Computational intelligence in economics and finance: [Vol. 1]
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| Weitere Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2004
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| Schriftenreihe: | Advanced information processing
|
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| Beschreibung: | XXII, 480 S. graph. Darst. |
| ISBN: | 3540440984 |
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MARC
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| 245 | 1 | 0 | |a Computational intelligence in economics and finance |n [Vol. 1] |c Shu-Heng Chen ... (ed.) |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2004 | |
| 300 | |a XXII, 480 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Advanced information processing | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Economics -- Data processing | |
| 650 | 4 | |a Economics, Mathematical | |
| 650 | 4 | |a Finance -- Data processing | |
| 650 | 0 | 7 | |a Soft Computing |0 (DE-588)4455833-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Entscheidungsunterstützung |0 (DE-588)4202171-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Financial Engineering |0 (DE-588)4208404-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Entscheidungsunterstützungssystem |0 (DE-588)4191815-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuro-Fuzzy-System |0 (DE-588)4560677-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Evolutionärer Algorithmus |0 (DE-588)4366912-8 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |y 2002 |z Research Triangle Park, NC |2 gnd-content | |
| 655 | 7 | |0 (DE-588)1071861417 |a Konferenzschrift |y 2002 |z Research Triangle Park NC |2 gnd-content | |
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Datensatz im Suchindex
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| adam_text |
TABLE
OF
CONTENTS
PART
I.
INTRODUCTION
1.
COMPUTATIONAL
INTELLIGENCE
IN
ECONOMICS
AND
FINANCE
SHU-HENG
CHEN
AND
PAUL
P.
WANG
.
3
1.1
WHAT
IS
COMPUTATIONAL
INTELLIGENCE?
.
4
1.2
FUZZY
LOGIC
AND
THE
RELATED
DISCIPLINES
.
10
1.2.1
FUZZY
LOGIC
.
10
1.2.2
ROUGH
SETS
.
12
1.2.3
GREY
FORECASTING
.
13
1.3
NEURAL
NETWORKS
AND
RELATED
DISCIPLINES
.
14
1.3.1
ARTIFICIAL
NEURAL
NETWORKS
.
14
1.3.2
SUPPORT
VECTOR
MACHINES
.
18
1.3.3
SELF-ORGANIZING
MAPS
.
20
1.3.4
FINITE
STATE
AUTOMATA
.
22
1.3.5
DECISION
TREES
.
25
1.3.6
K
NEAREST
NEIGHBORS
.
28
1.3.7
HIDDEN
MARKOV
.
30
1.3.8
FOURIER
TRANSFORMS
AND
WAVELET
TRANSFORMS
.
30
1.4
EVOLUTIONARY
COMPUTATION
AND
THE
RELATED
DISCIPLINES
.
33
1.4.1
EVOLUTIONARY
COMPUTATION:
THE
COMMON
FEATURES
.
33
1.4.2
EVOLUTIONARY
STRATEGIES
.
36
1.4.3
EVOLUTIONARY
PROGRAMMING
.
38
1.4.4
GENETIC
ALGORITHMS
AND
GENETIC
PROGRAMMING
.
39
1.4.5
SIMULATED
ANNEALING
.
39
1.4.6
ANT
ALGORITHMS
.
40
1.5
STATE-SPACE
APPROACH
.
43
1.6
APPLICATION
DOMAINS
.
45
1.7
PERFORMANCE
EVALUATION
.
46
1.7.1
PREDICTION
.
47
1.7.2
CLASSIFICATION
.
47
1.7.3
TRADING
.
48
1.7.4
FUNCTION
OPTIMIZATION
.
49
1.8
CONCLUDING
REMARKS:
AGENT-BASED
MODELS
.
49
REFERENCES
.
51
VIII
TABLE
OF
CONTENTS
PART
II.
FUZZY
LOGIC
AND
ROUGH
SETS
2.
INTELLIGENT
SYSTEM
TO
SUPPORT
JUDGMENTAL
BUSINESS
FORECASTING:
THE
CASE
OF
ESTIMATING
HOTEL
ROOM
DEMAND
MOUNIR
BEN
GHALIA
AND
PAUL
P.
WANG
.
59
2.1
INTRODUCTION
.
59
2.2
REVENUE
MANAGEMENT
SYSTEM
FOR
HOTELS
.
62
2.2.1
DEMAND
FORECASTING
AS
PART
OF
RMS
.
64
2.2.2
OPTIMIZATION
.
64
2.3
FORECASTING
USING
STATISTICAL
TECHNIQUES
.
