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Multi-Period Portfolio Selection with Stochastic Investment Horizon

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Optimization and Control for Systems in the Big-Data Era

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 252))

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Abstract

It is often the case that some unexpected events may force an investor to terminate her investment and exit the financial market. In this work, the mean-variance formulation of multi-period portfolio optimization with stochastic investment horizon is considered. Given the distribution of the uncertain investment horizon, the problem under investigation can be formulated as a nonseparable dynamic problem. By making use of the embedding technique of Li and Ng (Math Financ 4(2):387–406, 2000), an analytical optimal strategy and an analytical expression of the mean-variance efficient frontier for the mean-variance formulation of the problem are achieved. Two special cases are also discussed in this work.

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Author information

Authors and Affiliations

  1. Management School, Jinan University, Guangzhou, People’s Republic of China

    Lan Yi

Authors
  1. Lan Yi
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Correspondence to Lan Yi .

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Editors and Affiliations

  1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Tsan-Ming Choi

  2. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China

    Jianjun Gao

  3. Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia, USA

    James H. Lambert

  4. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Chi-Kong Ng

  5. Department of Management Science and Engineering, Business School, Qingdao University, Shandong, China

    Jun Wang

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Yi, L. (2017). Multi-Period Portfolio Selection with Stochastic Investment Horizon. In: Choi, TM., Gao, J., Lambert, J., Ng, CK., Wang, J. (eds) Optimization and Control for Systems in the Big-Data Era. International Series in Operations Research & Management Science, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-319-53518-0_12

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