Book
Data Mining in Finance: Advances in Relational and
Hybrid Methods
by Boris Kovalerchuk and Evgenii Vityaev,
Kluwer Acad. Publ, 2000
The Kluwer International Series in Engineering and Computer Science , Vol. 547
ISBN: 0-7923-7804-0
Kluwer's prepublication flyer, 2000 (pdf)
Foreword by Gregory Piatetsky-Shapiro
CONTENTS
1. The Scope and Methods
of the Study
2. Numerical Data Mining
Models with
Financial
Applications
3. Rule-Based and Hybrid
Financial Data Mining
4. Relational Data Mining
(RDM)
5. Financial Applications
of Relational Data Mining
6. Comparison of
Performance of RDM and other methods in financial
applications
7. Fuzzy logic approach
and its financial applications
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Foreword by Gregory Piatetsky-Shapiro
Finding
Profitable Knowledge
The information revolution is generating mountains of data, from sources as
diverse as astronomy observations, credit card transactions, genetics
research, telephone calls, and web clickstreams. At
the same time, faster and cheaper storage technology allows us to store
ever-greater amounts of data online, and better DBMS software provides an
easy access to those data-bases. The web revolution is also expanding the
focus of data mining beyond structured databases to the analysis of text,
hyperlinked web pages, images, sounds, movies and other multimedia data.
Mining financial data presents special challenges. For one, the rewards for
finding successful patterns are potentially enormous, but so are the
difficulties and sources of confusions. The efficient market theory states
that it is practically impossible to predict financial markets long-term.
However, there is good evidence that short-term trends do exist and programs
can be written to find them. The data miners' challenge is to find the trends
quickly while they are valid, as well as to recognize the time when the
trends are no longer effective.
Additional challenges of financial mining are to take into account the
abundance of domain knowledge that describes the intricately inter-related
world of global financial markets and to deal effectively with time series
and calendar effects. For example, Monday and Friday are known to usually have
different effects on S&P 500 than other days of
the week.
The authors present a comprehensive overview of major algorithmic approaches
to predictive data mining, including statistical, neural networks,
rule-based, decision-tree, and fuzzy-logic methods and examine the suitabil-ity of these approaches to financial data
mining.
They focus especially on relational data mining, 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 better use of underlying domain knowledge. Relational data
mining also has a better ability to explain the discovered rules -- ability
critical for avoiding spurious patterns which inevitably arise when the number
of variables ex-amined is very large. The earlier
algorithms for relational data mining, also known as ILP
-- inductive logic programming, suffer from a well-known inefficiency. The
authors introduce a new approach, which combines rela-tional
data mining with the analysis of statistical significance of discovered
rules. This reduces the search space and speeds up the algorithms. The
authors also introduce a set of interactive tools for "mining" the
knowledge from the experts. This helps to further reduce the search space.
The authors' grand tour of the data mining methods contains a number of
practical examples of forecasting S&P 500 and
exchange rates, and allows interested readers to start building their own
models. I expect that this book will be a handy reference to many financially
inclined data miners, who will find the volume both interesting and
profitable.
Gregory Piatetsky-Shapiro
Boston, Massachusetts
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