177
7.1.4 Out-of-Sample Performance......................... 177
7.1.5 Interpretation of Results......................... 178
7.2 Japan ................................................... 182
7.2.1 The Data.......................................... 184
7.2.2 Model Specification............................... 189
7.2.3 In-Sample Performance............................. 189
7.2.4 Out-of-Sample Performance......................... 190
7.2.5 Interpretation of Results......................... 191
7.3 Conclusion............................................... 196
7.3.1 MATLAB Program Notes.............................. 196
7.3.2 Suggested Exercises............................... 196
8 Classification: Credit Card Default and Bank Failures 199
8.1 Credit Card Risk....................................... 200
8.1.1 The Data.......................................... 200
8.1.2 In-Sample Performance............................. 200
8.1.3 Out-of-Sample Performance......................... 202
8.1.4 Interpretation of Results......................... 203
8.2 Banking Intervention..................................... 204
8.2.1 The Data.......................................... 204
8.2.2 In-Sample Performance............................. 205
8.2.3 Out-of-Sample Performance......................... 207
8.2.4 Interpretation of Results......................... 208
8.3 Conclusion............................................... 209
8.3.1 MATLAB Program Notes ............................. 210
8.3.2 Suggested Exercises............................... 210
9 Dimensionality Reduction and Implied Volatility
Forecasting 211
9.1 Hong Kong................................................ 212
9.1.1 The Data.......................................... 212
9.1.2 In-Sample Performance............................. 213
9.1.3 Out-of-Sample Performance......................... 214
x Contents
9.2 United States ........................................ 216
9.2.1 The Data........................................ 216
9.2.2 In-Sample Performance........................... 216
9.2.3 Out-of-Sample Performance....................... 218
9.3 Conclusion........................................... 219
9.3.1 MATLAB Program Notes............................ 220
9.3.2 Suggested Exercises............................. 220
Bibliography 221
Index
233
|
| adam_txt |
Contents
Preface xi
1 Introduction 1
1.1 Forecasting, Classification, and Dimensionality
Reduction. 1
1.2 Synergies. 4
1.3 The Interface Problems. 6
1.4 Plan of the Book . 8
1 Econometric Foundations 11
2 What Are Neural Networks? 13
2.1 Linear Regression Model. 13
2.2 GARCH Nonlinear Models. 15
2.2.1 Polynomial Approximation. 17
2.2.2 Orthogonal Polynomials. 18
2.3 Model Typology. 20
2.4 What Is A Neural Network?. 21
2.4.1 Feedforward Networks. 21
2.4.2 Squasher Functions. 24
2.4.3 Radial Basis Functions. 28
2.4.4 Ridgelet Networks. 29
2.4.5 Jump Connections. 30
2.4.6 Multilayered Feedforward Networks. 32
vi Contents
2.4.7 Recurrent Networks. 34
2.4.8 Networks with Multiple Outputs. 36
2.5 Neural Network Smooth-Transition Regime Switching
Models. 38
2.5.1 Smooth-Transition Regime Switching Models . 38
2.5.2 Neural Network Extensions. 39
2.6 Nonlinear Principal Components: Intrinsic
Dimensionality. 41
2.6.1 Linear Principal Components. 42
2.6.2 Nonlinear Principal Components. 44
2.6.3 Application to Asset Pricing. 46
2.7 Neural Networks and Discrete Choice. 49
2.7.1 Discriminant Analysis. 49
2.7.2 Logit. Regression. 50
2.7.3 Probit Regression. 51
2.7.4 Weibull Regression . 52
2.7.5 Neural Network Models for Discrete Choice . 52
2.7.6 Models with Multinomial Ordered Choice. 53
2.8 The Black Box Criticism and Data Mining . 55
2.9 Conclusion. 57
2.9.1 MATLAB Program Notes. 58
2.9.2 Suggested Exercises. 58
3 Estimation of a Network with Evolutionary Computation 59
3.1 Data Preprocessing. 59
3.1.1 Stationarity: Dickey-Fuller Test. 59
3.1.2 Seasonal Adjustment: Correction for Calendar
Effects. 61
3.1.3 Data Scaling. 64
