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Optimal Investment with Noise Trading Risk

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Abstract

This paper investigates the optimal dynamic investment for an investor who maximizes constant absolute risk aversion (CARA) utility in a discrete-time market with a riskfree bond and a risky stock. The risky stock is assumed to present both the dividend risk and the price risk. With our assumptions, the dividend risk is equivalent to fundamental risk, and the price risk is equivalent to the noise trading risk. The analytical expression for the optimal investment strategy is obtained by dynamic programming. The main result in this paper highlights the importance of differentiating between noise trading risk and fundamental risk for the optimal dynamic investment.

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Authors and Affiliations

  1. Lingnan College, Sun Yat-Sen University, Guangzhou, 510275, China

    Yunhui XU & Zhongfei LI

  2. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Yunhui XU & Ken Seng TAN

  3. China Institute for Actuarial Science, Central University of Finance and Economics, Beijing, 100081, China

    Ken Seng TAN

Authors
  1. Yunhui XU
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  2. Zhongfei LI
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  3. Ken Seng TAN
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Corresponding author

Correspondence to Yunhui XU.

Additional information

Xu acknowledges the Institute for Quantitative Finance and Insurance (IQFI) at the University of Waterloo. Li would like to acknowledge the National Science Foundation of China under Grant No. 70518001, the National Basic Research Program of China (973 Program) under Grant No. 2007CB814902, and the Social Science & Humanities foundation of Ministry of Education of China under Grant No. 07JA630031. Tan acknowledges the funding from the Canada Research Chairs Program, the Natural Sciences and Engineering Research Council of Canada, and the Cheung Kong Scholar Program of China.

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XU, Y., LI, Z. & TAN, K.S. Optimal Investment with Noise Trading Risk. J Syst Sci Complex 21, 519–526 (2008). https://doi.org/10.1007/s11424-008-9132-8

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  • DOI: https://doi.org/10.1007/s11424-008-9132-8

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