Multi-Layer Allocated Learning Based Neural Network for Resource Allocation Optimization

DOI

10.2991/jcis.2006.201How to use a DOI?

Keywords

Resource allocation, neural network, allocated learning based NN, portfolio, investment weight

Abstract

The investor’s asset allocation choice deeply depends on the trade-off between risk and return. The well-known mean variance method requires predetermined risk and expected return to calculate optimal investment weights of portfolio. The artificial neural network (ANN) with nonlinear capability is proven to solve large-scale complex problem effectively. However, the traditional ANN model cannot guarantee to produce reasonable investment allocation, because the summation of investment weight may not preserve 100% in output layer. This article introduces a multi-layer allocated learning based neural network model and takes financial portfolio as an example to optimize assets allocation weights. This model dynamically adjusts the investment weight as a basis of 100% of summing all of asset weights in the portfolio. The experimental results demonstrate the feasibility of optimal investment weights and superiority of ROI of buy-and-hold trading strategy compared with benchmark TSE (Taiwan Stock Exchange).

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Po-Chang Ko
AU  - Ping-Chen Lin
AU  - Jan-An You
AU  - Yu-Jen Tien
PY  - 2006/10
DA  - 2006/10
TI  - Multi-Layer Allocated Learning Based Neural Network for Resource Allocation Optimization
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SP  - 488
EP  - 491
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.201
DO  - 10.2991/jcis.2006.201
ID  - Ko2006/10
ER  -