|Author||Parque, V. ♦ Mabu, S. ♦ Hirasawa, K.|
|Source||IEEE Xplore Digital Library|
|Publisher||Institute of Electrical and Electronics Engineers, Inc. (IEEE)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Training ♦ Biological system modeling ♦ Economic indicators ♦ Robustness ♦ Testing ♦ Genetics ♦ Measurement|
|Abstract||Financial innovation is continuously testing the asset selection models, which are the key both for building robust portfolios and for managing diversified risk. This paper describes a novel evolutionary based scheme for the asset selection using Robust Genetic Network Programming(r-GNP). The distinctive feature of r-GNP lies in its generalization ability when building the optimal asset selection model, in which several training environments are used throughout the evolutionary approach to avoid the over-fitting problem to the training data. Simulation using stocks, bonds and currencies in developed financial markets show competitive advantages over conventional asset selection schemes.|
|Description||Author affiliation: Graduate School of Information, Production and Systems, Waseda University, Japan (Parque, V.; Mabu, S.; Hirasawa, K.)|
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Educational Use||Research ♦ Reading|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Rights Holder||Institute of Electrical and Electronics Engineers, Inc. (IEEE)|
|Size (in Bytes)||499.96 kB|
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