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

By Boris Kovalerchuk

ISBN-10: 0306470187

ISBN-13: 9780306470189

ISBN-10: 0792378040

ISBN-13: 9780792378044

Data Mining in Finance provides a finished review of significant algorithmic methods to predictive facts mining, together with statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic equipment, after which examines the suitability of those ways to monetary information mining. The booklet focuses particularly on relational information mining (RDM), that's a studying technique capable of research extra expressive ideas than different symbolic techniques. RDM is hence larger fitted to monetary mining, since it is ready to make higher use of underlying area wisdom. Relational info mining additionally has a greater skill to give an explanation for the came across principles - a capability severe for averting spurious styles which unavoidably come up while the variety of variables tested is huge. the sooner algorithms for relational facts mining, often referred to as inductive good judgment programming (ILP), be afflicted by a relative computational inefficiency and feature relatively constrained instruments for processing numerical information.
Data Mining in Finance introduces a brand new technique, combining relational info mining with the research of statistical value of came across principles. This reduces the hunt house and accelerates the algorithms. The publication additionally offers interactive and fuzzy-logic instruments for `mining' the information from the specialists, extra lowering the hunt house.
Data Mining in Finance incorporates a variety of functional examples of forecasting S&P 500, alternate premiums, inventory instructions, and score shares for portfolio, permitting readers to begin development their very own types. This publication is a superb reference for researchers and execs within the fields of man-made intelligence, desktop studying, facts mining, wisdom discovery, and utilized mathematics.

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

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Thus, it is obvious that normalization is needed in order to use information about differences in the stock prices, but the result will be sensitive to the particular normalization used. Two possible normalizations are: 1. normalization based on max values of stock price and volume in the sample: StockPrice(di)/maxStockPrice, StockVolume(d i)/maxStockVolume 2. normalization based on averages in the sample: StockPrice(di)/AverageStockPrice, StockVolume(di)/AverageStockVolume. Similarly, specific normalizations and related data coding are needed for many data types.

The sign forecast is simpler than absolute value forecast and first-order logic methods (Chapters 4 and 5) fit to discover sign forecast rules. Model #1, called a random walk model, was reviewed briefly in chapter 1. 410]. Nevertheless, this model is not applicable to interesting trading strategies. It does not produce ups and downs needed for developing those trading strategies. This model has zero difference for all days. Parameters for the most successful Models 4 and 5 were discovered using the relational data mining approach and the MMDR algorithm described in Chapters 4 and 5.

4). 3. 2. Genetic algorithms for modular neural networks As we have already discussed, financial time series have specific drawbacks, like poor signal-to-noise ratios, non-Gaussian noise distribution, and limited training data. Traditional backpropagation neural networks can address these problems using a mixture of smaller neural networks of differ- ent architectures as described in the previous section. However, backpropagation has other drawbacks: – it does not work if the error functions are not smooth (differentiable), and – it can become trapped in a local minimum of the error function and therefore it can miss the best neural network.

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

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