64
2.3.1
STATISTICAL
FORECASTING
TECHNIQUES
.
64
2.3.2
APPLICATION
TO
THE
ESTIMATION
OF
HOTEL
ROOM
DEMAND
65
2.3.3
LIMITATIONS
OF
STATISTICAL
FORECASTING
.
66
2.4
JUDGMENTAL
FORECASTING
IN
THE
HOTEL
INDUSTRY
.
67
2.4.1
WHAT
DO
HOTEL
MANAGERS
KNOW?
.
67
2.4.2
HOW
CAN
INFORMED
JUDGMENTAL
FORECASTING
HELP
IN
CREASE
REVENUE?
.
69
2.5
KNOWLEDGE
ENGINEERING
FOR
IS-JFK
.
69
2.5.1
UNSTRUCTURED
INTERVIEWS
.
70
2.5.2
STRUCTURED
INTERVIEWS
.
70
2.5.3
FOLLOW-UP
QUESTIONNAIRES
.
71
2.5.4
PROTOCOL
EXTRACTION
.
71
2.5.5
KNOWLEDGE
ANALYSIS
AND
REPRESENTATION
.
72
2.6
INTELLIGENT
SYSTEM
TO
SUPPORT
JUDGMENTAL
FORECAST
AND
KNOW
LEDGE
(IS-JFK)
.
72
2.6.1
FUZZIFICATION
.
73
2.6.2
KNOWLEDGE
BASE
.
74
2.6.3
DATABASE
.
74
2.6.4
USER
SUPPORT
ENGINE
.
74
2.6.5
APPROXIMATE
REASONING
ENGINE
.
74
2.6.6
DEFUZZIFICATION
.
75
2.7
FORECAST
ADJUSTMENT
USING
A
DIRECT
APPROACH
.
75
2.7.1
FUZZY
IF-THEN
RULES
FOR
THE
DIRECT
APPROACH
.
75
2.7.2
EXAMPLE
1
.
77
2.8
FUZZY
ADJUSTMENT
VIA
FUZZY
INTERVENTION
ANALYSIS
.
83
2.8.1
INTERVENTION
MODELS
.
83
2.8.2
FUZZY
INTERVENTION
MODELS
.
86
2.8.3
EXAMPLE
2
.
88
2.9
CONCLUSION
.
89
REFERENCES
.
91
TABLE
OF
CONTENTS
IX
3.
FUZZY
INVESTMENT
ANALYSIS
USING
CAPITAL
BUDGETING
AND
DYNAMIC
PROGRAMMING
TECHNIQUES
CENGIZ
KAHRAMAN
AND
CAFER
ERHAN
BOZDAG
.
93
3.1
INTRODUCTION
.
93
3.2
FUZZY
PRESENT
VALUE
(PV)
METHOD
.
96
3.3
FUZZY
CAPITALIZED
VALUE
METHOD
.
97
3.4
FUZZY
FUTURE
VALUE
METHOD
.
98
3.5
FUZZY
BENEFIT/COST
RATIO
METHOD
.
99
3.6
FUZZY
EQUIVALENT
UNIFORM
ANNUAL
VALUE
(EUAV)
METHOD
.
.
.
100
3.7
FUZZY
PAYBACK
PERIOD
(FPP)
METHOD
.
100
3.8
RANKING
FUZZY
NUMBERS
.
101
3.9
FUZZY
INTERNAL
RATE
OF
RETURN
(IRR)
METHOD
.
102
3.10
AN
EXPANSION
TO
GEOMETRIC
AND
TRIGONOMETRIC
CASH
FLOWS
.
104
3.10.1
GEOMETRIC
SERIES-FUZZY
CASH
FLOWS
IN
DISCRETE
COM
POUNDING
.
104
3.10.2
GEOMETRIC
SERIES-FUZZY
CASH
FLOWS
IN
CONTINUOUS
COMPOUNDING
.
105
3.10.3
TRIGONOMETRIC
SERIES-FUZZY
CONTINUOUS
CASH
FLOWS.
.
106
3.10.4
NUMERIC
EXAMPLE
I
.
107
3.10.5
NUMERIC
EXAMPLE
II
.
108
3.11
DYNAMIC
PROGRAMMING
FOR
MULTILEVEL
INVESTMENT
ANALYSIS
.
.
109
3.11.1
FUZZY
DYNAMIC
PROGRAMMING:
LITERATURE
REVIEW
.
ILL
3.11.2
CRISP
DYNAMIC
PROGRAMMING
FOR
MULTILEVEL
INVESTMENTS
.
