Generating effective defined-contribution pension plan using simulation optimization approach

  • Authors:

  • Tzu-Yi Yu;Hong-Chih Huang;Chun-Lung Chen;Qun-Ting Lin

  • Affiliations:

  • Department of Information Management, National Chi-Nan University, 470 University Road, Puli, Nantou 545, Taiwan, ROC;Department of Risk Management and Insurance, National Chengchi University, No. 64, Sec. 2, Zhi-Nan Road, Taipei 11605, Taiwan, ROC;Department of Management Information Systems, National Chengchi University, No. 64, Sec. 2, Zhi-Nan Road, Taipei 11605, Taiwan, ROC;Department of Risk Management and Insurance, National Chengchi University, No. 64, Sec. 2, Zhi-Nan Road, Taipei 11605, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

This paper presents an optimization approach to analyze the problems of portfolio selection for long-term investments, taking into consideration the specific target replacement ratio for defined-contribution (DC) pension scheme; the purpose is to generate an effective multi-period asset allocation that reaches an amount matching the target liability at retirement date and reduce the downside risk of the investment. A multi-period asset liability simulation model was used to generate 4000 asset return predictions, and an evolutionary algorithm, evolution strategies, was incorporated into the model to generate multi-period asset allocations under four conditions, considering different weights for measuring the importance of matching the target liability and different periods of downside risk measurement. Computational results showed that the evolutionary algorithm, evolution strategies, is a very robust and effective approach to generate promising asset allocations under all the four cases. In addition, computational results showed that the promising asset allocations revealed valuable information, which is able to help fund managers or investors achieve a higher average investment return or a lower level of volatility under different conditions.