By Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis, Konstantinos Theofilatos
Computational intelligence, a sub-branch of man-made intelligence, is a box which attracts at the wildlife and adaptive mechanisms so as to research behaviour in altering complicated environments. This booklet offers an interdisciplinary view of present technological advances and demanding situations about the program of computational intelligence options to monetary time-series forecasting, buying and selling and funding.
The e-book is split into 5 components. the 1st half introduces an important computational intelligence and monetary buying and selling suggestions, whereas additionally offering an important methodologies from those varied domain names. the second one half is dedicated to the appliance of conventional computational intelligence suggestions to the fields of monetary 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 functions of complex computational intelligence modelling concepts in monetary forecasting and buying and selling.
This quantity can be beneficial for graduate and postgraduate scholars of finance, computational finance, monetary engineering and laptop technology. Practitioners, investors and fiscal analysts also will make the most of this book.
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Extra info for Computational Intelligence Techniques for Trading and Investment
2013). , 2002), and to advance portfolio management and investment allocation problems (Lin and Liu, 2008). In general, they are famous for their exploration properties, but they fail to perform robust local searching and thus have limited exploitation properties. Moreover, in spite of research attempts to design efficient operators for continuous variable optimÂ� ization, they perform better in binary-Â�represented problems. 2â•‡ Differential evolution Differential evolution (DE) (Das and Suganthan, 2011) is considered as the prevalent stochastic real-Â�parameter optimization algorithm in current use.
Lately, many approaches are trying to deal with this problem. One very crucial step, as already mentioned, is the feature selection step, which not only raises the extracted models’ performance, but provides feedback about which inputs are significant for a given problem. Another technique for alleviating the interpretability need is the utility of the aforementioned techniques for avoiding overfitting. Keeping the models as simple as possible to avoid the bloat effect makes then more easily interpretable without harming their performance.
Likothanassis, ‘Exchange-Â�rates forecasting: a hybrid algorithm based on genetically optimized adaptive neural networks’, Computational Economics 20(3), 2002, 191–210. , ‘The theory of dynamic programming’, Bulletin of the American Mathematical Society 60, 1954, 503–516. ’ in L. Zurada, R. Marks and C. Â€1–12, 1994. , ‘Random Forests’, Machine Learning 45(1), 2001, 5–32. Broomhead, S. and D. Lowe, ‘Multivariate functional interpolation and adaptive networks’, Complex Systems, 2, 1988, 321–355.
Computational Intelligence Techniques for Trading and Investment by Christian Dunis, Spiros Likothanassis, Andreas Karathanasopoulos, Georgios Sermpinis, Konstantinos Theofilatos