113
3.11.3
FUZZY
DYNAMIC
PROGRAMMING
FOR
MULTILEVEL
INVESTMENTS
.
114
3.11.4
A
NUMERIC
EXAMPLE
.
115
3.12
CONCLUSIONS
.
124
REFERENCES
.
125
APPENDIX
.
127
4.
ROUGH
SETS
THEORY
AND
MULTIVARIATE
DATA
ANALYSIS
IN
CLASSIFICATION
PROBLEMS:
A
SIMULATION
STUDY
MICHAEL
DOUMPOS
AND
CONSTANTIN
ZOPOUNIDIS
.
129
4.1
INTRODUCTION
.
129
4.2
OUTLINE
OF
THE
ROUGH
SETS
APPROACH
.
130
4.3
EXPERIMENTAL
DESIGN
.
134
4.4
RESULTS
.
137
4.5
CONCLUSIONS
.
143
REFERENCES
.
145
X
TABLE
OF
CONTENTS
PART
III.
ARTIFICIAL
NEURAL
NETWORKS
AND
SUPPORT
VECTOR
MACHINES
5.
FORECASTING
THE
OPENING
CASH
PRICE
INDEX
IN
INTEGRATING
GREY
FORECASTING
AND
NEURAL
NETWORKS:
EVIDENCE
FROM
THE
SGX-DT
MSCI
TAIWAN
INDEX
FUTURES
CONTRACTS
TIAN-SHYUG
LEE,
NEN-JING
CHEN,
AND
CHIH-CHOU
CHIU
.
151
5.1
INTRODUCTION
.
151
5.2
LITERATURE
REVIEW
.
153
5.2.1
LEAD-LAG
RELATIONSHIP
BETWEEN
INDEX
FUTURES
AND
CASH
MARKETS
.
153
5.2.2
MARKET
TRADING
CHARACTERISTICS
AT
THE
OPENING
AND
CLOSING
STAGE
.
153
5.3
NEURAL
NETWORKS
AND
GREY
THEORY
.
154
5.3.1
NEURAL
NETWORKS
.
154
5.3.2
GREY
MODEL
AND
GREY
FORECASTING
.
156
5.4
EMPIRICAL
RESULTS
AND
DISCUSSION
.
159
5.4.1
GREY
FORECASTING
MODEL
.
159
5.4.2
NEURAL
NETWORKS
FORECASTING
MODEL
.
161
5.5
ROBUSTNESS
EVALUATION
OF
THE
NEURAL
NETWORKS
MODEL
.
164
5.6
CONCLUSIONS
AND
AREAS
OF
FUTURE
RESEARCH
.
165
REFERENCES
.
169
6.
A
SUPPORT
VECTOR
MACHINE
MODEL
FOR
CURRENCY
CRISES
DISCRIMINATION
CLAUDIO
M.
ROCCO
AND
JOSE
AH'
MORENO
.
171
6.1
INTRODUCTION
.
171
6.2
DEFINITION
OF
A
CURRENCY
CRISIS
.
171
6.3
SUPPORT
VECTOR
MACHINES
(SVMS)
.
172
6.3.1
LINEAR
SVM
.
173
6.3.2
NONLINEAR
SVM
.
176
6.3.3
IMPERFECT
SEPARATION
.
177
6.3.4
SVM
PROPERTIES
.
177
6.4
EXAMPLE
.
178
6.5
CONCLUSIONS
.
179
REFERENCES
.
180
7.
SALIENCY
ANALYSIS
OF
SUPPORT
VECTOR
MACHINES
FOR
FEATURE
SELECTION
IN
FINANCIAL
TIME
SERIES
FORECASTING
LIJUAN
CAO
AND
FRANCIS
E.
H.
TAY
.
182
7.1
INTRODUCTION
.
182
7.2
THEORY
OF
SUPPORT
VECTOR
MACHINES
(SVMS)
FOR
REGRESSION
ESTIMATION
.
184
TABLE
OF
CONTENTS
XI
7.3
SALIENCY
ANALYSIS
OF
SVMS
.
186
7.3.1
DERIVING
THE
SENSITIVITY
.
187
7.3.2
REMOVING
IRRELEVANT
FEATURES
.
188
7.4
EXPERIMENTS
.
189
7.4.1
SIMULATED
DATA
.
189
7.4.2
REAL
FINANCIAL
DATA
.
192
7.5
CONCLUSIONS
.
195
REFERENCES
.
199
PART
IV.
SELF-ORGANIZING
MAPS
AND
WAVELETS
8.
