Dietmar G. Maringer – författare

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.

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.