3.2 The Nonlinear Estimation Problem . 65
3.2.1 Local Gradient-Based Search: The Quasi-Newton
Method and Backpropagation . 67
3.2.2 Stochastic Search: Simulated Annealing. 70
3.2.3 Evolutionary Stochastic Search: The Genetic
Algorithm. 72
3.2.4 Evolutionary Genetic Algorithms. 75
3.2.5 Hybridization: Coupling Gradient-Descent,
Stochastic, and Genetic Search Methods. 75
3.3 Repeated Estimation and Thick Models. 77
3.4 MATLAB Examples: Numerical Optimization and
Network Performance. 78
3.4.1 Numerical Optimization. 78
3.4.2 Approximation with Polynomials and
Neural Networks. 80
Contents vii
3.5 Conclusion. 83
3.5.1 MATLAB Program Notes. 83
3.5.2 Suggested Exercises. 84
4 Evaluation of Network Estimation 85
4.1 In-Sample Criteria. 85
4.1.1 Goodness of Fit Measure. 86
4.1.2 Hannan-Quinn Information Criterion . 86
4.1.3 Serial Independence: Ljung-Box and McLeod-Li
Tests. 86
4.1.4 Symmetry. 89
4.1.5 Normality. 89
4.1.6 Neural Network Test for Neglected Nonlinearity:
Lee-White-Granger Test . 90
4.1.7 Brock-Deckert-Scheinkman Test for Nonlinear
Patterns. 91
4.1.8 Summary of In-Sample Criteria. 93
4.1.9 MATLAB Example. 93
4.2 Out-of-Sample Criteria. 94
4.2.1 Recursive Methodology . . 95
4.2.2 Root Mean Squared Error Statistic. 96
4.2.3 Diebold-Mariano Test for Out-of-Sample Errors . . 96
4.2.4 Harvey, Ley bourne, and Newbold Size Correction
of Diebold-Mariano Test . 97
4.2.5 Out-of-Sample Comparison with Nested Models . . 98
4.2.6 Success Ratio for Sign Predictions: Directional
Accuracy. 99
4.2.7 Predictive Stochastic Complexity. 100
4.2.8 Cross-Validation and the .632 Bootstrapping
Method. 101
4.2.9 Data Requirements: How Large for Predictive
Accuracy?. 102
4.3 Interpretive Criteria and Significance of Results. 104
4.3.1 Analytic Derivatives. 105
4.3.2 Finite Differences. 106
4.3.3 Does It Matter?. 107
4.3.4 MATLAB Example: Analytic and Finite
Differences. 107
4.3.5 Bootstrapping for Assessing Significance. 108
4.4 Implementation Strategy. 109
4.5 Conclusion. 110
4.5.1 MATLAB Program Notes. 110
4.5.2 Suggested Exercises. Ill
Vlll
Contents
II Applications and Examples 113
5 Estimating and Forecasting with Artificial Data 115
5.1 Introduction. 115
5.2 Stochastic Chaos Model. 117
5.2.1 In-Sample Performance. 118
5.2.2 Out-of-Sample Performance. 120
5.3 Stochastic Volatility/Jump Diffusion Model. 122
5.3.1 In-Sample Performance. 123
5.3.2 Out-of-Sample Performance. 125
5.4 The Markov Regime Switching Model. 125
5.4.1 In-Sample Performance. 128
5.4.2 Out-of-Sample Performance . 130
5.5 Volatality Regime Switching Model. 130
5.5.1 In-Sample Performance. 132
5.5.2 Out-of-Sample Performance. 132
5.6 Distorted Long-Memory Model. 135
5.6.1 In-Sample Performance. 136
5.6.2 Out-of-Sample Performance. 137
5.7 Black-Sholes Option Pricing Model: Implied Volatility
Forecasting. 137
5.7.1 In-Sample Performance. 140
5.7.2 Out-of-Sample Performance. 142
5.8 Conclusion. 142
5.8.1 MATLAB Program Notes. 142
5.8.2 Suggested Exercises. 143
6 Times Series: Examples from Industry and Finance 145
6.1 Forecasting Production in the Automotive Industry . . . 145
6.1.1 The Data. 146
6.1.2 Models of Quantity Adjustment. 148
6.1.3 In-Sample Performance. 150
6.1.4 Out-of-Sample Performance. 151
6.1.5 Interpretation of Results. 152
6.2 Corporate Bonds: Which Factors Determine the
Spreads?. 156
6.2.1 The Data. 157
6.2.2 A Model for the Adjustment of Spreads. 157
6.2.3 In-Sample Performance. 160
6.2.4 Out-of-Sample Performance. 160
6.2.5 Interpretation of Results. 161
Contents ix
6.3 Conclusion. 165
6.3.1 MATLAB Program Notes. 166
6.3.2 Suggested Exercises. 166
7 Inflation and Deflation: Hong Kong and Japan 167
7.1 Hong Kong. 168
7.1.1 The Data. 169
7.1.2 Model Specification. 174
7.1.3 In-Sample Performance. 177
7.1.4 Out-of-Sample Performance. 177
7.1.5 Interpretation of Results. 178
7.2 Japan . 182
7.2.1 The Data. 184
7.2.2 Model Specification. 189
7.2.3 In-Sample Performance. 189
7.2.4 Out-of-Sample Performance. 190
7.2.5 Interpretation of Results. 191
7.3 Conclusion. 196
