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Deep neural network for optimal retirement consumption in defined contribution pension system
[Réseau de neurones profond pour consommation à la retraite optimale en système de retraite à cotisations définies]

Author

Listed:
  • Wen Chen

    (CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra])

  • Nicolas Langrené

    (CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra])

Abstract
In this paper, we develop a deep neural network approach to solve a lifetime expected mortality-weighted utility-based model for optimal consumption in the decumulation phase of a defined contribution pension system. We formulate this problem as a multi-period finite-horizon stochastic control problem and train a deep neural network policy representing consumption decisions. The optimal consumption policy is determined by personal information about the retiree such as age, wealth, risk aversion and bequest motive, as well as a series of economic and financial variables including inflation rates and asset returns jointly simulated from a proposed seven-factor economic scenario generator calibrated from market data. We use the Australian pension system as an example, with consideration of the government-funded means-tested Age Pension and other practical aspects such as fund management fees. The key findings from our numerical tests are as follows. First, our deep neural network optimal consumption policy, which adapts to changes in market conditions, outperforms deterministic drawdown rules proposed in the literature. Moreover, the out-of-sample outperformance ratios increase as the number of training iterations increases, eventually reaching outperformance on all testing scenarios after less than 10 minutes of training. Second, a sensitivity analysis is performed to reveal how risk aversion and bequest motives change the consumption over a retiree's lifetime under this utility framework. Our results show that stronger risk aversion generates a flatter consumption pattern; however, there is not much difference in consumption with or without bequest until age 103. Third, we provide the optimal consumption rate with different starting wealth balances. We observe that optimal consumption rates are not proportional to initial wealth due to the Age Pension payment. Forth, with the same initial wealth balance and utility parameter settings, the optimal consumption level is different between males and females due to gender differences in mortality. Specifically, the optimal consumption level is slightly lower for females until age 84.

Suggested Citation

  • Wen Chen & Nicolas Langrené, 2020. "Deep neural network for optimal retirement consumption in defined contribution pension system [Réseau de neurones profond pour consommation à la retraite optimale en système de retraite à cotisatio," Working Papers hal-02909818, HAL.
    • Handle: RePEc:hal:wpaper:hal-02909818
    Note: View the original document on HAL open archive server: https://hal.science/hal-02909818v1
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    References listed on IDEAS

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    1. Andréasson, Johan G. & Shevchenko, Pavel V. & Novikov, Alex, 2017. "Optimal consumption, investment and housing with means-tested public pension in retirement," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 32-47.
    2. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin, 2006. "Stochastic lifestyling: Optimal dynamic asset allocation for defined contribution pension plans," Journal of Economic Dynamics and Control, Elsevier, vol. 30(5), pages 843-877, May.
    3. Wilkie, A. D. & Şahin, Şule, 2017. "Yet more on a stochastic economic model: Part 3B: stochastic bridging for retail prices and wages," Annals of Actuarial Science, Cambridge University Press, vol. 11(1), pages 100-127, March.
    4. Huang, Huaxiong & Milevsky, Moshe A. & Salisbury, Thomas S., 2012. "Optimal retirement consumption with a stochastic force of mortality," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 282-291.
    5. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
    6. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    7. Wilkie, A. D. & Şahin, Şule, 2019. "Yet more on a stochastic economic model: Part 5: a vector autoregressive (VAR) Model for retail prices and wages," Annals of Actuarial Science, Cambridge University Press, vol. 13(1), pages 92-108, March.
    8. Wilkie, A.D., 1995. "More on a Stochastic Asset Model for Actuarial Use," British Actuarial Journal, Cambridge University Press, vol. 1(5), pages 777-964, December.
    9. Mao, Hong & Ostaszewski, Krzysztof M. & Wang, Yuling, 2014. "Optimal retirement age, leisure and consumption," Economic Modelling, Elsevier, vol. 43(C), pages 458-464.
    10. Butt, Adam & Deng, Ziyong, 2012. "Investment strategies in retirement: in the presence of a means-tested government pension," Journal of Pension Economics and Finance, Cambridge University Press, vol. 11(2), pages 151-181, April.
    11. Li, Yuying & Forsyth, Peter A., 2019. "A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 189-204.
    12. Ding, Jie & Kingston, Geoffrey & Purcal, Sachi, 2014. "Dynamic asset allocation when bequests are luxury goods," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 65-71.
    13. Peter A. Forsyth & Kenneth R. Vetzal & Graham Westmacott, 2019. "Management of Portfolio Depletion Risk through Optimal Life Cycle Asset Allocation," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(3), pages 447-468, July.
    14. Jin, Zhuo & Liu, Guo & Yang, Hailiang, 2020. "Optimal consumption and investment strategies with liquidity risk and lifetime uncertainty for Markov regime-switching jump diffusion models," European Journal of Operational Research, Elsevier, vol. 280(3), pages 1130-1143.
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    Keywords

    decumulation; retirement income; deep learning; stochastic control; economic scenario generator; defined-contribution pension; optimal consumption;
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