Biologically inspired algorithms for financial modelling: with 39 tables
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin ; Heidelberg ; New York
Springer
2006
|
| Series: | Natural computing series
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 257 - 269 |
| Physical Description: | XV, 275 S. graph. Darst. |
| ISBN: | 9783540262527 3540262520 |
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MARC
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| 100 | 1 | |a Brabazon, Anthony |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Biologically inspired algorithms for financial modelling |b with 39 tables |c Anthony Brabazon ; Michael O'Neill |
| 264 | 1 | |a Berlin ; Heidelberg ; New York |b Springer |c 2006 | |
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| 650 | 4 | |a Finanzmarkt / Wirtschaftsmodell / Bioökonomik / Wertpapieranalyse / Prognoseverfahren / Theorie | |
| 650 | 4 | |a Finanzmathematik - Evolutionärer Algorithmus | |
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Record in the Search Index
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|---|---|
| adam_text | ANTHON
Y BRABAZON YY MICHAEL O NEILL
BIOLOGICALLY INSPIRED
ALGORITHMS FOR FINANCIAL
MODELLIN
G
WITH 92 FIGURES AN
D 39 TABLES
FYA
SPRINGE
R
CONTENTS
1 INTRODUCTIO
N
1
1.1 BIOLOGICALLY INSPIRED ALGORITHMS 2
1.1.1 ARTIFICIAL NEURAL NETWORKS 2
1.1.2 EVOLUTIONARY COMPUTATION 2
1.1.3 SOCIAL SYSTEMS 4
1.1.4 ARTIFICIAL IMMUNE SYSTEMS 4
1.2 COMPUTER TRADING ON FINANCIAL MARKETS 4
1.3 CHALLENGES IN THE MODELLING OF FINANCIAL MARKETS 5
1.3.1 DO PRICES FOLLOW A RANDOM WALK? 6
1.3.2 ATTACK OF THE ANOMALIES 7
1.4 LINEAR MODELS 8
1.5 STRUCTURE OF THE BOOK 10
PAR
T I METHODOLOGIE
S
2 NEURA
L NETWOR
K METHODOLOGIE
S
15
2.1 A TAXONOMY OF NNS 15
2.2 THE MULTI LAYER PERCEPTRON 16
2.2.1 TRAINING AN MLP 20
2.2.2 PRACTICAL ISSUES IN TRAINING MLPS 23
2.2.3 RECURRENT NETWORKS 28
2.3 RADIAL BASIS FUNCTION NETWORKS 29
2.4 SELF-ORGANISING MAPS 32
2.4.1 IMPLEMENTING A SOM 33
2.5 SUMMARY 35
3 EVOLUTIONAR
Y METHODOLOGIE
S
37
3.1 GENETIC ALGORITHM 37
3.1.1 CANONICAL GA 40
3.1.2 EXAMPLE OF THE GA 41
XII CONTENTS
3.1.3 EXTENDING TH
E CANONICAL GA 43
3.1.4 SCHEMA AND BUILDING BLOCKS 48
3.2 DIFFERENTIAL EVOLUTION 49
3.2.1 DE ALGORITHM 49
3.3 GENETIC PROGRAMMING 54
3.3.1 MORE COMPLEX G
P ARCHITECTURES 58
3.4 COMBINING EA AND MLP METHODOLOGIES 63
3.5 APPLYING EAS T
O EVOLVE TRADING RULES 68
3.6 RECENT DEVELOPMENTS IN EVOLUTIONARY COMPUTATION 70
3.7 SUMMARY 71
4 GRAMMATICA
L EVOLUTIO
N
73
4.1 GRAMMATICAL EVOLUTION 73
4.1.1 BIOLOGICAL ANALOGY 74
4.1.2 MAPPING PROCESS 76
4.1.3 MAPPING EXAMPLE 79
4.2 MUTATION AND CROSSOVER IN GE 82
4.3 RECENT DEVELOPMENTS IN GE 84
4.3.1 SEARCH ENGINE 84
4.3.2 META-GRAMMARS 85
4.3.3
TTGE
87
4.3.4 APPLICATIONS AND ALTERNATIVE GRAMMARS 87
4.4 SUMMARY 88
5 TH
E PARTICL
E SWAR
