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Data mining in finance : advances in relational and hybrid methods

Data mining in finance : advances in relational and hybrid methods (Loan 8 times)

Material type
단행본
Personal Author
Kovalerchuk, Boris. Vityaev, Evgenii.
Title Statement
Data mining in finance : advances in relational and hybrid methods / by Boris Kovalerchuk and Evgenii Vityaev.
Publication, Distribution, etc
Boston :   Kluwer Academic Publishers ;   Norwell, Mass :   Distributors for North, Central, and South America, Kluwer Academic Publishers,   c2000.  
Physical Medium
xiv, 308 p. : ill. ; 25 cm.
Series Statement
The Kluwer international series in engineering and computer science ; SECS 547
ISBN
0792378040 (acid-free paper)
Bibliography, Etc. Note
Includes bibliographical references and index. Includes bibliographical references (p. [285]-298) and index.
Subject Added Entry-Topical Term
Investments -- Data processing. Stock price forecasting -- Data processing. Data mining.
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082 0 0 ▼a 332.1/0285/63 ▼2 21
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100 1 ▼a Kovalerchuk, Boris.
245 1 0 ▼a Data mining in finance : ▼b advances in relational and hybrid methods / ▼c by Boris Kovalerchuk and Evgenii Vityaev.
260 ▼a Boston : ▼b Kluwer Academic Publishers ; ▼a Norwell, Mass : ▼b Distributors for North, Central, and South America, Kluwer Academic Publishers, ▼c c2000.
300 ▼a xiv, 308 p. : ▼b ill. ; ▼c 25 cm.
440 4 ▼a The Kluwer international series in engineering and computer science ; ▼v SECS 547
504 ▼a Includes bibliographical references and index.
504 ▼a Includes bibliographical references (p. [285]-298) and index.
650 0 ▼a Investments ▼x Data processing.
650 0 ▼a Stock price forecasting ▼x Data processing.
650 0 ▼a Data mining.
700 1 ▼a Vityaev, Evgenii.

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No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 332.10285 K88d Accession No. 111187814 Availability Available Due Date Make a Reservation Service B M
No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Sejong Academic Information Center/Social Science/ Call Number 332.10285 K88d Accession No. 151134535 Availability Available Due Date Make a Reservation Service M

