Главная
   Монографии
    -  Интеллектуальный Анализ
        Данных в финансах

    -  Компьютерное познание
   Главы в монографиях
   Реляционный анализ данных
    -  Существо подхода
    -  Теория и методы
     - Сравнение
      с другими методами

   Компьютерное познание
    -  Проблема предсказания
    -  Теория измерений
    -  Вероятностные формальные
      понятия

    -  Проблема индукции
    -  Классификация как закон
   Моделирование когнитивных
   процессов

    -  Принципы
    -  Теория функциональных
      систем

    -  Моделирование аниматов
    -  Восприятие
   Приложения
    -  Финансовый прогноз
    -  Биоинформатика
    -  Медицина
    -  Анализ жульничества
    -  Другие приложения
   Проекты
   Лекции и учебное пособие
   Авторы и контакты
    -  Евгений Витяев
    -  Борис Ковалерчук


Рейтинг@Mail.ru

Last updated 10/02/2016

Монография по финансовому прогнозированию

Boris Kovalerchuk and Evgenii Vityaev
Data Mining in Finance: Advances in Relational and Hybrid Methods, Kluwer Acad. Pub., 2000.
Foreword  by Gregory Piatetsky-Shapiro
Preface   Kluwer flyer

TABLE OF CONTENTS

Foreword by Gregory Piatetsky-Shapiro
Preface
Acknowledgements

1. The Scope and Methods of the Study

1.1 Introduction
1.2 Problem definition
1.3 Data mining methodologies
1.3.1 Parameters
1.3.2 Problem ID and profile
1.3.3 Comparison of intelligent decision support methods
1.4 Modern methodologies in financial knowledge discovery
1.4.1 Deterministic dynamic system approach
1.4.2 Efficient market theory
1.4.3 Fundamental and technical analyses
1.5 Data mining and database management
1.6 Data mining: definitions and practice
1.7 Learning paradigms for data mining
1.8 Intellectual challenges in data mining

2. Numerical Data Mining Models with Financial Applications

2.1. Statistical, autoregression models
2.1.1. ARIMA models
2.1.2. Steps in developing ARIMA model
2.1.3. Seasonal ARIMA
2.1.4. Exponential smoothing and trading day regression
2.1.5. Comparison with other methods.
2.2. Financial applications of autoregression models
2.3. Instance-based learning and financial applications
2.4. Neural networks
2.4.1. Introduction
2.4.2. Steps
2.4.3. Recurrent networks
2.4.4. Dynamically modifying network structure
2.5. Neural networks and hybrid systems in finance
2.6. Recurrent neural networks in finance
2.7. Modular networks and genetic algorithms
2.7.1. Mixture of neural networks
2.7.2. Genetic algorithms for modular neural networks
2.8. Testing results and the complete round robin method
2.8.1. Introduction
2.8.2. Approach and method
2.8.3. Multithreaded implementation
2.8.4. Experiments with SP500 and neural networks
2.9. Expert mining
2.10. Interactive learning of monotone Boolean functions
2.10.1. Basic definitions and results
2.10.2. Algorithm for restoring a monotone Boolean function
2.10.3. Construction of Hansel chains

3. Rule-Based and Hybrid Financial Data Mining

3.1. Decision tree and DNF learning
3.1.1. Advantages
3.1.2. Limitation: size of the tree
3.1.3. Constructing decision trees
3.1.4. Ensembles and hybrid methods for decision trees
3.1.5. Discussion
3.2. Decision tree and DNF learning in finance
3.2.1. Decision-tree methods in finance
3.2.2. Extracting decision tree and sets of rules for SP500
3.2.3. Sets of decision trees and DNF learning in finance
3.3. Extracting decision trees from neural networks
3.3.1. Approach
3.3.2. Trepan algorithm
3.4. Extracting decision trees from neural networks in finance
3.4.1. Predicting the Dollar­Mark exchange rate
3.4.2. Comparison of performance
3.5. Probabilistic rules and knowledge­based stochastic modeling
3.5.1. Probabilistic networks and probabilistic rules
3.5.2. The na?ve Bayes classifier
3.5.3. The mixture of experts
3.5.4. The hidden Markov model
3.5.5. Uncertainty of the structure of stochastic models
3.6. Knowledge­based stochastic modeling in finance
3.6.1. Markov chains in finance
3.6.2. Hidden Markov models in finance

