Personalized goal-based investing via multi-stage stochastic goal programming

Kim, Woo Chang; Kwon, Do-Gyun; Lee, Yongjae; Kim, Jang Ho; Lin, Changle

doi:10.1080/14697688.2019.1662079

Cited by 19 publications

(3 citation statements)

References 31 publications

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“…Individuals must also contend with term life insurance, health insurance, and private pension plans, all of which may be significant products in their financial life-cycle. Finally, we assume that the risk preferences of individuals are described with a standard utility function exhibiting constant relative risk aversion (CRRA) but alternative objectives can be considered (Kim et al 2020). These and other issues will be considered in future work.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Lifetime consumption and investment with housing, deferred annuities and home equity release

Jang

Owadally

Clare

et al. 2021

Quantitative Finance

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We develop a life-cycle optimal investment and consumption model with deferred annuities, housing, mortgages and home equity release. The investor can hold cash, bonds, stocks, annuities and can invest in housing, through renting, purchasing, or a mix of both, with access to variable-rate mortgages. In retirement, the investor can access his housing equity through a form of home equity release called a home reversion contract. Transaction costs, taxes and management fees are explicitly included. The investor's risk preferences are represented by standard power utility derived from housing and non-housing consumption, both before and after retirement, and from bequest. We use multi-stage stochastic programming to solve the optimization problem numerically. Our results show that both non-housing and housing consumption may be higher in retirement when deferred annuities and home reversion are available than when they are absent. The bequest motive has little effect on home reversion and results in a small reduction in overall annuitization.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Lifetime consumption and investment with housing, deferred annuities and home equity release

Jang

Owadally

Clare

et al. 2021

Quantitative Finance

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“…The Enhance topic was research that investigates how traditional financial products and services are implemented and improved by using fintech. Examples include using fintech to improve the traditional activities of making personal consumer loans (Di Maggio & Yao, 2021 ; Gerrans et al, 2021 ), analyzing the creditworthiness of borrowers (Jagtiani & Lemieux, 2019 ), and enhancing customer experience in traditional wealth management (Kim et al, 2020 ). The Impact topic analyses fintech’s influence on industries, governments, and economies and includes the impact of technology on banking industry misconduct (Bertsch et al, 2020 ), fragility of financial institutions that use various technologies (Fung et al, 2020 ), how various technologies are affecting the insurance industry (Stoeckli et al, 2018 ), and the new regulatory models necessary from fintech (Jiang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Fintech: A content analysis of the finance and information systems literature

et al. 2023

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“…Because many real-world optimization problems have got several inaccurate information estimates & goals and conflicting criteria, the stochastic goal programming method suggests an analytical structure aid in modeling and solving such problems. Stochastic goal programming can cope with the inherent uncertainty and has been applied in several fields including Portfolio selection, project selection, resource allocation, Healthcare management, transportation, marketing [21], cash management [22], wealth management [23], economic development, energy consumption, workforce allocation, and greenhouse gas emissions [24], forest planning [25]. Little applications of stochastic goal programming in production planning in manufacturing systems are observed hence the necessity for multi-objective optimization of the manufacturing lot size under stochastic demand.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

S.¹

2022

ijcsrr

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In many manufacturing problems, multi-objective optimizations are representative models, as objectives are considered a conflict with one another. In real-life applications, optimizing a specific solution concerning one objective may end up in unacceptable results concerning the other objectives. Many Manufacturing companies operate under uncertainties and this affects the system performance. Stochastic product demand is one of the challenges faced by manufacturing companies and often affects the manufacturing system’s performance and decision-making. Making the proper decisions regarding manufacturing lot-sizing problems is critical for any manufacturer because it makes the firm compete within the market. In this paper, Markov chains in conjunction with stochastic goal programming were used to develop an optimization model for the manufacturing lot size. The over-achievement or under-achievement of the manufacturing lot size was determined by defining the goal constraints, deviation variables, priorities, and objective function. The different states of demand for the product with stochastic demand were represented by states of a Markov chain. Using the applied mathematics solver in MATLAB TM, the optimization model was then solved, determining the quantity of product to be manufactured in a given quarter of the year as demand changes from one state to another.

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Personalized goal-based investing via multi-stage stochastic goal programming

Cited by 19 publications

References 31 publications

Lifetime consumption and investment with housing, deferred annuities and home equity release

Lifetime consumption and investment with housing, deferred annuities and home equity release

Fintech: A content analysis of the finance and information systems literature

Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand

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