Titlebook: Natural Computing in Computational Finance; Volume 3 Anthony Brabazon,Michael O’Neill,Dietmar G. Maring Book 2010 Springer-Verlag Berlin He

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书目名称Natural Computing in Computational Finance影响因子(影响力)




书目名称Natural Computing in Computational Finance影响因子(影响力)学科排名




书目名称Natural Computing in Computational Finance网络公开度




书目名称Natural Computing in Computational Finance网络公开度学科排名




书目名称Natural Computing in Computational Finance被引频次




书目名称Natural Computing in Computational Finance被引频次学科排名




书目名称Natural Computing in Computational Finance年度引用




书目名称Natural Computing in Computational Finance年度引用学科排名




书目名称Natural Computing in Computational Finance读者反馈




书目名称Natural Computing in Computational Finance读者反馈学科排名

新陈代谢

Evolutionary Computation and Trade Executionmethods for this task. Furthermore, we build an Agent-based Artificial Stock Market and apply a Genetic Algorithm to evolve an efficient trade execution strategy. Finally, we suggest a number of opportunities for future research.

Kernel

Evolutionary Money Managementong and short positions. Thus, evaluation is contingent on the current market position. Using this architecture the paper investigates the effects of money management. Money management refers to certain measures that traders use to control risk and take profits, but the findings in this paper suggest that it has detrimental effects on performance.

愤世嫉俗者

Book 2010nd practitioners in the fields of computationalfinance and economics. The inspiration for this book was due in part to the success of EvoFIN 2009, the 3rd European Workshop on Evolutionary Computation in Finance and Economics. This book follows on from Natural Computing in Computational Finance Volumes I and II.

vascular

Book 2010pplication of a range of cutting-edge natural computing and agent-basedmethodologies in computational finance and economics. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students a

MELON

Robust Regression with Optimisation Heuristicsg to obtain parameter estimates. Particular emphasis is put on the convergence properties of these techniques for fixed computational resources, and the techniques’ sensitivity for different parameter settings.

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idiopathic

Index Mutual Fund Replicationhts. According to the experiment results, it is found that the proposed model replicates the first two moments of the fund returns by using only five equities. The TE optimization strategy under loss aversion with tolerance triggered rebalancing dominates other combinations studied with regard to tracking ability and cost efficiency.

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