Optimal Investment Strategies for Pension Funds with Regulation-Conform Dynamic Pension Payment Management in the Absence of Guarantees

Purpose: The purpose of the study was to investigate the determinants influencing pension fund investment performance in Kenya.Methodology: The study employed a descriptive research design. The study target population was all the 33 registered pension funds in Kenya, and the sample size was 66 senior employees involved in decision making. The study adopted a census approach and therefore data was collected from all the 33 registered pension funds. A questionnaire was used to collect primary data from the selected respondents. The data collected was analyzed using the statistical package for social sciences (SPSS) version 23.0. The software was used to produce frequencies, descriptive and inferential statistics which was used to derive generalizations and conclusions regarding the population. Multiple linear regression model was used to measure the relationship between the independent variables and the dependent variable. The study findings were presented using figures and tables.Results: The study findings revealed a positive and significant relationship between diversification decisions, management competency, investment strategies, regulation compliance and investment performance of pension funds in Kenya.Unique contribution to theory, practice and policy: The study recommended that the management of pension funds should establish a strong organization structure and policy implementation, which will enhance their portfolio composition; the firms should have highly competent management; should incorporate investment literacy and capability programs in their organizations; and should continue adhering to the set regulations.

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Optimal Fisheries Investment under Uncertainty

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f83-241 ◽

1983 ◽

Vol 40 (12) ◽

pp. 2080-2091 ◽

Cited By ~ 39

Author(s):

Anthony T. Charles

Keyword(s):

Optimal Investment ◽

Fish Stock ◽

Investment Strategies ◽

Economic Optimization ◽

Stock Management ◽

Investment Under Uncertainty ◽

Capital Costs ◽

Wide Range ◽

Full Analysis ◽

Fishing Fleets

A full analysis of optimal fisheries investment strategies must take into account high levels of uncertainty in future fishery returns, as well as irreversibility of investment in specialized, nonmalleable fishing fleets. A stochastic optimization model is analyzed using dynamic programming to determine optimal policy functions for both fleet investment and fish stock management within an uncertain environment. The resulting policies are qualitatively similar to those found in the corresponding deterministic case, but quantitative differences can be substantial. Simulation results show that optimal fleet capacity should be expected to fluctuate over a fairly wide range, induced by stochastic variations in the biomass. However, the performance of a linear-cost risk-neutral fishery is fairly insensitive to variations in investment and escapement policies around their optimum levels, so that economic optimization is "forgiving" within this context. A framework of balancing upside and downside investment risks is used here to explain the roles of several fishery parameters in relation to optimal investment under uncertainty. In particular, the intrinsic growth rate of the resource and the ratio of unit capital costs to unit operating costs are found to be key parameters in determining whether investment should be higher or lower under uncertainty.

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On the Distribution of Terminal Wealth under Dynamic Mean-Variance Optimal Investment Strategies

SIAM Journal on Financial Mathematics ◽

10.1137/20m1338241 ◽

2021 ◽

Vol 12 (2) ◽

pp. 566-603

Author(s):

Pieter M. van Staden ◽

Duy-Minh Dang ◽

Peter A. Forsyth

Keyword(s):

Optimal Investment ◽

Investment Strategies ◽

Terminal Wealth ◽

Mean Variance

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Optimal Investment Strategies in Defined Contribution Pension Plans

Asset and Liability Management Handbook ◽

10.1057/9780230307230_10 ◽

2011 ◽

pp. 234-279

Author(s):

David Blake ◽

Andrew Cairns ◽

Kevin Dowd

Keyword(s):

Optimal Investment ◽

Pension Plans ◽

Investment Strategies ◽

Defined Contribution

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A diffusion process model for the optimal investment strategies of an R & D project

Journal of Applied Probability ◽

10.1017/s0021900200033751 ◽

1980 ◽

Vol 17 (03) ◽

pp. 646-653 ◽

Cited By ~ 3

Author(s):

Dror Zuckerman

Keyword(s):

Differential Equation ◽

Diffusion Process ◽

Process Model ◽

Compound Poisson Process ◽

Optimal Investment ◽

Investment Strategies ◽

Diffusion Parameters ◽

Decision Variables ◽

Expenditure Rate ◽

Linear Differential

In this article we examine an R & D project in which the project status changes according to a diffusion process. The decision variables include a resource expenditure strategy and a stopping policy which determines when the project should be terminated. The drift and the diffusion parameters of the project status process are assumed to be functions of the resource expenditure rate. The terminal reward from the project is a non-decreasing function of the project status. Our purpose is to select optimal investment strategies under the discounted return criterion. The value of the project is shown to be a solution of a second order, non-linear differential equation. Finally, we derive the optimal investment strategies for an R & D project in which the project status changes according to a non-homogeneous compound Poisson process by using diffusion approximation.

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Mean-variance portfolio selection with an uncertain exit-time in a regime-switching market

RAIRO - Operations Research ◽

10.1051/ro/2018050 ◽

2019 ◽

Vol 53 (4) ◽

pp. 1171-1186

Author(s):

Reza Keykhaei

Keyword(s):

Portfolio Selection ◽

Regime Switching ◽

Exit Time ◽

Efficient Frontier ◽

Optimal Investment ◽

Homogeneous Markov Chain ◽

Investment Strategies ◽

Special Cases ◽

Mean Variance Portfolio ◽

Mean Variance

In this paper, we deal with multi-period mean-variance portfolio selection problems with an exogenous uncertain exit-time in a regime-switching market. The market is modelled by a non-homogeneous Markov chain in which the random returns of assets depend on the states of the market and investment time periods. Applying the Lagrange duality method, we derive explicit closed-form expressions for the optimal investment strategies and the efficient frontier. Also, we show that some known results in the literature can be obtained as special cases of our results. A numerical example is provided to illustrate the results.

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Stochastic Multi-Objective Optimization-Based Life Cycle Cost Analysis for New Construction Materials and Technologies

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119853578 ◽

2019 ◽

Vol 2673 (11) ◽

pp. 466-479

Author(s):

Jingqin Gao ◽

Kaan Ozbay ◽

Hani Nassif ◽

Onur Kalan

Keyword(s):

Life Cycle ◽

Cost Analysis ◽

Life Cycle Cost ◽

Construction Materials ◽

Optimal Investment ◽

Management Decision ◽

Life Cycle Cost Analysis ◽

Investment Strategies ◽

New Materials ◽

New Construction

The sustainability of transportation infrastructure depends on the adoption of new construction materials and technologies that can potentially improve performance and productivity. However, most agencies would like to evaluate these new materials and technologies at both the project and network levels before replacing the traditional ones. It also remains a challenge to reliably estimate the costs and lifetime performance of new construction materials and technologies because of limited implementation data. To address these issues, this paper presents a comprehensive bottom-up methodology based on Life Cycle Cost Analysis (LCCA) to integrate project- and network-level analysis that can fast-track the acceptance of new materials or technologies. Hypothesized improvement rates are applied to the deterioration functions of existing materials to represent the expected improved performance of a new material compared with a conventional material with relatively similar characteristics. This new approach with stochastic treatment allows us to probabilistically evaluate new materials with limited data for their future performance. Feasible maintenance and rehabilitation schedules are found for each facility at the project level and near-optimal investment strategies are identified at the network level by using a metaheuristic evolutionary algorithm while satisfying network-wide constraints. This provides an effective solution to many issues that have not been fully addressed in the past, including the trade-off between multiple objectives, effects of time, uncertainty, and outcome interpretation. A hypothetical bridge deck system from New Jersey’s bridge inventory database is used to demonstrate the applicability of the proposed methodology in constructing a planning and management decision-support procedure.

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