Portfolio selection with qualitative input

Chiarawongse, Anant; Kiatsupaibul, Seksan; Tirapat, Sunti; Roy, Benjamin Van

doi:10.1016/j.jbankfin.2011.08.005

Cited by 16 publications

(20 citation statements)

References 23 publications

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“…In fact, in section 6 of Chiarawongse et al (2012) it is pointed out with respect to financial applications that "When an analyst offers a qualitative view but is uncertain about its validity, it is useful for the decision maker to be provided with a measure of confidence. This could take the form of a probability that the view is valid.…”

Section: Extensionsmentioning

confidence: 99%

“…The solution to the optimization problem is In this example the estimateμ t is based on the rank constrained statistical estimates proposed by Chiarawongse et al (2012), where the estimates are obtained by a Markov chain Monte Carlo. Here the calculations are replaced by the recursive integration methodology presented in this paper in order to obtain more accurate estimates.…”

Section: Portfolio Selectionmentioning

confidence: 99%

“…Here the calculations are replaced by the recursive integration methodology presented in this paper in order to obtain more accurate estimates. Furthermore, in Chiarawongse et al (2012) the authors perform their experiments based on simulated data sets, whereas in this example a real data set is considered instead of ten industry monthly asset returns during the period of 01/1999 -06/2014 obtained from Kenneth French's web site.…”

Section: Portfolio Selectionmentioning

confidence: 99%

“…Our objective is the same as that in Chiarawongse et al (2012), which is to demonstrate the potential benefit of adopting a rank constrained statistical estimate. Two experiments are performed, one using prior parameter estimatesμ and the other using rank constrained statistical estimatesμ.…”

Section: Portfolio Selectionmentioning

confidence: 99%

“…Furthermore, rankings can also be subjectively derived from a systematic preference aggregation process, as in Kemeny and Snell (1962), Young (1995), and Ali and Meilȃ (2012), for example. The methodology described in this paper is an important tool to directly incorporate such ranking information into the statistical inference process, as discussed by Chiarawongse et al (2012), which can be applied in these various areas of study.…”

Section: Introductionmentioning

confidence: 99%

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Rank constrained distribution and moment computations

Kiatsupaibul

Hayter

Liu

2017

Computational Statistics & Data Analysis

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Consider a set of independent random variables with specified distributions or a set of multivariate normal random variables with a product correlation structure. This paper shows how the distributions and moments of these random variables can be calculated conditional on a specified ranking of their values. This can be useful when the ordering of the variables can be determined without observing the actual values of the variables, as in ranked set sampling, for example. Thus, prior information on the distributions and moments from their individual specified distributions can be updated to provide improved posterior information using the known ranking. While these calculations ostensibly involve high dimensional integral expressions, it is shown how the previously developed general recursive integration methodology can be applied to this problem so that they can be evaluated in a straightforward manner as a series of one-dimensional or two-dimensional integral calculations. Furthermore, the proposed methodology possesses a self-correction mechanism in the computation that prevents any serious growth of the errors. Examples illustrate how different kinds of ranking information affect the distributions, expectations, variances, and covariances of the variables, and how they can be employed to solve a decision making problem.

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Section: Extensionsmentioning

confidence: 99%

Section: Portfolio Selectionmentioning

confidence: 99%

Section: Portfolio Selectionmentioning

confidence: 99%

Section: Portfolio Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rank constrained distribution and moment computations

Kiatsupaibul

Hayter

Liu

2017

Computational Statistics & Data Analysis

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An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection

Gulliksson

Mazur

2019

Comput Econ

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Covariance matrix of the asset returns plays an important role in the portfolio selection. A number of papers is focused on the case when the covariance matrix is positive definite. In this paper, we consider portfolio selection with a singular covariance matrix. We describe an iterative method based on a second order damped dynamical systems that solves the linear rank-deficient problem approximately. Since the solution is not unique, we suggest one numerical solution that can be chosen from the iterates that balances the size of portfolio and the risk. The numerical study confirms that the method has good convergence properties and gives a solution as good as or better than the solutions that are based on constrained least norm Moore-Penrose, Lasso, and naive equal-weighted approaches. Finally, we complement our result with an empirical study where we analyze a portfolio with actual returns listed in S&P 500 index. Keywords Mean-variance portfolio • Singular covariance matrix • Linear ill-posed problems • Second order damped dynamical systems B Stepan Mazur

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Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation

Zhou

Zhang

2019

Applied Soft Computing

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Portfolio selection with qualitative input

Cited by 16 publications

References 23 publications

Rank constrained distribution and moment computations

Rank constrained distribution and moment computations

An Iterative Approach to Ill-Conditioned Optimal Portfolio Selection

Hesitant fuzzy linguistic portfolio model with variable risk appetite and its application in the investment ratio calculation

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