Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

Takano, Yasunari; Gotoh, Jun‐ya

doi:10.1016/j.eswa.2013.11.043

Cited by 20 publications

(6 citation statements)

References 25 publications

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“…The main goal of the mean-variance model is to optimally allocate wealth by considering the trade-off between risk and return. Recently, substantial effort has been devoted to extending the portfolio theory based on Markowitz's framework, including the multi-period portfolio problem with a kernel-based control policy (Takano and Gotoh, 2014), the mean-variance portfolio based on a stochastic benchmark (Bernard and Vanduffel, 2014), the portfolio management with financial ratios and technical indicators (Silva et al 2015), and the multi-objective portfolio considering the dependence structure of asset returns (Babaei et al, 2015). We refer the interested readers to Markowitz (2014) for a detailed discussion about a half-century of research on mean-variance approximations to the expected utility.…”

Section: Introductionmentioning

confidence: 99%

Robust Multi-Period Portfolio Model Based on Prospect Theory and ALMV-PSO Algorithm

2014

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Section: Introductionmentioning

confidence: 99%

Robust Multi-Period Portfolio Model Based on Prospect Theory and ALMV-PSO Algorithm

2014

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“…This section presents a special case of the general problem formulation. We picked functions G(y i ) and f (r s t , y i ) similar to the model developed in Takano and Gotoh (2014). Let m > 0 be some integer and K m (v s t , v k t ) be the kernel function defined as follows:…”

Section: Special Case Of General Formulationmentioning

confidence: 99%

“…In order to avoid the dimensionality problems, Calafiore (2008) modeled the investment decisions as linear functions that remained the same across all scenarios and produced the investment decision based on previous performance of the asset. Takano and Gotoh (2014) modeled the investment decisions with the kernel method, resulting in the nonlinear control functions depending upon returns of instruments.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation of Retirement Portfolios

Maritato

Lane

Murphy

et al. 2022

JRFM

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A retiree with a savings account balance, but without a pension, is confronted with an important investment decision that has to satisfy two conflicting objectives. Without a pension, the function of the savings is to provide post-employment income to the retiree. At the same time, most retirees want to leave an estate to their heirs. Guaranteed income can be acquired by investing in an annuity. However, that decision takes funds away from investment alternatives that might grow the estate. The decision is made even more complicated because one does not know how long one will live. A long life expectancy may require more annuities, and a short life expectancy could promote more risky investments. However there are very mixed opinions about both strategies. A framework has been developed to assess consequences and the trade-offs of alternative investment strategies. We propose a stochastic programming model to frame this complicated problem. The objective is to maximize expected estate value, subject to cash outflow constraints. The model is motivated by the Markowitz mean-variance approach, but with risk measured by CVaR and additional sophisticated constraints. The cash outflow shortages are penalized in the objective function of the problem. We use the kernel method to build position adjustment functions that control how much is invested in each asset. These adjustments nonlinearly depend upon asset returns in previous years. A case study was conducted using two variations of the model. The parameters used in this case study correspond to a typical retirement situation. The case study shows that if the market forecasts are pessimistic, it is optimal to invest in an annuity. The case study results, codes, and data are posted on our website.

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“…for asset allocation with a budget constraint and confirms superior performance to the scenario tree model, see also related references therein. Takano and Gotoh [19,20] develops a nonlinear control policy using kernel method for portfolio optimization under a short sales constraint. In the absence of transaction costs, Cui, Gao, Li and Li [5] achieves multi-period portfolio solutions for the mean-variance investment utility under a short sales constraint.…”

Section: Introductionmentioning

confidence: 99%

Linear Rebalancing Strategy for Multi-Period Dynamic Portfolio Optimization Under Regime Switches

Komatsu

Makimoto

2018

JORSJ

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Although there is a growing interest of applying regime switching models to portfolio optimization, it has never been quite easy as yet to obtain analytical solutions under practical conditions such as self-financing constraints and short sales constraints. In this paper, we extend the linear rebalancing rule proposed in Moallemi and Saglam [17] to regime switching models and provide a multi-period dynamic investment strategy that is comprised of a linear combination of factors with regime dependent coefficients. Under plausible mathematical assumptions, the problem to determine optimal coefficients maximizing a mean-variance utility penalized for transaction costs subject to self-financing and short sales constraints can be formulated as a quadratic programming which is easily solved numerically. To suppress an exponential increase of a number of optimization variables caused by regime switches, we propose a sample space reduction method. From numerical experiments under a practical setting, we confirm that our approach achieves sufficiently reasonable performances, even when sample space reduction is applied for longer investment horizon. The results also show superior performance of our approach to that of the optimal strategy without concerning transaction costs.

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Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

Cited by 20 publications

References 25 publications

Robust Multi-Period Portfolio Model Based on Prospect Theory and ALMV-PSO Algorithm

Robust Multi-Period Portfolio Model Based on Prospect Theory and ALMV-PSO Algorithm

Optimal Allocation of Retirement Portfolios

Linear Rebalancing Strategy for Multi-Period Dynamic Portfolio Optimization Under Regime Switches

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