An interactive approach to stochastic programming-based portfolio optimization

Köksalan, Murat; Şakar, Ceren Tuncer

doi:10.1007/s10479-014-1719-y

Cited by 25 publications

(13 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it calls for the development of stochastic Pareto efficiency concepts discussed above. In contrast, in some of the existing stochastic multiobjective optimization models, summary statistics such as expected value, CVaR or variance are used as the multiple criteria (see, for example, Köksalan and Şakar, 2014, for a stochastic portfolio optimization problem with three criteria: expected return, CVaR and a liquidity measure). Using these summary statistics, the resulting problem becomes a deterministic multicriteria optimization problem for which the well-defined deterministic Pareto optimality concepts can be applied.…”

Section: Coherence and Stochastic Pareto Optimalitymentioning

confidence: 99%

“…One method of obtaining Pareto optimal solutions is to scalarize these multiple criteria using a single weight vector in the scalarization set C. By heuristically searching over C, multiple solutions in the deterministic efficient frontier are generated, and then an interactive method is employed for the decision makers to choose among these solutions. To illustrate this approach, consider a modification of the portfolio optimization problem in Köksalan and Şakar (2014) where G 1 (z) is the uncertain return of the portfolio and G 2 (z) is a random liquidity measure. Suppose that two criteria are considered:…”

Section: Coherence and Stochastic Pareto Optimalitymentioning

confidence: 99%

See 1 more Smart Citation

Robust multicriteria risk-averse stochastic programming models

2017

View full text Add to dashboard Cite

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional value-at-risk (CVaR). First, we introduce a model that optimizes the worst-case multivariate CVaR, and develop a finitely convergent delayed cut generation algorithm for finite probability spaces. We also show that this model can be reformulated as a compact linear program under certain assumptions. In addition, for the cut generation problem, which is in general a mixed-integer program, we give a stronger formulation for the equiprobable case. Next, we observe that similar polyhedral enhancements are also useful for a related class of multivariate CVaR-constrained optimization problems that has attracted attention recently. In our computational study, we use a budget allocation application to compare the decisions from our proposed maximin type risk-averse model with those from its risk-neutral version and the multivariate CVaR-constrained model. Finally, we illustrate the effectiveness of the proposed solution methods for both classes of models.

show abstract

Section: Coherence and Stochastic Pareto Optimalitymentioning

confidence: 99%

Section: Coherence and Stochastic Pareto Optimalitymentioning

confidence: 99%

Robust multicriteria risk-averse stochastic programming models

2017

View full text Add to dashboard Cite

show abstract

“…It is important to know that liabilities shall not be risked in any case, or whether a certain small probability of risking them is acceptable. In the second case, it is possible to apply downside risk measures (Pla-Santamaria and Bravo 2013) such as the value-at-risk (VaR) or the conditional value-at-risk (CVaR) (Köksalan and Tuncer Sakar 2014;Krzemienowski and Szymczyk 2014). Below we summarize the main features of both downside risk measures:…”

Section: Risk Profiling In Behavioral Portfolio Theorymentioning

confidence: 99%

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

et al. 2015

View full text Add to dashboard Cite

The current economic crisis fuels the financial social responsibility after an epoch of many excesses with damaging effects. This work tackles two emerging streams in the financial literature: the behavioral portfolio theory with mental accounting and the socially responsible investment (SRI). Promoting SRI is regarded by a lot of financial experts, policymakers and researchers from the field of economic and social sciences, as one of the potential solutions in order to avoid future crises. Therefore, new models for this investment approach are necessary. We try to support the class of investors that select their investments under a mental accounting framework and also they want to achieve a certain level of SR quality in their portfolios. In order to reconcile the two choice frames, avoiding unnecessary sacrifices in financial performance, we have designed a model based on goal programming that integrates the two cornerstones of the investor. Furthermore, we propose a fuzzy inference system to determine the amount of money allocated to each mental account as well as the confidence level assigned to each mental account. This tool is based on expert knowledge modeled by fuzzy if-then rules.

show abstract

“…Closed-form solutions are only presented with the assumption of independence by Li and Ng (2000), Zhu et al (2004), Yan et al (2009Yan et al ( , 2012, Yu et al (2010Yu et al ( , 2012, Wu and Li (2012), Li and Li (2012). For more general models, the solution is frequently determined by a numerical procedure (see e.g., van Binsbergen and Brandt, 2007;Mansini et al, 2007;Gülpınar and Rustem, 2007;Zhang et al, 2012Liu et al, 2012Liu et al, , 2013Köksalan and Şakar, 2014).…”

Section: Introductionmentioning

confidence: 99%

The Admissible Multiperiod Mean Variance Portfolio Selection Problem with Cardinality Constraints

Zhang¹,

Li²

2017

Industrial Engineering and Management Systems

View full text Add to dashboard Cite

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

show abstract

An interactive approach to stochastic programming-based portfolio optimization

Cited by 25 publications

References 39 publications

Robust multicriteria risk-averse stochastic programming models

Robust multicriteria risk-averse stochastic programming models

Multi-criteria decision making for choosing socially responsible investment within a behavioral portfolio theory framework: a new way of investing into a crisis environment

The Admissible Multiperiod Mean Variance Portfolio Selection Problem with Cardinality Constraints

Contact Info

Product

Resources

About