M MODE
L
89
5.1 PSO ALGORITHM 89
5.1.1 CONSTRICTION COEFFICIENT VERSION OF PSO 92
5.1.2 PARAMETER SETTINGS FOR PSO 93
5.2 DISCRETE PSO 94
5.3 COMPARING PSO AND TH
E GA 94
5.4 MLP-SWARM HYBRIDS 95
5.5 GRAMMATICAL SWARM 95
5.6 EXAMPLE OF A FINANCIAL APPLICATION OF PSO 96
5.7 RECENT DEVELOPMENTS IN PSO 96
5.8 SUMMARY 97
6 AN
T COLON
Y MODEL
S
99
6.1 ANT-FORAGING MODELS 99
6.1.1 ANT-FORAGING ALGORITHM 100
6.2 A FINANCIAL APPLICATION OF ACO 104
6.3 ANT-INSPIRED CLASSIFICATION ALGORITHMS 105
6.4 HYBRID ANT MODELS 105
6.5 SUMMARY 106
CONTENTS XIII
ARTIFICIA
L IMMUN
E SYSTEM
S
107
7.1 OVERVIEW OF NATURA
L IMMUN
E SYSTEM
S 108
7.1.1 INNAT
E VS ADAPTIV
E IMMUNIT
Y 108
7.1.2 COMPONENT
S OF TH
E IMMUN
E SYSTE
M 108
7.2 DESIGNING ARTIFICIAL IMMUN
E ALGORITHM
S 113
7.2.1 NEGATIV
E SELECTION ALGORITH
M 113
7.2.2 CLONA
L EXPANSIO
N AN
D SELECTION ALGORITH
M 114
7.3 FINANCIA
L APPLICATIO
N OF TH
E NEGATIV
E SELECTION ALGORITH
M ...
. 116
7.4 SUMMAR
Y 118
PAR
T II MODE
L DEVELOPMEN
T
8 MODE
L DEVELOPMEN
T PROCES
S
121
8.1 PROJECT GOALS 121
8.1.1 WHAT T
O FORECAST? 121
8.1.2 WHAT PERFORMANCE MEASURE IS APPROPRIATE? 123
8.2 DATA COLLECTION 124
8.2.1 TRADING PHILOSOPHY 124
8.2.2 HOW MUCH DAT
A IS ENOUGH? 128
8.3 SELECTING AND PREPROCESSING THE DAT
A 130
8.3.1 SELECTION 130
8.3.2 PREPROCESSING 130
8.4 POSTPROCESSING THE OUTPUT 134
8.4.1 ENTRY STRATEGY 134
8.4.2 EXIT STRATEGY 134
8.4.3 MONEY MANAGEMENT 135
8.5 VALIDATING THE SYSTEM 135
8.6 IMPLEMENTATION AND MAINTENANCE 140
8.7 SUMMARY 142
9 TECHNICAL ANALYSI
S
143
9.1 TECHNICAL INDICATORS 144
9.1.1 MOVING AVERAGE 146
9.1.2 MOMENTUM 148
9.1.3 BREAKOUT 149
9.1.4 STOCHASTIC OSCILLATORS 150
9.1.5 VOLUME DATA : 152
9.1.6 OTHER INDICATORS 153
9.2 USING TECHNICAL INDICATORS IN A TRADING SYSTEM 154
9.3 SUMMARY 155
XIV
CONTENTS
PART III CASE STUDIE
S
10 OVERVIEW OF CASE STUDIE
S
159
11 INDEX PREDICTIO
N USIN
G MLP
S
161
11.1 METHODOLOGY 162
11.1.1 MODEL SELECTION 166
11.1.2 MODEL STACKING 167
11.2 RESULTS 169
11.2.1 RMSE AND CORRELATION 169
11.2.2 TRADING SYSTEM 171
11.3 DISCUSSION 172
12 INDE
X PREDICTIO
N USIN
G A MLP-G
A HYBRI
D
175
12.1 METHODOLOGY 175
12.1.1 MODEL CONSTRUCTION 176
12.2 RESULTS 178
12.2.1 MLP-GA 179
12.2.2 ANALYSIS OF WEIGHT VECTORS 180
12.3 DISCUSSION 182
13 INDE
X TRADING USIN
G GRAMMATICAL EVOLUTIO
N
183
13.1 METHODOLOGY 183
13.1.1 GE SYSTEM SETUP 188
13.2 RESULTS 189
13.3 DISCUSSION 190
14 ADAPTIV
E TRADING USIN
G GRAMMATICA
L EVOLUTIO
N
193
14.1 INTRODUCTION 193
14.2 METHODOLOGY 193
14.2.1 MOVING WINDOW 194
14.2.2 VARIABLE POSITION TRADING 194
14.2.3 RETURN CALCULATION 195
14.3 RESULTS 196
14.3.1 TRAINING RETURNS 197
14.3.2 OUT-OF-SAMPLE RETURNS 199
14.4 DISCUSSION 201
15 INTRA-DAY TRADING USIN
G GRAMMATICAL EVOLUTIO
N
203
15.1 BACKGROUND 203
15.2 METHODOLOGY 204
15.2.1 TRADING SYSTEM 206
15.2.2 GE SYSTEM SETUP 207
15.3 RESULTS
Y.