7.3.1 MATLAB Program Notes. 196
7.3.2 Suggested Exercises. 196
8 Classification: Credit Card Default and Bank Failures 199
8.1 Credit Card Risk. 200
8.1.1 The Data. 200
8.1.2 In-Sample Performance. 200
8.1.3 Out-of-Sample Performance. 202
8.1.4 Interpretation of Results. 203
8.2 Banking Intervention. 204
8.2.1 The Data. 204
8.2.2 In-Sample Performance. 205
8.2.3 Out-of-Sample Performance. 207
8.2.4 Interpretation of Results. 208
8.3 Conclusion. 209
8.3.1 MATLAB Program Notes . 210
8.3.2 Suggested Exercises. 210
9 Dimensionality Reduction and Implied Volatility
Forecasting 211
9.1 Hong Kong. 212
9.1.1 The Data. 212
9.1.2 In-Sample Performance. 213
9.1.3 Out-of-Sample Performance. 214
x Contents
9.2 United States . 216
9.2.1 The Data. 216
9.2.2 In-Sample Performance. 216
9.2.3 Out-of-Sample Performance. 218
9.3 Conclusion. 219
9.3.1 MATLAB Program Notes. 220
9.3.2 Suggested Exercises. 220
Bibliography 221
Index
233 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | McNelis, Paul D. 1947- |
| author_GND | (DE-588)129243744 |
| author_facet | McNelis, Paul D. 1947- |
| author_role | aut |
| author_sort | McNelis, Paul D. 1947- |
| author_variant | p d m pd pdm |
| building | Verbundindex |
| bvnumber | BV035040264 |
| classification_rvk | QP 700 |
| ctrlnum | (OCoLC)634961381 (DE-599)HBZHT014339879 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035040264 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:52:56Z |
| indexdate | 2024-07-09T21:20:49Z |
| institution | BVB |
| isbn | 0124859674 9780124859678 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016709098 |
| oclc_num | 634961381 |
| open_access_boolean | |
| owner | DE-945 DE-384 |
| owner_facet | DE-945 DE-384 |
| physical | XV, 243 S. graph. Darst. |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Elsevier |
| record_format | marc |
| series2 | Academic Press advanced finance series |
| spelling | McNelis, Paul D. 1947- Verfasser (DE-588)129243744 aut Neural networks in finance gaining predictive edge in the market Paul D. McNelis Amsterdam [u.a.] Elsevier 2005 XV, 243 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Academic Press advanced finance series Finanzierungstheorie (DE-588)4154418-3 gnd rswk-swf Schätztheorie (DE-588)4121608-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Finanzierungstheorie (DE-588)4154418-3 s Neuronales Netz (DE-588)4226127-2 s Schätztheorie (DE-588)4121608-8 s DE-604 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016709098&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McNelis, Paul D. 1947- Neural networks in finance gaining predictive edge in the market Finanzierungstheorie (DE-588)4154418-3 gnd Schätztheorie (DE-588)4121608-8 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4154418-3 (DE-588)4121608-8 (DE-588)4226127-2 |
| title | Neural networks in finance gaining predictive edge in the market |
| title_auth | Neural networks in finance gaining predictive edge in the market |
| title_exact_search | Neural networks in finance gaining predictive edge in the market |
| title_exact_search_txtP | Neural networks in finance gaining predictive edge in the market |
| title_full | Neural networks in finance gaining predictive edge in the market Paul D. McNelis |
| title_fullStr | Neural networks in finance gaining predictive edge in the market Paul D. McNelis |
| title_full_unstemmed | Neural networks in finance gaining predictive edge in the market Paul D. McNelis |
| title_short | Neural networks in finance |
| title_sort | neural networks in finance gaining predictive edge in the market |
| title_sub | gaining predictive edge in the market |
| topic | Finanzierungstheorie (DE-588)4154418-3 gnd Schätztheorie (DE-588)4121608-8 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Finanzierungstheorie Schätztheorie Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016709098&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mcnelispauld neuralnetworksinfinancegainingpredictiveedgeinthemarket |