By Boris Kovalerchuk, Evgenii Vityaev,
Publisher: Springer
Number Of Pages: 328
Publication Date: 2000-03-01
Sales Rank: 764149
ISBN / ASIN: 0792378040
EAN: 9780792378044
Binding: Hardcover
Manufacturer: Springer
Studio: Springer
Average Rating: 3.5
TABLE OF CONTENTS
Foreword by Gregory Piatetsky-Shapiro xi
Preface xiii
Acknowledgements xv
1. The Scope and Methods of the Study
Introduction..........................................................................................
Problem definition ........................................................................................
Data mining methodologies ..........................................................................
Parameters....................................................................................
Problem ID and profile ...................................................................
Comparison of intelligent decision support methods........................
1.4 Modern methodologies in financial knowledge discovery .................
Deterministic dynamic system approach ..........................................
Efficient market theory ...............................................................
Fundamental and technical analyses ...............................................
Data mining and database management....................................................
Data mining: definitions and practice .......................................................
Learning paradigms for data mining...........................................................
Intellectual challenges in data mining...........................................................
2. Numerical Data Mining Models with Financial Applications
2.1. Statistical, autoregression models ........................................................
ARIMA models...........................................................................
Steps in developing ARIMA model..............................................
Seasonal ARIMA .....................................................................
Exponential smoothing and trading day regression .....................
Comparison with other methods.........................................................
Financial applications of autoregression models ...................................
Instance–based learning and financial applications ...........................
Neural networks ......................................................................................
Introduction.................................................................................
Steps ...............................................................................................
Recurrent networks....................................................................
Dynamically modifying network structure ....................................
2.5. Neural networks and hybrid systems in finance ....................................
Recurrent neural networks in finance ...................................................
Modular networks and genetic algorithms.........................................
Mixture of neural networks.............................................................
Genetic algorithms for modular neural networks......................
2.8.Testing results and the complete round robin method.......................
Introduction................................................................................
Approach and method ...................................................................
Multithreaded implementation .....................................................
Experiments with SP500 and neural networks ............................
2.9.Expert mining ..........................................................................................
2.10. Interactive learning of monotone Boolean functions .....................
Basic definitions and results .....................................................
Algorithm for restoring a monotone Boolean function..........
Construction of Hansel chains ..................................................
3. Rule-Based and Hybrid Financial Data Mining
3.1. Decision tree and DNF learning.........................................................
Advantages.................................................................................
Limitation: size of the tree.............................................................
Constructing decision trees ............................................................
Ensembles and hybrid methods for decision trees.....................
Discussion ...............................................................................................
3.2.Decision tree and DNF learning in finance........................................
Decision-tree methods in finance...............................................
Extracting decision tree and sets of rules for SP500..................
Sets of decision trees and DNF learning in finance...................
3.3.Extracting decision trees from neural networks.................................
.
Approach....................................................................................
Trepan algorithm..............................................................................
3.4. Extracting decision trees from neural networks in finance................
Predicting the Dollar-Mark exchange rate.................................
Comparison of performance ........................................................
3.5.Probabilistic rules and knowledge-based stochastic modeling........
Probabilistic networks and probabilistic rules.........................
The naïve Bayes classifier ..........................................................
The mixture of experts ................................................................
The hidden Markov model ...........................................................
Uncertainty of the structure of stochastic models ..................
3.6.Knowledge-based stochastic modeling in finance...........................
Markov chains in finance ...............................................................
Hidden Markov models in finance .............................................
DATA MINING IN FINANCE ix
4. Relational Data Mining (RDM)
Introduction......................................................................................
Examples..........................................................................................
Relational data mining paradigm .........................................................
Challenges and obstacles in relational data mining.........................
Theory of RDM ...................................................................................
Data types in relational data mining ..........................................
Relational representation of examples. .....................................
First-order logic and rules............................................................
4.6 Background knowledge .......................................................................
Arguments constraints and skipping useless hypotheses........
Initial rules and improving search of hypotheses.....................
Relational data mining and relational databases ......................
4.7 Algorithms: FOIL and FOCL ...............................................................
Introduction..............................................................................
FOIL...........................................................................................
FOCL .............................................................................................
4.8 Algorithm MMDR ................................................................................
Approach..................................................................................
MMDR algorithm and existence theorem................................
Fisher test.......................................................................................
MMDR pseudocode.......................................................................
4.8.5 Comparison of FOIL and MMDR .............................................
4.9 Numerical relational data mining .........................................................
Data types ............................................................................................
Problem of data types ....................................................................
Numerical data type .................................................................
Representative measurement theory........................................
Critical analysis of data types in ABL .......................................
4.11 Empirical axiomatic theories: empirical contents of data............
Definitions. ..................................................................................
Representation of data types in empirical axiomatic theories.
Discovering empirical regularities as universal formulas........
5. Financial Applications of Relational Data Mining
Introduction......................................................................................
Transforming numeric data into relations.........................................
Hypotheses and probabilistic “laws”................................................
Markov chains as probabilistic “laws” in finance............................
Learning............................................................................
Method of forecasting ...........................................................................
5.7.Experiment 1 .........................................................................................
Forecasting Performance for hypotheses H1-H4 ......................
Forecasting performance for a specific regularity...................
Forecasting performance for Markovian expressions..............
5.8.Experiment 2..........................................................................................
5.9.Interval stock forecast for portfolio selection .....................................
Predicate invention for financial applications: calendar effects..
Conclusion ........................................................................................
6 Comparison of Performance of RDM and other methods in financial
applications
Forecasting methods .............................................................................
Approach: measures of performance ..................................................
Experiment 1: simulated trading performance.................................
Experiment 1: comparison with ARIMA.........................................
Experiment 2: forecast and simulated gain......................................
Experiment 2: analysis of performance...........................................
Conclusion ...........................................................................................
7. Fuzzy logic approach and its financial applications
Knowledge discovery and fuzzy logic.............................................
“Human logic” and mathematical principles of uncertainty...........
Difference between fuzzy logic and probability theory ..................
Basic concepts of fuzzy logic .........................................................
Inference problems and solutions ......................................................
Constructing coordinated contextual linguistic variables................
Examples...................................................................................
Context space ..........................................................................
Acquisition of fuzzy sets and membership function..............
Obtaining linguistic variables .....................................................
7.7.Constructing coordinated fuzzy inference ......................................
Approach..................................................................................
Example ..................................................................................
Advantages of "exact complete" context for fuzzy inference..
7.8.Fuzzy logic in finance..............................................................................
Review of applications of fuzzy logic in finance......................
Fuzzy logic and technical analysis..............................................
REFERENCES......................................................................................
Subject Index........................................................................
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