9th Joint International Conference on Information Sciences (JCIS-06)

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

Authors
Po-Chang Ko 0, Ping-Chen Lin, Jan-An You, Yu-Jen Tien
Corresponding Author
Po-Chang Ko
0Department of Information Management, KUAS
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DOI
https://doi.org/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).
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Publication Date
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ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.201How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Po-Chang Ko
AU  - Ping-Chen Lin
AU  - Jan-An You
AU  - Yu-Jen Tien
PY  - NaN/NaN
DA  - NaN/NaN
TI  - Multi-Layer Allocated Learning Based Neural Network for Resource Allocation Optimization
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.201
DO  - https://doi.org/10.2991/jcis.2006.201
ID  - KoNaN/NaN
ER  -