A model for portfolio selection with order of expected returns

Xia, Yusen; Liu, Baoding; Wang, Shouyang; Lai, Kin Keung

doi:10.1016/s0305-0548(99)00059-3

Cited by 169 publications

(54 citation statements)

References 8 publications

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“…The use of artificial intelligence techniques, imported from statistical learning theory, such as classification trees (Breiman et al, 1984) and neural networks (Desai et al, 1996;Malhotra & Malhotra, 2002) has become increasingly common in credit scoring systems. Statistical learning methods have received great attention in the past decade in finance-related research, for credit scoring and bankruptcy prediction (Li et al, 2006), bankruptcy classification (Lensberg et al, 2006), stress analysis (Gestel et al, 2006) and application for financing decisions and return (West et al, 2005;Xia et al, 2000). In addition, regression techniques (Lee & Chen, 2005) and clustering techniques (Wei et al, 2014) have also been adapted for the credit scoring problem.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Filho

Artes

2018

Prod.

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Paper aims: This paper presents a comparison of the performances of the Bayesian additive regression trees (BART), Random Forest (RF) and the logistic regression model (LRM) for the development of credit scoring models. Originality:It is not usual the use of BART methodology for the analysis of credit scoring data. The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations. The use of credit bureau variables is not usual in academic papers.Research method: Several models were adjusted and their performances were compared by using regular methods. Main findings:The analysis confirms the superiority of the BART model over the LRM for the analyzed data. RF was superior to LRM only for the balanced sample. The best-adjusted BART model was superior to RF. Implications for theory and practice:The paper suggests that the use of BART or RF may bring better results for credit scoring modelling. KeywordsCredit. Machine learning. Logistic regression. BART. Random Forest.

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Section: Bibliographic Reviewmentioning

confidence: 99%

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Filho

Artes

2018

Prod.

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“…After the introduction of the mean-variance model by Markowitz which is the basis for single-period investment portfolio selection models, many developments were performed on the model (see, for example, Xia et al, 2000;Giove et al, 2006: Gupta et al, 2008Yu & Lee, 2011).…”

Section: Single-period Investment Portfoliomentioning

confidence: 99%

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Barati¹,

Mohammadi²,

Naderi³

2016

10.5267/j.ijiec

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Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA) was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.

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“…Markowitz (1952) proposed mean-variance models for the portfolio selection problem and he formulated them mathematically in different ways such as minimizing variance for a given expected value, or maximizing expected value for a given variance. Since then, the mean-variance models have been well developed in both theory and algorithm (Crama & Schyns 2003;Xia et al 2000). The mean-variance models for the portfolio selection problem has been central to research activities of this area and it has served as a basis for the development of modern financial theory for many years (Hao & Liu 2008).…”

Section: Introductionmentioning

confidence: 99%

Possibility theory for multiobjective fuzzy random portfolio optimization

Sadati¹,

Doniavi²,

Samadi³

2014

10.5267/j.dsl

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The problem of portfolio optimization is a standard problem in financial world and it has received tremendous attentions. Portfolio optimization plays essential role in determining portfolio strategies for investors. Portfolio optimization is intrinsically a discrete optimization problem whose decision criteria are in conflict and the proposed study of this paper considers a portfolio optimization problem involving fuzzy random variables. To solve the proposed model, we first present the possibility and necessity-based model to reformulate the fuzzy random portfolio selection model into linear programming models and using the resulted linear programs, a multi-objective problem is constructed. To solve the multi-objective problem we propose some methods to consider decision makers' optimistic and pessimistic views. A numerical example illustrates the whole idea on multiobjective fuzzy random portfolio optimization by possibility and necessity-based model.

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A model for portfolio selection with order of expected returns

Cited by 169 publications

References 8 publications

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Application of bayesian additive regression trees in the development of credit scoring models in Brazil

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Possibility theory for multiobjective fuzzy random portfolio optimization

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