4. Relational Data Mining (RDM)

4.1. Introduction
4.2. Examples
4.3. Relational data mining paradigm
4.4 Challenges and obstacles in relational data mining
4.5 Theory of RDM
4.5.1 Data types in relational data mining
4.5.2 Relational representation of examples.
4.5.3 First-order logic and rules
4.6 Background knowledge
4.6.1 Arguments constraints and skipping useless hypotheses
4.6.2 Initial rules and improving search of hypotheses
4.6.3 Relational data mining and relational databases
4.7 Algorithms: FOIL and FOCL
4.7.1 Introduction
4.7.2 FOIL
4.7.3 FOCL
4.8 Algorithm MMDR
4.8.1 Approach
4.8.2 MMDR algorithm and existence theorem
4.8.3 Fisher test
4.8.4 MMDR pseudocode
4.8.5 Comparison of FOIL and MMDR
4.9 Numerical relational data mining
4.10 Data types
4.10.1 Problem of data types
4.10.2 Numerical data type
4.10.3.Representative measurement theory
4.10.4 Critical analysis of data types in ABL
4.11 Empirical axiomatic theories: empirical contents of data
4.11.1 Definitions.
4.11.2 Representation of data types in empirical axiomatic theories
4.11.3 Discovering empirical regularities as universal formulas

5 Financial Applications of Relational Data Mining

5.1. Introduction
5.2. Transforming numeric data into relations
5.3. Hypotheses and probabilistic "laws"
5.4. Markov chains as "probabilistic laws" in finance
5.5. Learning
5.6. Method of Forecasting
5.7. Experiment 1
5.7.1. Forecasting Performance for hypotheses H1-H4
5.7.2. Forecasting performance for a specific regularity
5.7.3. Forecasting performance for Markovian expressions
5.8. Experiment 2
5.9. Interval stock forecast for portfolio selection
5.10. Predicate invention for financial applications: calendar effects
5.11. Conclusion

6 Comparison of Performance of RDM and other methods in financial applications

6.1. Forecasting methods
6.2. Approach: measures of performance
6.3. Experiment 1: simulated trading performance
6.4. Experiment 1: comparison with ARIMA
6.5. Experiment 2: forecast and simulated gain
6.6. Experiment 2: analysis of performance
6.7. Conclusion

7. Fuzzy logic approach and its financial applications

7.1. Knowledge discovery and fuzzy logic
7.2. "Human logic" and mathematical principles of uncertainty
7.3. Difference between fuzzy logic and probability theory
7.4. Basic concepts of fuzzy logic
7.5. Inference problems and solutions
7.6. Constructing coordinated contextual linguistic variables
7.6.1. Examples
7.6.2. Context space
7.6.3. Acquisition of fuzzy sets and membership function
7.6.4. Obtaining linguistic variables
7.7. Constructing coordinated fuzzy inference
7.7.1. Approach
7.7.2. Example
7.7.3. Advantages of "exact complete" context for fuzzy inference
7.8. Fuzzy logic in finance
7.8.1. Review of applications of fuzzy logic in finance
7.8.2. Fuzzy logic and technical analysis

REFERENCES

Subject Index
xi
xiii
xv



1
3
4
4
6
7
9
9
10
11
12
14
17
19



21
22
25
27
28
28
30
32
36
36
38
39
40
40
42
44
44
45
46
47
47
52
54
58
66
66
67
69



71
71
72
81
84
87
88
88
89
93
95
95
96
97
97
99
102
103
106
107
108
111
112
112
114



115
118
123
127
129
129
130
135
139
139
140
144
146
146
147
150
151
151
154
160
163
165
166
169
169
174
175
176
180
180
181
187



189
191
193
196
199
202
204
204
207
209
212
213
215
218




219
220
222
225
227
228
229



231
235
239
240
248
251
252
259
262
264
266
266
268
270
278
278
281

285

299