Download Data Mining in Finance: Advances in Relational and Hybrid by Boris Kovalerchuk PDF

By Boris Kovalerchuk

ISBN-10: 0306470187

ISBN-13: 9780306470189

ISBN-10: 0792378040

ISBN-13: 9780792378044

Data Mining in Finance provides a finished assessment of significant algorithmic methods to predictive info mining, together with statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic equipment, after which examines the suitability of those methods to monetary facts mining. The booklet focuses particularly on relational information mining (RDM), that's a studying process capable of examine extra expressive ideas than different symbolic ways. RDM is hence higher suited to monetary mining, since it is ready to make better use of underlying area wisdom. Relational info mining additionally has a greater skill to give an explanation for the came upon principles - a capability severe for heading off spurious styles which unavoidably come up while the variety of variables tested is massive. the sooner algorithms for relational facts mining, sometimes called inductive common sense programming (ILP), be afflicted by a relative computational inefficiency and feature quite constrained instruments for processing numerical info.
Data Mining in Finance introduces a brand new process, combining relational facts mining with the research of statistical value of chanced on principles. This reduces the quest area and accelerates the algorithms. The e-book additionally provides interactive and fuzzy-logic instruments for `mining' the data from the specialists, extra lowering the hunt house.
Data Mining in Finance features a variety of functional examples of forecasting S&P 500, trade charges, inventory instructions, and ranking shares for portfolio, permitting readers to begin construction their very own types. This e-book is a superb reference for researchers and execs within the fields of synthetic intelligence, computer studying, facts mining, wisdom discovery, and utilized mathematics.

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Extra resources for Data Mining in Finance: Advances in Relational and Hybrid Methods

Example text

On the other hand, even a very subtle forecasting result combined with an appropriate trading strategy can bring a significant profit. 1 below have a subtle statistical significance, but some of them (Models 4 and 5) were able to produce correct buy/hold/sell signals in 75-79% of cases in simulated trading for two years. 1, where s is the periodic parameter t is the day, T(t) is the target stock for day t, and a,b,c and q are model coefficients. These coefficients were evaluated for models 3-5 using non-linear optimization methods and test data (1995-1996 years).

Indeed, neural networks are widely used in finance. , 1997]. These publications cover basic neural network examples, backpropagation, and data preprocessing as well as more advanced issues. These issues include neural network and fuzzy logic hybrid systems (see chapter 7) and a variety of specific applications: neural network-based financial trading sys- Numerical Data Mining Models and Financial Applications 41 tems and hybrid neural-fuzzy systems for financial modeling and forecasting. Use of backpropagation neural networks in finance is exemplified in the following study [Rao, Rao, 1993].

Another approach, sometimes called “regression without models” [Farlow, 1984] does not assume a class of models. 8 exemplify this approach [Worbos, 1975]. There are sensitive assumptions behind of these approaches. We discuss them and their impact on forecasting in this chapter. 9 is devoted to “expert mining”, that is, methods for extracting knowledge from experts. Models trained from data can serve as artificial “experts” along with or in place of human experts. 10 describes background mathematical facts about the restoration of monotone Boolean functions.

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Data Mining in Finance: Advances in Relational and Hybrid Methods by Boris Kovalerchuk


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