Intelligent Financial Portfolio Composition based on Evolutionary Computation Strategies [electronic resource] / by Antonio Gorgulho, Rui F.M.F. Neves, Nuno C.G. Horta.

By: Gorgulho, Antonio [author.]Contributor(s): Neves, Rui F.M.F [author.] | Horta, Nuno C.G [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: SpringerBriefs in Applied Sciences and Technology: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XI, 77 p. 30 illus., 15 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642329890Subject(s): Engineering | Artificial intelligence | Finance | Engineering | Computational Intelligence | Financial Economics | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Preface -- Introduction -- Related Work -- Solution’s Architecture -- System Validation -- Conclusions and Future Work -- References -- Appendixes.
In: Springer eBooksSummary: The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. This subject is becoming popular among computer scientists which try to adapt known Intelligent Computation techniques to the market’s domain. This book proposes a potential system based on Genetic Algorithms, which aims to manage a financial portfolio by using technical analysis indicators. The results are promising since the approach clearly outperforms the remaining approaches during the recent market crash.
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Preface -- Introduction -- Related Work -- Solution’s Architecture -- System Validation -- Conclusions and Future Work -- References -- Appendixes.

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. This subject is becoming popular among computer scientists which try to adapt known Intelligent Computation techniques to the market’s domain. This book proposes a potential system based on Genetic Algorithms, which aims to manage a financial portfolio by using technical analysis indicators. The results are promising since the approach clearly outperforms the remaining approaches during the recent market crash.

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