Get Computational Intelligence in Economics and Finance PDF

By Shu-Heng Chen

ISBN-10: 3642079024

ISBN-13: 9783642079023

ISBN-10: 3662063735

ISBN-13: 9783662063736

Due to the power to deal with particular features of economics and finance forecasting difficulties like e.g. non-linear relationships, behavioral adjustments, or knowledge-based area segmentation, we now have lately witnessed a fantastic progress of the appliance of computational intelligence methodologies during this field.

In this quantity, Chen and Wang accumulated not only works on conventional computational intelligence techniques like fuzzy common sense, neural networks, and genetic algorithms, but additionally examples for newer applied sciences like e.g. tough units, help vector machines, wavelets, or ant algorithms. After an introductory bankruptcy with a structural description of the entire methodologies, the following components describe novel purposes of those to regular economics and finance difficulties like enterprise forecasting, foreign money concern discrimination, foreign currency echange markets, or inventory markets behavior.

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It is then clear that Fourier analysis is very poorly suited to very brief signals, or signals that change suddenly and unpredictably. However, in the area of economics and finance, the brief changes often carry the most interesting and significant information. In order to isolate signal discontinuities, one would like to have some very short basis functions. At the same time, in order to obtain detailed frequency analysis, one would like to have some very long basis functions. The best way to achieve this is to have short high-frequency basis functions and long 32 Shu-Heng Chen and Paul P.

64] used wavelets as the basis for density estimation. 1 Evolutionary Computation: the Common Features The last important pillar of computational intelligence is so-called evolutionary computation (EC). EC uses nature as an inspiration. While it also has a long history of utilization in economics and finance, it is, relatively speaking, a new kid in the block, as compared with neural networks, and even more so as compared to fuzzy logic. It has also drawn less attention from economists and financial analysts than the other two approaches.

J = 1, ... , J). Furthermore, the state j (j = 1, ... , a first-order Markov process with a transition probability matrix P(a, b), where a, b = 1, ... , J. The modeling task is then to identify these J states, the m models, and the distribution pi, ... -. with the transition probability matrix. If the state is not determined by Xl" but by an unobserved series {Zt}, then the Markov process is also called the hidden Markov model. In the latter case, Zt has also to be estimated. In application, this general model may be simplified in different ways.

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Computational Intelligence in Economics and Finance by Shu-Heng Chen

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