Computational Intelligence Techniques for Trading and by Christian Dunis, Spiros Likothanassis, Andreas
By Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis, Konstantinos Theofilatos
Computational intelligence, a sub-branch of man-made intelligence, is a box which pulls at the flora and fauna and adaptive mechanisms as a way to research behaviour in altering advanced environments. This booklet presents an interdisciplinary view of present technological advances and demanding situations in regards to the program of computational intelligence ideas to monetary time-series forecasting, buying and selling and funding.
The booklet is split into 5 elements. the 1st half introduces crucial computational intelligence and fiscal buying and selling ideas, whereas additionally featuring an important methodologies from those varied domain names. the second one half is dedicated to the applying of conventional computational intelligence suggestions to the fields of economic forecasting and buying and selling, and the 3rd half explores the purposes of synthetic neural networks in those domain names. The fourth half delves into novel evolutionary-based hybrid methodologies for buying and selling and portfolio administration, whereas the 5th half offers the purposes of complex computational intelligence modelling concepts in monetary forecasting and buying and selling.
This quantity could be helpful for graduate and postgraduate scholars of finance, computational finance, monetary engineering and desktop technological know-how. Practitioners, investors and fiscal analysts also will reap the benefits of this book.
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Additional info for Computational Intelligence Techniques for Trading and Investment
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Moreover, in most cases little is known a priori about the structure of the search space of the problem. To deal with these hard optimization problems a variety of meta-Â�heuristic methods have been proposed in the CI domain. These methods do not guarantee that the optimal solution of a problem will be found. Moreover, most of them do not have any strong mathematical background to justify their performance. They are inspired from nature observations to extract algorithmic solutions for exploring the search space as well as possible while exploiting its promising areas.
SVMs can also be used to separate classes that cannot be separated with a linear classifier. In such cases, the coordinates of the objects are mapped into a feature space using non-Â�linear functions. The feature space in which every object is projected is a high-Â�dimensional space in which the two classes can be separÂ� ated with a linear classifier. SVMs are classification techniques which, however, have been extended in order to enable their application in regression problems such as financial forecasting.