Multi-Layer Allocated Learning Based Neural Network for Resource Allocation Optimization
Po-Chang Ko 0, Ping-Chen Lin, Jan-An You, Yu-Jen Tien
0Department of Information Management, KUAS
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.201How to use a DOI?
- Resource allocation, neural network, allocated learning based NN, portfolio, investment weight
- 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).
- 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 - 2006/10 DA - 2006/10 TI - Multi-Layer Allocated Learning Based Neural Network for Resource Allocation Optimization PB - Atlantis Press SP - 488 EP - 491 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.201 DO - https://doi.org/10.2991/jcis.2006.201 ID - Ko2006/10 ER -