208
15.4 DISCUSSION 210
CONTENTS XV
16 AUTOMATI
C GENERATIO
N OF FOREIGN EXCHANG
E TRADING RULE
S
. 211
16.1 BACKGROUND 211
16.2 METHODOLOGY 212
16.3 RESULTS 214
16.3.1 US-STG 216
16.3.2 US-YEN 217
16.3.3 US-DM 217
16.4 DISCUSSION 218
17 CORPORAT
E FAILURE PREDICTIO
N USIN
G GRAMMATICAL
EVOLUTIO
N
219
17.1 BACKGROUND 220
17.1.1 DEFINITION OF CORPORATE FAILURE 220
17.1.2 EXPLANATORY VARIABLES 221
17.2 METHODOLOGY 222
17.2.1 GE SYSTEM SETUP 223
17.2.2 LDA METHOD 224
17.3 RESULTS 224
17.3.1 FORM OF THE EVOLVED CLASSIFIERS 225
17.4 DISCUSSION 226
18 CORPORAT
E FAILURE PREDICTIO
N USIN
G AN AN
T MODE
L
229
18.1 BACKGROUND 229
18.2 METHODOLOGY 230
18.2.1 ANT SYSTEM 231
18.3 RESULTS 235
18.4 DISCUSSION 238
19 BON
D RATIN
G USIN
G GRAMMATICA
L EVOLUTIO
N
239
19.1 BACKGROUND 240
19.1.1 RATING PROCESS 240
19.2 METHODOLOGY 241
19.3 RESULTS 243
19.4 DISCUSSION 247
20 BON
D RATIN
G USIN
G AI
S
249
20.1 METHODOLOGY 249
20.2 RESULTS 252
20.3 DISCUSSION 252
21 WRAP-UP
255
REFERENCE
S
257
INDE
X
271
|
| adam_txt |
ANTHON
Y BRABAZON YY MICHAEL O'NEILL
BIOLOGICALLY INSPIRED
ALGORITHMS FOR FINANCIAL
MODELLIN
G
WITH 92 FIGURES AN
D 39 TABLES
FYA
SPRINGE
R
CONTENTS
1 INTRODUCTIO
N
1
1.1 BIOLOGICALLY INSPIRED ALGORITHMS 2
1.1.1 ARTIFICIAL NEURAL NETWORKS 2
1.1.2 EVOLUTIONARY COMPUTATION 2
1.1.3 SOCIAL SYSTEMS 4
1.1.4 ARTIFICIAL IMMUNE SYSTEMS 4
1.2 COMPUTER TRADING ON FINANCIAL MARKETS 4
1.3 CHALLENGES IN THE MODELLING OF FINANCIAL MARKETS 5
1.3.1 DO PRICES FOLLOW A RANDOM WALK? 6
1.3.2 ATTACK OF THE ANOMALIES 7
1.4 LINEAR MODELS 8
1.5 STRUCTURE OF THE BOOK 10
PAR
T I METHODOLOGIE
S
2 NEURA
L NETWOR
K METHODOLOGIE
S
15
2.1 A TAXONOMY OF NNS 15
2.2 THE MULTI LAYER PERCEPTRON 16
2.2.1 TRAINING AN MLP 20
2.2.2 PRACTICAL ISSUES IN TRAINING MLPS 23
2.2.3 RECURRENT NETWORKS 28
2.3 RADIAL BASIS FUNCTION NETWORKS 29
