Investment choices: Indivisible non-marketable assets and suboptimal solutions

ABSTRACT Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

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Pareto suboptimal solutions under coalitions of disturbances

2016 International Conference Stability and Oscillations of Nonlinear Control Systems (Pyatnitskiy's Conference) ◽

10.1109/stab.2016.7541161 ◽

2016 ◽

Cited By ~ 1

Author(s):

Dmitry V. Balandin ◽

Mark M. Kogan

Keyword(s):

Suboptimal Solutions

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An evolutionary approach to the vehicle route planning in e-waste mobile collection on demand

Soft Computing ◽

10.1007/s00500-021-05665-w ◽

2021 ◽

Vol 25 (8) ◽

pp. 6665-6680

Author(s):

Krzysztof Szwarc ◽

Piotr Nowakowski ◽

Urszula Boryczka

Keyword(s):

Tabu Search ◽

Quality Evaluation ◽

Memetic Algorithm ◽

Route Planning ◽

Memetic Algorithms ◽

Approximate Methods ◽

On Demand ◽

Suboptimal Solutions ◽

Novel Method ◽

The Given

AbstractThe article discusses the utilitarian problem of the mobile collection of waste electrical and electronic equipment. Due to its $$\mathcal {NP}$$ NP -hard nature, implies the application of approximate methods to discover suboptimal solutions in an acceptable time. The paper presents the proposal of a novel method of designing the Evolutionary and Memetic Algorithms, which determine favorable route plans. The recommended methods are determined using quality evaluation indicators for the techniques applied herein, subject to the limits characterizing the given company. The proposed Memetic Algorithm with Tabu Search provides much better results than the metaheuristics described in the available literature.

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