SEARCHING
FINANCIAL
PATTERNS
WITH
SELF-ORGANIZING
MAPS
SHU-HENG
CHEN
AND
HONGXING
HE
.
203
8.1
MOTIVATION
AND
INTRODUCTION
.
203
8.2
SELF-ORGANIZING
MAPS
.
204
8.3
CHARTS
CONSTRUCTED
BY
SOM
.
206
8.4
DO
SOM-INDUCED
CHARTS
REVEAL
TRADING
SIGNALS?
.
209
8.5
SOM-INDUCED
TRADING
STRATEGIES
.
212
8.6
CONCLUDING
REMARKS
.
215
REFERENCES
.
216
9.
EFFECTIVE
POSITION
OF
EUROPEAN
FIRMS
IN
THE
FACE
OF
MONETARY
INTEGRATION
USING
KOHONEN
'
S
SOFM
RAQUEL
FLOREZ
LOPEZ
.
217
9.1
THE
ECONOMIC
AND
MONETARY
UNION
PROCESS
.
217
9.2
ANNS
AND
SOFMS
.
219
9.2.1
ARTIFICIAL
NEURAL
NETWORKS
.
219
9.2.2
SELF-ORGANIZING
MAPS
.
220
9.3
EMPIRICAL
ANALYSIS
.
221
9.3.1
THE
BACH
DATABASE
.
221
9.3.2
ECONOMIC
ANALYSIS
(I):
COST
ANALYSIS
.
222
9.3.3
ECONOMIC
ANALYSIS
(II):
PROFITABILITY
ANALYSIS
.
226
9.4
CONCLUSIONS
.
229
REFERENCES
.
232
10.
FINANCIAL
APPLICATIONS
OF
WAVELETS
AND
SELF-ORGANIZING
MAPS
DIMITRIOS
MOSHOU
AND
HERMAN
RAMON
.
234
10.1
INTRODUCTION
.
234
10.2
WAVELETS
.
235
10.3
WAVELET
PACKETS
.
236
10.4
WAVELET-BASED
DENOISING
.
237
10.5
SELF-ORGANIZING
MAP
.
238
XII
TABLE
OF
CONTENTS
10.6
WAVELET-BASED
DENOISING
OF
FINANCIAL
DATA
.
240
10.7
SOM
AND
WAVELET
COMBINATION
FOR
ABNORMALITY
DETECTION
.
241
10.8
CONCLUSIONS
.
248
REFERENCES
.
248
PART
V.
SEQUENCE
MATCHING
AND
FEATURE-BASED
TIME
SERIES
MODELS
11.
PATTERN
MATCHING
IN
MULTIDIMENSIONAL
TIME
SERIES
ARNOLD
POLANSKI
.
253
11.1
INTRODUCTION
.
253
11.2
STRING
SEARCH
IN
A
TIME
SERIES
.
254
11.3
THE
PATTERN
DESCRIPTION
LANGUAGE
(PDL)
.
255
11.4
THE
PATTERN
MATCHING
MACHINE
(PMM)
.
257
11.5
AN
APPLICATION
.
258
11.6
CONCLUSIONS
AND
FURTHER
RESEARCH
.
260
REFERENCES
.
261
12.
STRUCTURAL
PATTERN
DISCOVERY
IN
TIME
SERIES
DATABASES
WEIQIANG
LIN,
MEHMET
A.
ORGUN,
AND
GRAHAM
J.
WILLIAMS
.
262
12.1
INTRODUCTION
.
262
12.2
DISCRETE-VALUED
TIME
SERIES
EXAMPLES
AND
THEIR
MODELS
.
263
12.2.1
SOME
EXAMPLES
IN
DISCRETE-VALUED
TIME
SERIES
.
264
12.2.2
DISCRETE-VALUED
SERIES
MODELS
.
264
12.3
LOCAL
POLYNOMIAL
AND
HIDDEN
MARKOV
MODELING
.
265
12.3.1
DEFINITIONS
AND
BASIC
MODELS
.
265
12.3.2
HIDDEN
MARKOV
MODELS
.
268
12.3.3
LOCAL
POLYNOMIAL
MODELING
.
268
12.4
LOCAL
POLYNOMIAL
HIDDEN
MARKOV
MODELS
.
270
12.4.1
MODELING
DTS
.
270
12.4.2
STRUCTURAL
PATTERN
DISCOVERY
.
271
12.4.3
VALUE-POINT
PATTERN
DISCOVERY
.
271
12.4.4
LOCAL
POLYNOMIAL
HIDDEN
MARKOV
MODEL
FOR
PATTERN
DISCOVERY
(LPHMM)
.