2.4 SELF-ORGANISING MAPS 32
2.4.1 IMPLEMENTING A SOM 33
2.5 SUMMARY 35
3 EVOLUTIONAR
Y METHODOLOGIE
S
37
3.1 GENETIC ALGORITHM 37
3.1.1 CANONICAL GA 40
3.1.2 EXAMPLE OF THE GA 41
XII CONTENTS
3.1.3 EXTENDING TH
E CANONICAL GA 43
3.1.4 SCHEMA AND BUILDING BLOCKS 48
3.2 DIFFERENTIAL EVOLUTION 49
3.2.1 DE ALGORITHM 49
3.3 GENETIC PROGRAMMING 54
3.3.1 MORE COMPLEX G
P ARCHITECTURES 58
3.4 COMBINING EA AND MLP METHODOLOGIES 63
3.5 APPLYING EAS T
O EVOLVE TRADING RULES 68
3.6 RECENT DEVELOPMENTS IN EVOLUTIONARY COMPUTATION 70
3.7 SUMMARY 71
4 GRAMMATICA
L EVOLUTIO
N
73
4.1 GRAMMATICAL EVOLUTION 73
4.1.1 BIOLOGICAL ANALOGY 74
4.1.2 MAPPING PROCESS 76
4.1.3 MAPPING EXAMPLE 79
4.2 MUTATION AND CROSSOVER IN GE 82
4.3 RECENT DEVELOPMENTS IN GE 84
4.3.1 SEARCH ENGINE 84
4.3.2 META-GRAMMARS 85
4.3.3
TTGE
87
4.3.4 APPLICATIONS AND ALTERNATIVE GRAMMARS 87
4.4 SUMMARY 88
5 TH
E PARTICL
E SWAR
M MODE
L
89
5.1 PSO ALGORITHM 89
5.1.1 CONSTRICTION COEFFICIENT VERSION OF PSO 92
5.1.2 PARAMETER SETTINGS FOR PSO 93
5.2 DISCRETE PSO 94
5.3 COMPARING PSO AND TH
E GA 94
5.4 MLP-SWARM HYBRIDS 95
5.5 GRAMMATICAL SWARM 95
5.6 EXAMPLE OF A FINANCIAL APPLICATION OF PSO 96
5.7 RECENT DEVELOPMENTS IN PSO 96
5.8 SUMMARY 97
6 AN
T COLON
Y MODEL
S
99
6.1 ANT-FORAGING MODELS 99
6.1.1 ANT-FORAGING ALGORITHM 100
6.2 A FINANCIAL APPLICATION OF ACO 104
6.3 ANT-INSPIRED CLASSIFICATION ALGORITHMS 105
6.4 HYBRID ANT MODELS 105
6.5 SUMMARY 106
CONTENTS XIII
ARTIFICIA
L IMMUN
E SYSTEM
S
107
7.1 OVERVIEW OF NATURA
L IMMUN
E SYSTEM
S 108
7.1.1 INNAT
E VS ADAPTIV
E IMMUNIT
Y 108
7.1.2 COMPONENT
S OF TH
E IMMUN
E SYSTE
M 108
7.2 DESIGNING ARTIFICIAL IMMUN
E ALGORITHM
S 113
7.2.1 NEGATIV
E SELECTION ALGORITH
M 113
7.2.2 CLONA
L EXPANSIO
N AN
D SELECTION ALGORITH
M 114
7.3 FINANCIA
L APPLICATIO
N OF TH
E NEGATIV
E SELECTION ALGORITH
M .
