施工仿真 (219)

Resource allocation neural network in portfolio selection

Po-Chang Ko a ,Ping-Chen Lin

b,*

a

Department of Information Management,National Kaohsiung University of Applied Sciences,Taiwan b

Institute of Finance and Information,National Kaohsiung University of Applied Sciences,Taiwan

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-offbetween 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 tra-ditional 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).

Ó2007Elsevier Ltd.All rights reserved.

Keywords:Resource allocation;Neural network;Portfolio;Investment;Optimization

1.Introduction

The resource allocation problem is a process of allocat-ing a set of resources among a set of entities or activities.It is a complex problem encountered in a variety of areas in operations economics and operation researches,such as portfolio selection,production planning,and computer scheduling.In general,the resource allocation is NP-com-plete if considering a variety of constrains and limitations which are common trade-offs.The intelligent computa-tional techniques such as artificial neural networks (ANNs)would be more suitable to improve the resource allocation problem.ANNs had attracted much more efforts from both academic scholars and industrial practitioners since Rosenblatt first applied single-layer perceptron to pattern classification learning in the late 1950s.Recently,a growing interest researches had been focused on using ANNs in finances and economics because they are powerful to imi-tate flexible nonlinear modeling relationship capabilities.

ANNs require no assumption about the distribution of the underlying data and no restriction about the causal relationship between the dependent variable and indepen-dent variable.

The commonly used neural networks involve five mod-els:(1)The Back-propagation network (BPN)model was proposed by Rumelhart,Hinton,and Williams (1986).This model is based on the error-correction learning rule.In the forward pass,an input vector is propagated through the hidden layer to the output layer.Then,the error signal pro-duced in the output layer would be propagated backward through the network layer and correct the synaptic weight for each neuron recursively.(2)Radial basis functions (RBFs)model was introduced by Broomhead and Lowe (1988)which is proven to be well suited for approximation and pattern classification problems.It is traditionally associated with radial functions in some statistical manner (e.g.Gaussian distribution)in a single-layer network to reveal how learning proceeds.(3)Support vector machine (SVMs)was pioneered by Vapnik in the early 1990s (Vap-nik,1992).SVM is based on the statistical learning theory to construct a hyperplane for classification and regression with maximum margin (Vapnik,1995,1998).(4)Recurrent

0957-4174/$-see front matter Ó2007Elsevier Ltd.All rights reserved.doi:10.1016/j.eswa.2007.07.031

*

Corresponding author.Tel.:+886738145267530.E-mail address:lety@.tw (Ping-Chen Lin).

/locate/eswa

Available online at

Expert Systems with Applications 35(2008)

330–337

Expert Systems with Applications