272
12.5
EXPERIMENTAL
RESULTS
.
273
12.5.1
STRUCTURAL
PATTERN
SEARCHING
.
274
12.5.2
VALUE-POINT
PATTERN
SEARCHING
.
278
12.5.3
LPHMM
FOR
PATTERN
SEARCHING
.
280
12.6
RELATED
WORK
.
283
12.7
CONCLUDING
REMARKS
.
285
REFERENCES
.
285
TABLE
OF
CONTENTS
XIII
13.
ARE
EFFICIENT
MARKETS
REALLY
EFFICIENT?:
CAN
FINANCIAL
ECONOMETRIC
TESTS
CONVINCE
MACHINE-LEARNING
PEOPLE?
SHU-HENG
CHEN
AND
TZU-WEN
KUO
.
288
13.1
INTRODUCTION
.
288
13.2
CUBIST
.
289
13.3
DATA
DESCRIPTION
.
290
13.4
EXPERIMENTAL
RESULTS
.
294
13.5
CONCLUSIONS
.
295
REFERENCES
.
296
14.
NEAREST-NEIGHBOUR
PREDICTIONS
IN
FOREIGN
EXCHANGE
MARKETS
FERNANDO
FERNANDEZ-RODRIGUEZ,
SIMON
SOSVILLA-RIVERO,
AND
JULIAN
ANDRADA-FELIX
.
297
14.1
INTRODUCTION
.
297
14.2
THE
NN
APPROACH
TO
NONLINEAR
FORECASTING
.
299
14.2.1
LITERATURE
REVIEW
.'
.
299
14.2.2
THE
APPROACH
IN
THIS
PAPER
.
301
14.2.3
SELECTING
THE
KEY
PARAMETERS
IN
(S)NN
PREDICTIONS
.
304
14.3
THE
EUROPEAN
MONETARY
SYSTEM
.
305
14.4
ASSESSING
THE
FORECAST
ACCURACY
OF
THE
NN
PREDICTORS
.
306
14.5
ASSESSING
THE
ECONOMIC
VALUE
OF
THE
NN
PREDICTORS
.
312
14.6
CONCLUDING
REMARKS
.
320
REFERENCES
.
320
PART
VI.
EVOLUTIONARY
COMPUTATION,
SWARM
INTELLIGENCE
AND
SIMULATED
ANNEALING
15.
DISCOVERING
HIDDEN
PATTERNS
WITH
GENETIC
PROGRAMMING
SHU-HENG
CHEN
AND
TZU-WEN
KUO
.
329
15.1
DISCOVERING
THE
HIDDEN
LAW
OF
MOTION
.
329
15.1.1
DETERMINISTIC
CHAOTIC
PROCESSES
.
330
15.1.2
NONLINEAR
STOCHASTIC
TIME
SERIES
.
334
15.2
STATISTICAL
BEHAVIOR
OF
GENETIC
PROGRAMMING
.
334
15.2.1
KABOUDAN
'
S
PREDICTABILITY
TEST
.
334
15.2.2
THE
FITTED
RESIDUALS
OF
GP
.
336
15.3
DISCOVERING
THE
TECHNICAL
TRADING
RULES
.
337
15.3.1
FOREIGN
EXCHANGE
MARKETS
.
338
15.3.2
STOCK
MARKETS
.
340
15.3.3
FUTURES
.
344
15.4
CONCLUDING
REMARKS
.
346
REFERENCES
.
346
XIV
TABLE
OF
CONTENTS
16.
NUMERICAL
SOLUTIONS
TO
A
STOCHASTIC
GROWTH
MODEL
BASED
ON
THE
EVOLUTION
OF
A
RADIAL
BASIS
NETWORK
FERNANDO
ALVAREZ,
NESTOR
CARRASQUERO,
AND
CLAUDIO
ROCCO
.
348
16.1
INTRODUCTION
.
348
16.2
PROPOSED
METHOD
.
349
16.3
EXPERIMENTS
AND
RESULTS
.
352
16.4
CONCLUSIONS
.
355
REFERENCES
.
357
17.
EVOLUTIONARY
STRATEGIES
VS.
NEURAL
NETWORKS:
AN
INFLATION
FORECASTING
EXPERIMENT
GRAHAM
KENDALL,
JANE
M.
BINNER,
AND
ALICIA
M.
GAZELY
.
358
17.1
INTRODUCTION
.
358
17.2
EVOLUTIONARY
STRATEGIES
.
359
17.3
DATA
AND
MODEL
SPECIFICATION
.
360
17.4
TESTING
AND
RESULTS
.
