توضیحات
ABSTRACT
Computational intelligence techniques are very useful tools for solving problems that involve understanding, modeling, and analyzing large data sets. One of the numerous fields where computational intelligence has found an extremely important role is finance. More precisely, the problem of selecting an investment portfolio to guarantee a given return, at a minimal risk, have been solved using intelligent techniques such as support-vector machines, neural networks, rule-based expert systems, and genetic algorithms. Even though these methods provide good and usually fast approximation of the best investment strategy, they suffer some common drawbacks including the neglect of the dependence among criteria characterizing investment assets (i.e. return, risk, etc.), the ignorance
of the interdependence among assets, and the assumption that all available data are precise and certain. To face these weaknesses, we suggest the use of utility-based multi-criteria decision making setting and fuzzy
integration over intervals.
INTRODUCTION
Given the pervasive nature of computer science, virtually all areas have had to deal with enormous amounts of data. These data alone do not provide much information if they cannot be analyzed, understood, and used to extend knowledge. The strength of computational intelligence is to give a wide variety of techniques that can be used
to process, model and understand these datasets. One of the fields where computational intelligence has been extremely useful is finance. Over the past four thousand years, finance has been studied from various perspectives [8] ranging from basic arithmetic to probabilistic techniques and stochastic modeling, and machine learning approaches. Numerous problems in the area of finance use computational techniques (e.g. data mining, machine learning, stochastic differential equations), to solve problems such as option pricing and portfolio management. The former deals with how to assign a price to a derivative instrument in such a way that arbitrage is not necessary, and is mostly tackled from a stochastic perspective, using models such as Black-Scholes [13]
Year : 2011
Publisher : Reliable Computing
By : Tanja Mago , Xiaojing Wang , Francois Modave , Martine Ceberio
File Information : English Language / 12 Page / Size :124 K
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سال : 2011
ناشر : Reliable Computing
کاری از : Tanja Mago , Xiaojing Wang , Francois Modave , Martine Ceberio
اطلاعات فایل : زبان انگلیسی / 12 صفحه / حجم : 124 k
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