Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science) (Hardcover)

~ (Author), Evgenii Vityaev (Author)

Editorial Reviews

Product Description

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.

Product Details

• Hardcover: 328 pages
• Publisher: Springer; 1 edition (March 1, 2000)
• Language: English
• ISBN-10: 0792378040
• ISBN-13: 978-0792378044
• Product Dimensions: 9.5 x 6.4 x 0.9 inches
• Shipping Weight: 1.3 pounds (View shipping rates and policies)
• Average Customer Review: 3.5 out of 5 stars  See all reviews (6 customer reviews)
• Amazon.com Sales Rank: #1,358,415 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

10 of 14 people found the following review helpful:

4.0 out of 5 stars Good Book, April 19, 2000

By A Customer

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.

1 of 1 people found the following review helpful:

4.0 out of 5 stars Marching to the beat of a different drum, April 29, 2009

By	Dimitri Shvorob (Nashville, Tennessee) - See all my reviews (REAL NAME)

I posted the book's table of contents - see "Customer images" link above - and would like to point out Chapter 4, which discusses "relational data mining", also recommending the closely related Chapters 3 and 7. (But not Chapter 2; the authors' mastery of time-series methods - if these do belong to data mining - is not obvious).

I am skeptical about applicability of the proposed method, and would consult other books on neural nets or fuzzy logic - or Bayesian belief networks, for example, forgotten here - but others may disagree.

Jeers to Kluwer for charging over $100 for a book that really should cost $50-60, yet not hiring a proof-reader. ("Daiwa Secretes"!)

3 of 4 people found the following review helpful:

4.0 out of 5 stars Excellent book in terms outlined by its authors, June 30, 2002

By	Mark Mills (Glen Rose, TX USA) - See all my reviews (REAL NAME)

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.