362
17.5
CONCLUDING
REMARKS
.
367
REFERENCES
.
368
18.
BUSINESS
FAILURE
PREDICTION
USING
MODIFIED
ANTS
ALGORITHM
CHUNFENG
WANG,
XIN
ZHAO,
AND
LI
KANG
.
369
18.1
INTRODUCTION
.
369
18.2
ANTS
ALGORITHM
.
371
18.2.1
MAIN
IDEA
OF
THE
ANTS
ALGORITHM
.
371
18.2.2
THE
IMPROVEMENT
OF
THE
ANTS
ALGORITHM
.
372
18.3
COMPUTATION
EXPERIMENT
.
375
18.3.1
DATA
AND
VARIABLES
.
375
18.3.2
THE
STATISTIC
CHARACTERISTIC
OF
DATA
SETS
.
375
18.4
DISCRETIZATION
OF
DATA
.
377
18.5
THE
APPLICATION
OF
THE
ANTS
ALGORITHM
IN
THE
CLASSIFICATION
PROBLEM
.
378
18.5.1
CONSTRUCTING
THE
OBJECTIVE
FUNCTION
.
378
18.5.2
THE
DESCRIPTION
OF
THE
APPLICATION
.
379
18.6
IMPLEMENTATION
.
381
18.6.1
THE
PARAMETERS
OF
THE
ANTS
ALGORITHM
.
381
18.6.2
THE
PARTITION
RULE
AND
RESULTS
.
381
18.7
DISCUSSIONS
.
382
18.7.1
CLASSIFICATION
ACCURACY
.
382
18.7.2
EXTERNAL
VALIDITY
.
382
18.7.3
EXPLANATORY
ABILITY
.
383
18.7.4
COMPARED
WITH
THE
ORIGINAL
ANTS
ALGORITHM
.
383
18.7.5
THE
REDUCTION
OF
THE
ALGORITHM
'
S
PARAMETER
.
384
TABLE
OF
CONTENTS
XV
18.7.6
DYNAMIC
UPDATE
MECHANISM
.
384
18.7.7
THE
TERMINATION
CRITERION
.
384
18.7.8
COMPUTATION
EFFICIENCY
.
384
18.8
CONCLUSION
.
385
REFERENCES
.
385
19.
TOWARDS
AUTOMATED
OPTIMAL
EQUITY
PORTFOLIOS
DISCOVERY
IN
A
FINANCIAL
KNOWLEDGE
MANAGEMENT
SYSTEM
YI-CHUAN
LU
AND
HILARY
CHENG
.
387
19.1
INTRODUCTION
.
387
19.2
A
CASE
STUDY
AS
MOTIVATION
.
388
19.3
FKMS:
A
KNOWLEDGE
MANAGEMENT
SOLUTION
.
389
19.4
DATA
MANAGEMENT
.
391
19.4.1
DEFINING
METADATA
.
391
19.4.2
DATA
CONVERSION
.
392
19.4.3
DATA
CUBE
CREATION
.
392
19.5
KNOWLEDGE
DISCOVERY
VIA
DATA
MINING
TECHNIQUE
.
393
19.5.1
MULTIFACTOR
EQUITY
PORTFOLIO
MANAGEMENT
ISSUES
.
394
19.5.2
OBJECTIVE
FUNCTION
AND
CONSTRAINTS
.
394
19.5.3
ADAPTIVE
RANDOM
SEARCH
.
395
19.6
IMPLEMENTATION
AND
RESULTS
.
398
19.6.1
PORTFOLIO
SIMULATION
.
399
19.6.2
RESULTS
.
399
19.7
KNOWLEDGE
DISCOVERY
-
WITH
OTHER
DATA
MINING
APPLICA
TIONS
.
400
REFERENCES
.
401
PART
VII.
STATE
SPACE
MODELING
OF
TIME
SERIES
20.
WHITE
NOISE
TESTS
AND
SYNTHESIS
OF
APT
ECONOMIC
FACTORS
USING
TFA
KAI
CHUN
CHIU
AND
LEI
XU
.
405
20.1
INTRODUCTION
.
405
20.2
THE
ARBITRAGE
PRICING
THEORY
.
406
20.3
TEMPORAL
FACTOR
ANALYSIS
.
406
20.3.1
AN
OVERVIEW
OF
TFA
.
406
20.3.2
MODEL
SELECTION
VS.
APPROPRIATE
NUMBER
OF
FACTORS.
.
408
20.3.3
GROUNDS
AND
BENEFITS
FOR
USING
TFA
IN
APT
ANALYSIS
408
20.3.4
TESTABILITY
OF
THE
TFA
MODEL
.