. 116
7.4 SUMMAR
Y 118
PAR
T II MODE
L DEVELOPMEN
T
8 MODE
L DEVELOPMEN
T PROCES
S
121
8.1 PROJECT GOALS 121
8.1.1 WHAT T
O FORECAST? 121
8.1.2 WHAT PERFORMANCE MEASURE IS APPROPRIATE? 123
8.2 DATA COLLECTION 124
8.2.1 TRADING PHILOSOPHY 124
8.2.2 HOW MUCH DAT
A IS ENOUGH? 128
8.3 SELECTING AND PREPROCESSING THE DAT
A 130
8.3.1 SELECTION 130
8.3.2 PREPROCESSING 130
8.4 POSTPROCESSING THE OUTPUT 134
8.4.1 ENTRY STRATEGY 134
8.4.2 EXIT STRATEGY 134
8.4.3 MONEY MANAGEMENT 135
8.5 VALIDATING THE SYSTEM 135
8.6 IMPLEMENTATION AND MAINTENANCE 140
8.7 SUMMARY 142
9 TECHNICAL ANALYSI
S
143
9.1 TECHNICAL INDICATORS 144
9.1.1 MOVING AVERAGE 146
9.1.2 MOMENTUM 148
9.1.3 BREAKOUT 149
9.1.4 STOCHASTIC OSCILLATORS 150
9.1.5 VOLUME DATA : 152
9.1.6 OTHER INDICATORS 153
9.2 USING TECHNICAL INDICATORS IN A TRADING SYSTEM 154
9.3 SUMMARY 155
XIV
CONTENTS
PART III CASE STUDIE
S
10 OVERVIEW OF CASE STUDIE
S
159
11 INDEX PREDICTIO
N USIN
G MLP
S
161
11.1 METHODOLOGY 162
11.1.1 MODEL SELECTION 166
11.1.2 MODEL STACKING 167
11.2 RESULTS 169
11.2.1 RMSE AND CORRELATION 169
11.2.2 TRADING SYSTEM 171
11.3 DISCUSSION 172
12 INDE
X PREDICTIO
N USIN
G A MLP-G
A HYBRI
D
175
12.1 METHODOLOGY 175
12.1.1 MODEL CONSTRUCTION 176
12.2 RESULTS 178
12.2.1 MLP-GA 179
12.2.2 ANALYSIS OF WEIGHT VECTORS 180
12.3 DISCUSSION 182
13 INDE
X TRADING USIN
G GRAMMATICAL EVOLUTIO
N
183
13.1 METHODOLOGY 183
13.1.1 GE SYSTEM SETUP 188
13.2 RESULTS 189
13.3 DISCUSSION 190
14 ADAPTIV
E TRADING USIN
G GRAMMATICA
L EVOLUTIO
N
193
14.1 INTRODUCTION 193
14.2 METHODOLOGY 193
14.2.1 MOVING WINDOW 194
14.2.2 VARIABLE POSITION TRADING 194
14.2.3 RETURN CALCULATION 195
14.3 RESULTS 196
14.3.1 TRAINING RETURNS 197
14.3.2 OUT-OF-SAMPLE RETURNS 199
14.4 DISCUSSION 201
15 INTRA-DAY TRADING USIN
G GRAMMATICAL EVOLUTIO
N
203
15.1 BACKGROUND 203
15.2 METHODOLOGY 204
15.2.1 TRADING SYSTEM 206
15.2.2 GE SYSTEM SETUP 207
15.3 RESULTS
Y.
208
15.4 DISCUSSION 210
CONTENTS XV
16 AUTOMATI
C GENERATIO
N OF FOREIGN EXCHANG
E TRADING RULE
S
. 211
16.1 BACKGROUND 211
16.2 METHODOLOGY 212
16.3 RESULTS 214
16.3.1 US-STG 216
16.3.2 US-YEN 217
16.3.3 US-DM 217
16.4 DISCUSSION 218
17 CORPORAT
E FAILURE PREDICTIO
N USIN
G GRAMMATICAL
EVOLUTIO
N
219
17.1 BACKGROUND 220
17.1.1 DEFINITION OF CORPORATE FAILURE 220
17.1.2 EXPLANATORY VARIABLES 221
17.2 METHODOLOGY 222
17.2.1 GE SYSTEM SETUP 223
17.2.2 LDA METHOD 224
17.3 RESULTS 224
17.3.1 FORM OF THE EVOLVED CLASSIFIERS 225
17.4 DISCUSSION 226
18 CORPORAT
E FAILURE PREDICTIO
N USIN
G AN AN
T MODE
L
229
18.1 BACKGROUND 229
18.2 METHODOLOGY 230
18.2.1 ANT SYSTEM 231
18.3 RESULTS 235
18.4 DISCUSSION 238
19 BON
D RATIN
G USIN
G GRAMMATICA
L EVOLUTIO
N
239
19.1 BACKGROUND 240
19.1.1 RATING PROCESS 240
19.2 METHODOLOGY 241
19.3 RESULTS 243
19.4 DISCUSSION 247
20 BON
D RATIN
G USIN
G AI
S
249
20.1 METHODOLOGY 249
20.2 RESULTS 252
20.3 DISCUSSION 252
21 WRAP-UP
255
REFERENCE
S
257
INDE
X
271 |
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| author | Brabazon, Anthony O'Neill, Michael 1975- |
| author_GND | (DE-588)123987695 |
| author_facet | Brabazon, Anthony O'Neill, Michael 1975- |
| author_role | aut aut |
| author_sort | Brabazon, Anthony |
| author_variant | a b ab m o mo |
| building | Verbundindex |
| bvnumber | BV021523727 |
| classification_rvk | QH 700 |
| ctrlnum | (OCoLC)254564337 (DE-599)BVBBV021523727 |
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| dewey-hundreds | 000 - Computer science, information, general works |
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| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV021523727 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:23:16Z |
