Download Computational Intelligence in Economics and Finance: Volume by Paul P. Wang, Tzu-Wen Kuo PDF

By Paul P. Wang, Tzu-Wen Kuo

Readers will locate, during this hugely suitable and groundbreaking e-book, learn starting from purposes in monetary markets and enterprise management to varied economics difficulties. not just are empirical stories using a number of CI algorithms provided, yet so are also theoretical versions in response to computational equipment. as well as direct purposes of computational intelligence, readers may also detect how those equipment are mixed with traditional analytical tools similar to statistical and econometric versions to yield hottest results.

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Additional resources for Computational Intelligence in Economics and Finance: Volume II

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To enhance the quality of the volume, all chapters, apart from those that are authored by native speakers, have been sent to an English editor for reviewing. In this regard, we are particularly grateful to Bruce Stewart for his excellent editing service. References 1. Adcock A, Thangavel A, Whitfield-Gabrieli S, Knutson B, Gabrieli J (2006) Reward-motivated learning: mesolimbic activation precedes memory formation. Neuron 50(3):507–517 2. Aha D (1997) Lazy learning. Kluwer 3. Aha D, Kibler D, Marc K (1991) Instance-based learning algorithms.

6 Fuzzy Clustering The foregoing fuzzy system allows us to convert and embed empirical qualitative knowledge into reasoning systems capable of performing approximate pattern matching and interpolation. However, these systems cannot adapt or learn because they are unable to extract knowledge from existing data. One approach for overcoming this limitation is to use fuzzy clustering. The essence of fuzzy clustering is that it produces reasonable centers for clusters of data, in the sense that the centers capture the essential feature of the cluster, and then groups data vectors around cluster centers that are reasonably close to them.

12 13 , which shows the membership functions for the fuzzy investment tax rates of a beta one company 14, with assumed investments, liabilities and underwriting profit, before and after the effect of the liability tax shield. Fig. 12. Fuzzy Interest Rate 13 14 Adapted from [27, Figure 1] A beta one company has a completely diversified stock holding, and thus has the same amount of risk (β = 1) as the entire market. 38 Arnold F. 6%). A similar result occurs when both the assets and liabilities are considered.

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