Resource allocation neural network in portfolio selection

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).

[1]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[2]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[3]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[4]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[5]  Lei Xu,et al.  An extended ASLD trading system to enhance portfolio management , 2003, IEEE Trans. Neural Networks.

[6]  Shin-Yuan Hung,et al.  Integrating arbitrage pricing theory and artificial neural networks to support portfolio management , 1996, Decis. Support Syst..

[7]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[8]  David W. Wright,et al.  Using Artificial Neural Networks to Pick Stocks , 1993 .

[9]  Craig Ellis,et al.  Can a neural network property portfolio selection process outperform the property market , 2005 .

[10]  Guoqiang Peter Zhang,et al.  An investigation of model selection criteria for neural network time series forecasting , 2001, Eur. J. Oper. Res..

[11]  Nicolas Chapados,et al.  Cost functions and model combination for VaR-based asset allocation using neural networks , 2001, IEEE Trans. Neural Networks.

[12]  Stanley G. Eakins,et al.  Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks , 2003 .

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Darius Plikynas,et al.  Analysis of foreign investment impact on the dynamics of national capitalization structure: A computational intelligence approach , 2005 .

[16]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[17]  Po-Chang Ko,et al.  An evolution-based approach with modularized evaluations to forecast financial distress , 2006, Knowl. Based Syst..