Resource allocation neural network in portfolio selection

Authors:
Po-Chang Ko;Ping-Chen Lin
Affiliations:
Department of Information Management, National Kaohsiung University of Applied Sciences, Taiwan;Institute of Finance and Information, National Kaohsiung University of Applied Sciences, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 5
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Integrating arbitrage pricing theory and artificial neural networks to support portfolio management

Decision Support Systems - Special double issue: unified programming
Neural network techniques for financial performance prediction: integrating fundamental and technical analysis

Decision Support Systems - Special issue: Data mining for financial decision making
Cost functions and model combination for VaR-based asset allocation using neural networks

IEEE Transactions on Neural Networks
An extended ASLD trading system to enhance portfolio management

IEEE Transactions on Neural Networks

A genetic network programming with learning approach for enhanced stock trading model

Expert Systems with Applications: An International Journal
PB-ADVISOR: A private banking multi-investment portfolio advisor

Information Sciences: an International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Portfolio selection is a resource allocation problem in a finance market. The investor's asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation problem. However, the traditional ANN model cannot guarantee the summation of produced investment weight always preserves 100% in output layer. This article introduces a resource allocation neural network model to optimize investment weight of portfolio. This model will dynamically adjust 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 Taiwan Stock Exchange (TSE).