408
20.4
TESTS
OF
WHITE
NOISE
RESIDUALS
.
409
20.4.1
DATA
CONSIDERATIONS
.
409
20.4.2
TEST
STATISTICS
.
409
20.4.3
EMPIRICAL
RESULTS
.
410
XVI
TABLE
OF
CONTENTS
20.5
SYNTHESIS
OF
ECONOMIC
FACTORS
.
413
20.5.1
METHODOLOGY
AND
TEST
STATISTICS
.
415
20.5.2
EMPIRICAL
RESULTS
.
416
20.6
CONCLUSION
.
418
REFERENCES
.
418
21.
LEARNING
AND
MONETARY
POLICY
IN
A
SPECTRAL
ANALYSIS
REPRESENTATION
ANDREW
HUGHES
HALLETT
AND
CHRISTIAN
R.
RICHTER
.
420
21.1
INTRODUCTION
.
420
21.2
THE
RELATIONSHIP
BETWEEN
THE
TIME
DOMAIN
AND
THE
FRE
QUENCY
DOMAIN
.
422
21.2.1
SPECTRA
AND
CROSS-SPECTRA
IN
OUR
ANALYSIS
.
422
21.2.2
CONFIDENCE
INTERVALS
.
424
21.2.3
THE
INDIRECT
ESTIMATION
TECHNIQUE
.
425
21.3
ECONOMETRIC
IMPLEMENTATION:
A
TIME-VARYING
APPROACH
TO
THE
TERM
STRUCTURE
OF
INTEREST
RATES
.
426
21.4
EMPIRICAL
RESULTS
.
428
21.4.1
RESULTS
IN
THE
TIME
DOMAIN
.
428
21.4.2
THE
GAIN
AND
THE
PHASE
SHIFTS
IN
THE
IMPLEMENTATION
OF
BRITISH
MONETARY
POLICY
1992-98
.
429
21.4.3
THE
RISK
PREMIUM
ON
THE
UK
INTEREST
RATE
.
432
21.5
CONCLUSION
.
434
REFERENCES
.
434
22.
INTERNATIONAL
TRANSMISSION
OF
BUSINESS
CYCLES:
A
SELF-ORGANIZING
MARKOV-SWITCHING
STATE-SPACE
MODEL
MORIKAZU
HAKAMATA
AND
GENSHIROU
KITAGAWA
.
436
22.1
INTRODUCTION
.
436
22.2
MONTE
CARLO
FILTER/SMOOTHER
.
437
22.2.1
GENERAL
STATE-SPACE
MODEL
.
437
22.2.2
SEQUENTIAL
MONTE
CARLO
FILTER/SMOOTHER
.
438
22.3
SELF-ORGANIZING
STATE-SPACE
MODEL
AND
SELF-TUNING
OF
PARAM
ETERS
.
440
22.4
SELF-ORGANIZING
MARKOV-SWITCHING
STATE-SPACE
MODEL
.
441
22.5
APPLICATION
.
444
22.5.1
DATA
.
444
22.5.2
RESULTS
.
444
22.6
CONCLUSIONS
.
445
REFERENCES
.
446
TABLE
OF
CONTENTS
XVII
PART
VIII.
AGENT-BASED
MODELS
23.
HOW
INFORMATION
TECHNOLOGY
CREATES
BUSINESS
VALUE
IN
THE
PAST
AND
IN
THE
CURRENT
ELECTRONIC
COMMERCE
(EC)
ERA
YASUO
KADONO
AND
TAKAO
TERANO
.
449
23.1
INTRODUCTION
.
449
23.2
FRAMEWORK
OF
3C-DRIVE
.
450
23.2.1
VERTICAL
AXIS
.
451
23.2.2
HORIZONTAL
AXIS
.
3
453
23.3
SURVEY
OF
EXISTING
RESEARCH
AND
PERSPECTIVES
.
454
23.3.1
PERSPECTIVE
OF
DIRECT
QUANTITATIVE
COMPARISON
OF
IT
INVESTMENT
AND
VALUE
.
455
23.3.2
PERSPECTIVE
OF
INFORMATION
ECONOMICS
.
456
23.3.3
PERSPECTIVE
OF
ALIGNMENT
APPROACH
.
456
23.3.4
PERSPECTIVE
OF
IT-ORGANIZATION
CORRELATION
.
458
23.3.5
OTHER
PERSPECTIVES
.
459
23.4
SIMULATION
STUDY
AND
RESULTS
.
