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Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer...
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by Boris Kovalerchuk and Evgenii Vityaev
Sales Rank: 380830
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Discount: 28 %
$120.00
At Amazon
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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
Book 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.
Book Info
Presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, rule-based decision-tree and fuzzy logic methods. DLC: Investment--Data processing.
Customer Reviews & Comments
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.
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Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer...
Discount: 28 %
Available from Amazon
Price: $120.00
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