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 | | Data Mining in Finance: Advances in Relational and Hybrid Methods (The Springer International Series in Engineering and Computer Science) by Boris Kovalerchuk, Evgenii Vityaev ISBN-10: 9780792378044 ISBN-10: 0-7923-7804-0 ISBN-13: 9780792378044 ISBN-13: 978-0-7923-7804-4 Hardcover 2000-03-01 Springer Find Lowest Price |
Editorials | ||
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. | ||
Reviews | ||
To read but to complete with some other sources 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. | ||
Excellent book in terms outlined by its authors 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 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. | ||
It is a very informative book It is a very informative book with all major data mining methods and their comparisons compressed into 300 pages. Therefore, a significant part of the book is not leisurely reading. This is typical for the books from Kluwer Academic Publishers. One has to be ready to spend enough time to go through algorithms' details, pseudo code and comparisons of algorithms to get a serious benefit for the design of one's own model. For instance, understanding the power of first-order if -then rules over the decision trees gained from the book can significantly change and improve design. | ||
What a disappointment 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. | ||
Good Book 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. | ||