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

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

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

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Product details

Series: The Springer International Series in Engineering and Computer Science (Book 547)

Hardcover: 308 pages

Publisher: Springer; 2000 edition (April 30, 2000)

Language: English

ISBN-10: 0792378040

ISBN-13: 978-0792378044

Product Dimensions:

6.1 x 0.8 x 9.2 inches

Shipping Weight: 1.3 pounds (View shipping rates and policies)

Average Customer Review:

2.7 out of 5 stars

4 customer reviews

Amazon Best Sellers Rank:

#9,036,347 in Books (See Top 100 in Books)

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.

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 studies1. 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.

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 SEMTo 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.

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

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

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

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

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