Contents information

Table of Contents

CONTENTS
Foreword by Gregory Piatetsky-Shapiro = xi
Preface = xiii
Acknowledgments = xv
1. The Scope and Methods of the Study
  1.1. Introduction = 1
  1.2. Problem definition = 3
  1.3. Data mining methodologies = 4
    1.3.1. Parameters = 4
    1.3.2. Problem ID and profile = 6
    1.3.3. Comparison of intelligent decision support methods = 7
  1.4. Modern methodologies in financial knowledge discovery = 9
    1.4.1. Deterministic dynamic system approach = 9
    1.4.2. Efficient market theory = 10
    1.4.3. Fundamental and technical analyses = 11
  1.5. Data mining and database management = 12
  1.6. Data mining: definitions and practice = 14
  1.7. Learning paradigms for data mining = 17
  1.8. Intellectual challenges in data mining = 19
2. Numerical data Mining Models with Financial Applications
  2.1. Statistical, autoregression models = 21
    2.1.1. ARIMA models = 22
    2.1.2. Steps in developing ARIMA model = 25
    2.1.3. Seasonal ARIMA = 27
    2.1.4. Exponential smoothing and trading day regression = 28
    2.1.5. Comparison with other methods = 28
  2.2. Financial applications of autoregression models = 30
  2.3. Instance―based learning and financial applications = 32
  2.4. Neural networks = 36
    2.4.1. Introduction = 36
    2.4.2. Steps = 38
    2.4.3. Recurrent networks = 39
    2.4.4. Dynamically modifying network structure = 40
  2.5. Neural networks and hybrid systems in finance = 40
  2.6. Recurrent neural networks in finance = 42
  2.7. Modular networks and genetic algorithms = 44
    2.7.1. Mixture of neural networks = 44
    2.7.2. Genetic algorithms for modular neural networks = 45
  2.8. Testing results and the complete round robin method = 47
    2.8.1. Introduction = 47
    2.8.2. Approach and method = 47
    2.8.3. Multithreaded implementation = 52
    2.8.4. Experiments with SP500 and neural networks = 54
  2.9. Expert mining = 58
  2.10. Interactive Learning of monotone Boolean functions = 66
    2.10.1. Basic definitions and results = 66
    2.10.2. Algorithm for restoring a monotone Boolean function = 67
    2.10.3 Construction of Hansel chains = 69
3. Rule-Based and Hybrid financial Data Mining
  3.1. Decision tree and DNF learning = 71
    3.1.1. Advantages = 71
    3.1.2. Limitation: size of the tree = 72
    3.1.3. Constructing decision trees = 81
    3.1.4. Ensembles and hybrid methods for decision trees = 84
    3.1.5. Discussion = 87
  3.2 Decision tree and DNF learning in finance = 88
    3.2.1. Decision-tree methods in finance = 88
    3.2.2. Extracting decision tree and sets of rules for SP500 = 89
    3.2.3. Sets of decision trees and DNF learning in finance = 93
  3.3. Extracting decision trees from neural networks = 95
    3.3.1. Approach = 95
    3.3.2. Trepan algorithm = 96
  3.4. Extracting decision trees from neural networks in finance = 97
    3.4.1. Predicting the Dollar-Mark exchange rate = 97
    3.4.2. Comparison of performance = 99
  3.5. Probabilistic rules and knowledge-based stochastic modeling = 102
    3.5.1. Probabilistic networks and probabilistic rules = 103
    3.5.2. The na i ·· ve Bayes classifier = 106
    3.5.3. The mixture of experts = 107
    3.5.4. The hidden Markov model = 108
    3.5.5. Uncertainty of the structure of stochastic models = 111
  3.6. Knowledge-based stochastic modeling in finance = 112
    3.6.1. Markov chains in finance = 112
    3.6.2. Hidden Markov models in finance = 114
4. Relational Data Mining (RDM)
  4.1. Introduction = 115
  4.2. Examples = 118
  4.3. Relational data mining paradigm = 123
  4.4. Challenges and obstacles in relational data mining = 127
  4.5. Theory of RDM = 129
    4.5.1. Data types in relational data mining = 129
    4.5.2. Relational representation of examples = 130
    4.5.3. First-order logic and rules = 135
  4.6. Background knowledge = 140
    4.6.1. Arguments constraints and skipping useless hypotheses = 140
    4.6.2. Initial rules and improving search of hypotheses = 141
    4.6.3. Relational data mining and relational databases = 144
  4.7. Algorithms: FOIL and FOCL = 146
    4.7.1. Introduction = 146
    4.7.2. FOIL = 147
    4.7.3. FOCL = 150
  4.8. Algorithm MMDR = 151
    4.8.1. Approach = 151
    4.8.2. MMDR algorithm and existence theorem = 154
    4.8.3. Fisher test = 159
    4.8.4. MMDR pseudocode = 162
    4.8.5. Comparison of FOIL and MMDR = 165
  4.9. Numerical relational data mining = 166
  4.10. Data types = 169
    4.10.1. Problem of data types = 169
    4.10.2. Numerical data type = 174
    4.10.3. Representative measurement theory = 174
    4.10.4. Critical analysis of data types in ABL = 175
  4.11. Empirical axiomatic theories: empirical contents of data = 179
    4.11.1. Definitions = 179
    4.11.2. Representation of data types in empirical axiomatic theories = 181
    4.11.3. Discovering empirical regularities as universal formulas = 186
5. Financial Applications of Relational Data Mining
  5.1. Introduction = 189
  5.2. Transforming numeric data into relations = 191
  5.3. Hypotheses and probabilistic "laws" = 193
  5.4. Markov chains as probabilistic "laws" in finance = 196
  5.5. Learning = 199
  5.6. Method of forecasting = 202
  5.7. Experiment 1 = 204
    5.7.1. Forecasting Performance for hypotheses H1-H4 = 204
    5.7.2. Forecasting Performance for a specific regularity = 207
    5.7.3. Forecasting Performance for Markovian expressions = 209
  5.8. Experiment 2 = 212
  5.9. Interval stock forecast for portfolio selection = 213
  5.10. Predicate invention for financial applications: calendar effects = 215
  5.11. Conclusion = 218
6. Comparison of Performance of RDM and other methods in financial applications
  6.1 Forecasting methods = 219
  6.2. Approach: measures of performance = 220
  6.3. Experiment 1: simulated trading performance = 222
  6.4. Experiment 1: comparison with ARIMA = 225
  6.5. Experiment 2: forecast and simulated gain = 227
  6.6. Experiment 2: analysis of performance = 227
  6.7. Conclusion = 229
7. Fuzzy logic approach and its financial applications
  7.1. Knowledge discovery and fuzzy logic = 231
  7.2. "Human logic" and mathematical principles of uncertainty = 235
  7.3. difference between fuzzy logic and probability theory = 239
  7.4. Basic concepts of fuzzy logic = 240
  7.5. Inference problems and solutions = 248
  7.6. Constructing coordinated contextual linguistic variables = 262
    7.6.1. Examples = 252
    7.6.2. Context space = 259
    7.6.3. Acquisition of fuzzy sets and membership function = 262
    7.6.4. Obtaining linguistic variables = 265
  7.7. Constructing coordinated fuzzy inference = 266
    7.7.1. Approach = 266
    7.7.2. Example = 268
    7.7.3. Advantages of "exact complete" context for fuzzy inference = 270
  7.8. Fuzzy logic in finance = 278
    7.8.1. Review of applications of fuzzy logic in finance = 278
    7.8.2. Fuzzy logic and technical analysis = 281
REFERENCES = 285
Subject Index = 299

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