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Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science, 547) 2000th Edition

by Boris Kovalerchuk (Author), Evgenii Vityaev (Author)

2.7 4 ratings

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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.

ISBN-10

0792378040
ISBN-13

978-0792378044
Edition

2000th
Publisher

Springer
Publication date

April 30, 2000
Language

English
Dimensions

6.38 x 0.94 x 9.46 inches
Print length

324 pages
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Product details

Publisher ‏ : ‎ Springer; 2000th edition (April 30, 2000)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 324 pages
ISBN-10 ‏ : ‎ 0792378040
ISBN-13 ‏ : ‎ 978-0792378044
Item Weight ‏ : ‎ 3.11 pounds
Dimensions ‏ : ‎ 6.38 x 0.94 x 9.46 inches

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A Reader

What a disappointment

Reviewed in the United States on October 3, 2001

This book is badly written. It contains many useless comparisons
between different methods without telling you how to achieve the
best result. You still on your own.

One person found this helpful

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Sandro Saitta

Good examples but missing explanations

Reviewed in the United States on February 22, 2013

With a title such as "Data Mining in Finance: Advances in Relational and Hybrid Methods", one can think that this book is a set of research papers in this topic. However, it is not the case. The authors, Kovalerchuk and Vityaev, have written more than 300 pages about applying data mining techniques in finance.

Although the main topic of the book is relational data mining and its financial applications, there are several other topics such as statistical models, autoregression models, neural networks, decision trees, naive Bayes and fuzzy logic. There are also three other interesting chapters in the book. First, the introduction explains both the data mining (existing techniques) and the financial fields (technical and fundamental analysis). Second, a chapter about application of relational data mining to finance. Third, a chapter with comparison of techniques presented in the book. It can be noted that the foreword is written by Gregory Piatetsky-Shapiro from KDnuggets.

An advantage of the book is that both finance and data mining concepts are explained. Thus, one can read the book without the need of other resources. Before applying a given technique, the authors describe it, explain the data as well as the financial application need. Maybe more attention should have been paid to results and their explanations. They are sometimes confusing and lack some descriptions. Also the book is a little old now (2000) and due to this, one can be disappointed not to see SVM for example. Who knows, maybe in a next edition of the book. Finally, although not up to date, this book should be read by anybody applying data mining in finance.

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Mark Mills

Excellent book in terms outlined by its authors

Reviewed in the United States on June 30, 2002

This is one of the most informative books I've found on the subject of mathematical modeling of financial time series. The book is largely a review of the 'state of the art' and frequently expects the reader to be familiar with or willing to 'find and read' relevant articles, but we can all do that, can't we?
The book sequentially studies
1. Standard ARIMA (autoregressive models) which are closest to familiar linear regression techniques.
2. Neural nets and Bayesian trees (as a category called 'relational data mining' by the authors)
3. Fuzzy logic approaches (described as 'membership functions'. Membership functions are defined in terms of linguistic practice, whatever that is.).
In this way, the authors develop a seemingly comprehensive outline of the field, describing fields of study in terms of increasing abstraction. Of the three, I found the fuzzy logic discussion the most interesting.
I have to express some reservations regarding the perspective taken by the authors. Their view is that of the Newtonian physicist observing the interactions of bodies entirely independent of the viewer. At no point do the authors examine the implication of 'self participation' in the marketplace. For example, what happens to probability distribution 'X' when a trading entity uses the probability distribution 'X' to take a significant position in a security? If this seems interesting, you might try looking at "Theory of Financial Risks: From Statistical Physics to Risk Management", by Bouchaud or "An Introduction to Econophysics: Correlations and Complexity in Finance" by Mantegna and Stanley.

3 people found this helpful

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Vallaud

To read but to complete with some other sources

Reviewed in the United States on August 25, 2005

An interesting book even if the focus on finance on data mining put the reader always at the border line with some very usual statistics techniques. Literature on ARIMA in finance is exponential for instance.

It will be interesting that the authors develop some examples of the cases on existing and major softwares from the market as Clem or SEM

To read if you need to fulfil some knowledge in finance statistic models more than in data mining in finance, for statistic some Sage papers will give you a more pragmatic over view and for data mining read Larose's books too.

3 people found this helpful

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