459
23.4.1
SIMULATION
OBJECTIVES
AND
EXPERIMENTAL
SETUP
.
459
23.4.2
DESCRIPTIONS
OF
COMPETITIVE
ENVIRONMENTS
.
460
23.4.3
CHARACTERISTICS
OF
THE
AGENTS
.
461
23.4.4
ACTION
RULES
OF
THE
AGENTS
.
461
23.4.5
SIMULATION
RESULTS
.
461
23.5
CONCLUSION
.
464
REFERENCES
.
465
AUTHOR
INDEX
.
467
SUBJECT
INDEX
.
468 |
| adam_txt | |
| any_adam_object | 1 |
| any_adam_object_boolean | |
| author2 | Chen, Shu-Heng 1959- |
| author2_role | edt |
| author2_variant | s h c shc |
| author_GND | (DE-588)121139662 |
| author_facet | Chen, Shu-Heng 1959- |
| building | Verbundindex |
| bvnumber | BV023615408 |
| callnumber-first | H - Social Science |
| callnumber-label | HB143 |
| callnumber-raw | HB143.5.C664 2004 |
| callnumber-search | HB143.5.C664 2004 |
| callnumber-sort | HB 3143.5 C664 42004 |
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| dewey-sort | 3330 3285 263 222 |
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| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV023615408 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:43:40Z |
| indexdate | 2024-08-24T00:12:51Z |
| institution | BVB |
| isbn | 3540440984 |
| language | English |
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| record_format | marc |
| series2 | Advanced information processing |
| spelling | Computational intelligence in economics and finance [Vol. 1] Shu-Heng Chen ... (ed.) Berlin [u.a.] Springer 2004 XXII, 480 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Advanced information processing Datenverarbeitung Wirtschaft Economics -- Data processing Economics, Mathematical Finance -- Data processing Soft Computing (DE-588)4455833-8 gnd rswk-swf Entscheidungsunterstützung (DE-588)4202171-6 gnd rswk-swf Financial Engineering (DE-588)4208404-0 gnd rswk-swf Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd rswk-swf Neuro-Fuzzy-System (DE-588)4560677-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2002 Research Triangle Park, NC gnd-content (DE-588)1071861417 Konferenzschrift 2002 Research Triangle Park NC gnd-content Financial Engineering (DE-588)4208404-0 s Entscheidungsunterstützung (DE-588)4202171-6 s Evolutionärer Algorithmus (DE-588)4366912-8 s Neuro-Fuzzy-System (DE-588)4560677-8 s DE-604 Entscheidungsunterstützungssystem (DE-588)4191815-0 s Soft Computing (DE-588)4455833-8 s Chen, Shu-Heng 1959- (DE-588)121139662 edt (DE-604)BV023553623 1 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016931372&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Computational intelligence in economics and finance Datenverarbeitung Wirtschaft Economics -- Data processing Economics, Mathematical Finance -- Data processing Soft Computing (DE-588)4455833-8 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Financial Engineering (DE-588)4208404-0 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4202171-6 (DE-588)4208404-0 (DE-588)4191815-0 (DE-588)4560677-8 (DE-588)4366912-8 (DE-588)1071861417 |
| title | Computational intelligence in economics and finance |
| title_auth | Computational intelligence in economics and finance |
| title_exact_search | Computational intelligence in economics and finance |
| title_exact_search_txtP | Computational intelligence in economics and finance |
| title_full | Computational intelligence in economics and finance [Vol. 1] Shu-Heng Chen ... (ed.) |
| title_fullStr | Computational intelligence in economics and finance [Vol. 1] Shu-Heng Chen ... (ed.) |
| title_full_unstemmed | Computational intelligence in economics and finance [Vol. 1] Shu-Heng Chen ... (ed.) |
| title_short | Computational intelligence in economics and finance |
| title_sort | computational intelligence in economics and finance |
| topic | Datenverarbeitung Wirtschaft Economics -- Data processing Economics, Mathematical Finance -- Data processing Soft Computing (DE-588)4455833-8 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Financial Engineering (DE-588)4208404-0 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Datenverarbeitung Wirtschaft Economics -- Data processing Economics, Mathematical Finance -- Data processing Soft Computing Entscheidungsunterstützung Financial Engineering Entscheidungsunterstützungssystem Neuro-Fuzzy-System Evolutionärer Algorithmus Konferenzschrift 2002 Research Triangle Park, NC Konferenzschrift 2002 Research Triangle Park NC |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016931372&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023553623 |
| work_keys_str_mv | AT chenshuheng computationalintelligenceineconomicsandfinancevol1 |