| indexdate | 2024-07-09T20:37:46Z |
| institution | BVB |
| isbn | 9783540262527 3540262520 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014740189 |
| oclc_num | 254564337 |
| open_access_boolean | |
| owner | DE-703 DE-19 DE-BY-UBM DE-706 DE-11 |
| owner_facet | DE-703 DE-19 DE-BY-UBM DE-706 DE-11 |
| physical | XV, 275 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| record_format | marc |
| series2 | Natural computing series |
| spelling | Brabazon, Anthony Verfasser aut Biologically inspired algorithms for financial modelling with 39 tables Anthony Brabazon ; Michael O'Neill Berlin ; Heidelberg ; New York Springer 2006 XV, 275 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Natural computing series Literaturverz. S. 257 - 269 Finanzmarkt / Wirtschaftsmodell / Bioökonomik / Wertpapieranalyse / Prognoseverfahren / Theorie Finanzmathematik - Evolutionärer Algorithmus Finanzmathematik - Neuronales Netz Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 s Neuronales Netz (DE-588)4226127-2 s DE-604 Evolutionärer Algorithmus (DE-588)4366912-8 s O'Neill, Michael 1975- Verfasser (DE-588)123987695 aut text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2669234&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014740189&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brabazon, Anthony O'Neill, Michael 1975- Biologically inspired algorithms for financial modelling with 39 tables Finanzmarkt / Wirtschaftsmodell / Bioökonomik / Wertpapieranalyse / Prognoseverfahren / Theorie Finanzmathematik - Evolutionärer Algorithmus Finanzmathematik - Neuronales Netz Evolutionärer Algorithmus (DE-588)4366912-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4226127-2 (DE-588)4017195-4 |
| title | Biologically inspired algorithms for financial modelling with 39 tables |
| title_auth | Biologically inspired algorithms for financial modelling with 39 tables |
| title_exact_search | Biologically inspired algorithms for financial modelling with 39 tables |
| title_exact_search_txtP | Biologically inspired algorithms for financial modelling with 39 tables |
| title_full | Biologically inspired algorithms for financial modelling with 39 tables Anthony Brabazon ; Michael O'Neill |
| title_fullStr | Biologically inspired algorithms for financial modelling with 39 tables Anthony Brabazon ; Michael O'Neill |
| title_full_unstemmed | Biologically inspired algorithms for financial modelling with 39 tables Anthony Brabazon ; Michael O'Neill |
| title_short | Biologically inspired algorithms for financial modelling |
| title_sort | biologically inspired algorithms for financial modelling with 39 tables |
| title_sub | with 39 tables |
| topic | Finanzmarkt / Wirtschaftsmodell / Bioökonomik / Wertpapieranalyse / Prognoseverfahren / Theorie Finanzmathematik - Evolutionärer Algorithmus Finanzmathematik - Neuronales Netz Evolutionärer Algorithmus (DE-588)4366912-8 gnd Neuronales Netz (DE-588)4226127-2 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| topic_facet | Finanzmarkt / Wirtschaftsmodell / Bioökonomik / Wertpapieranalyse / Prognoseverfahren / Theorie Finanzmathematik - Evolutionärer Algorithmus Finanzmathematik - Neuronales Netz Evolutionärer Algorithmus Neuronales Netz Finanzmathematik |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2669234&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014740189&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT brabazonanthony biologicallyinspiredalgorithmsforfinancialmodellingwith39tables AT oneillmichael biologicallyinspiredalgorithmsforfinancialmodellingwith39tables |