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RecentyearshaveseentheapplicationofvariousNaturalComputing
algorithms for the purposes of ?nancial modelling. In this context
Natural Computing - gorithms can be broadly de?ned as computer
algorithms whose design draws inspirationfromphenomena in the
naturalworld. Particularfeatures of?nancial markets, including
their dynamic and interconnected characteristics, bear p- allels
with processes in the natural world and prima facie, this makes
Natural
Computingmethods'interesting'for?nancialmodellingapplications.
Inaddition to the problem-solving potential of natural processes
which Natural computing seeks to embody in its algorithms, we can
also consider Natural Computing in terms of its potential to
understand the natural processes which themselves serve as
inspiration. For example, ?nancial and biological systems exhibit
the phenomenon of emergence, or the activities of multiple
individual agents c- bining to co-evolve their own environment, and
a stream of work has emerged which applies learning mechanisms
drawn from Natural Computing algorithms for the purposes of
agent-based modelling in ?nance and economics. This book consists
of eleven chapters each of which was selected following a
rigorous,peer-reviewed,selectionprocess.
Thechaptersillustratetheapplication of a range of cutting-edge
natural computing and agent-based methodologies in computational
?nance and economics. While describing cutting edge appli- tions,
the chapters are written so that they are accessible to a wide
audience. Hence, they should be of interest to academics,students
and practitionersin the ?elds of computational ?nance and
economics.
Portfolio Management with Heuristic Optimization consist of two
parts. The first part (Foundations) deals with the foundations of
portfolio optimization, its assumptions, approaches and the
limitations when "traditional" optimization techniques are to be
applied. In addition, the basic concepts of several heuristic
optimization techniques are presented along with examples of how to
implement them for financial optimization problems. The second part
(Applications and Contributions) consists of five chapters,
covering different problems in financial optimization: the effects
of (linear, proportional and combined) transaction costs together
with integer constraints and limitations on the initital endowment
to be invested; the diversification in small portfolios; the effect
of cardinality constraints on the Markowitz efficient line; the
effects (and hidden risks) of Value-at-Risk when used the relevant
risk constraint; the problem factor selection for the Arbitrage
Pricing Theory.
RecentyearshaveseentheapplicationofvariousNaturalComputing
algorithms for the purposes of ?nancial modelling. In this context
Natural Computing - gorithms can be broadly de?ned as computer
algorithms whose design draws inspirationfromphenomena in the
naturalworld. Particularfeatures of?nancial markets, including
their dynamic and interconnected characteristics, bear p- allels
with processes in the natural world and prima facie, this makes
Natural
Computingmethods'interesting'for?nancialmodellingapplications.
Inaddition to the problem-solving potential of natural processes
which Natural computing seeks to embody in its algorithms, we can
also consider Natural Computing in terms of its potential to
understand the natural processes which themselves serve as
inspiration. For example, ?nancial and biological systems exhibit
the phenomenon of emergence, or the activities of multiple
individual agents c- bining to co-evolve their own environment, and
a stream of work has emerged which applies learning mechanisms
drawn from Natural Computing algorithms for the purposes of
agent-based modelling in ?nance and economics. This book consists
of eleven chapters each of which was selected following a
rigorous,peer-reviewed,selectionprocess.
Thechaptersillustratetheapplication of a range of cutting-edge
natural computing and agent-based methodologies in computational
?nance and economics. While describing cutting edge appli- tions,
the chapters are written so that they are accessible to a wide
audience. Hence, they should be of interest to academics,students
and practitionersin the ?elds of computational ?nance and
economics.
Portfolio Management with Heuristic Optimization consist of two
parts. The first part (Foundations) deals with the foundations of
portfolio optimization, its assumptions, approaches and the
limitations when "traditional" optimization techniques are to be
applied. In addition, the basic concepts of several heuristic
optimization techniques are presented along with examples of how to
implement them for financial optimization problems. The second part
(Applications and Contributions) consists of five chapters,
covering different problems in financial optimization: the effects
of (linear, proportional and combined) transaction costs together
with integer constraints and limitations on the initital endowment
to be invested; the diversification in small portfolios; the effect
of cardinality constraints on the Markowitz efficient line; the
effects (and hidden risks) of Value-at-Risk when used the relevant
risk constraint; the problem factor selection for the Arbitrage
Pricing Theory.
Erganzende und vertiefende Lernhilfe zum Lehrbuch"Finanzwirtschaft
fur Fortgeschrittene." Es richtet sich an alle, die uber das
finanzwirtschaftliche Basiswissen hinausgehende Kenntnisse in den
Bereichen zeitbezogene Entscheidungen in der Investitionsplanung,
Portfoliotheorie und Moderne Kapitalmarkttheorie sowie uber die
relevanten Kalkulationszinsfusse in der Investitionsplanung
besitzen."
Erganzende und vertiefende Lernhilfe zum Lehrbuch "Finanzwirtschaft
fur Anfanger." Es richtet sich an alle, die erste Kenntnisse in den
Bereichen Finanzmathematik, Investitionsrechnung und Emission
junger Aktien zu erwerben haben."
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