Kovalerchuk B., Vityaev B. Data Mining in Finance

New York: Kluwer Academic Publishers, 2002. — 308 p. — (The Kluwer International Series in Engineering and Computer Science).Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data.
Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space.
Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.The Scope and Methods of the StudyProblem definition
Data mining methodologies
Modern methodologies in financial knowledge discovery
Data mining and database management
Data mining: definitions and practice
Learning paradigms for data mining
Intellectual challenges in data mining
Numerical Data Mining Models with Financial Applications
Statistical, autoregression models
Financial applications of autoregression models
Instance–based learning and financial applications
Neural networks
Neural networks and hybrid systems in finance
Recurrent neural networks in finance
Modular networks and genetic algorithms
Testing results and the complete round robin method
Expert mining
Interactive learning of monotone Boolean functions
Rule-Based and Hybrid Financial Data Mining
Decision tree and DNF learning
Decision tree and DNF learning in finance
Extracting decision trees from neural networks
Extracting decision trees from neural networks in finance
Probabilistic rules and knowledge-based stochastic modeling
Knowledge-based stochastic modeling in finance
Relational Data Mining (RDM)Examples
Relational data mining paradigm
Challenges and obstacles in relational data mining
Theory of RDM
Background knowledge
Algorithms: FOIL and FOCL
Algorithm MMDR
Numerical relational data mining
Data types
Empirical axiomatic theories: empirical contents of data
Financial Applications of Relational Data MiningTransforming numeric data into relations
Hypotheses and probabilistic “laws”
Markov chains as probabilistic “laws” in finance
Learning
Method of forecasting
Experiment 1
Experiment 2
Interval stock forecast for portfolio selection
Predicate invention for financial applications: calendar effectsComparison 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 performanceFuzzy 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
Constructing coordinated fuzzy inference
Fuzzy logic in finance