Portfolio rebalancing model with transaction costs using interval optimization

Kumar, Prasanna; Panda, Geetanjali; Gupta, Umesh C.

doi:10.1007/s12597-015-0210-0

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…       [10]        [11]       [12]       [13]        [14]       [21]       [22]        [23]…”

Section: ______________________________mentioning

confidence: 99%

“…In this case, transaction costs on purchases are measured by the amount added to the portfolio 0 , and transaction costs on sales are measured by the amount deducted from the initial portfolio 0 . If the investor pays transaction costs proportional to for every added amount to the -th asset ( + ), and proportional to for every deducted amount from the -th asset ( − ), the total transaction costs of rebalancing the portfolio will be derived by (14)…”

Section: Transaction Costsmentioning

confidence: 99%

“…In fact, transaction cost is one of the main constraints for modeling portfolio rebalancing that help in creating more realistic models [3]. Many researchers studied portfolio rebalancing problem considering transaction costs including Sun et al [4], 2006, Fang et al [5], 2006, Fadaei-Nezad and Banaeian [6], 2010, Yu and Lee [7], 2011, Woodside-Oriakhi et al [8], 2013, Gupta et al [9], 2014, Wang et al [10], 2014, Chen et al [11], 2014, Qin et al [12], 2014, Rabbani [13], 2014 and Kumar et al [14], 2015. In order to measure portfolio performance different scholars practiced various models considering different parameters including risk, return, liquidity, investment horizon and so forth.…”

Section: Introductionmentioning

confidence: 99%

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Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Zandieh

Mohaddesi²

2019

IJOR

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In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimization solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver.

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Section: ______________________________mentioning

confidence: 99%

Section: Transaction Costsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Zandieh

Mohaddesi²

2019

IJOR

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“…Out of these, we highlight the work of Bhattacharyya et al (2011) who use the concept of interval numbers in fuzzy set theory to extend the classical mean–variance portfolio selection model into mean–variance–skewness considering transaction costs. Kumar et al (2015) suggest a portfolio rebalancing model where return and risk and some of the constraints lie in intervals. Later, the same authors also propose a multi-objective portfolio selection model, where risk, return as well as decision variables are given as intervals (Kumar et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

Exploring the trade-off between liquidity, risk and return under sectoral diversification across distinct economic settings

Henriques

Neves

2021

JRF

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PurposeThis paper aims to explore the trade-off between liquidity, risk and return under sectoral diversification across distinct economic settings and investment strategies.Design/methodology/approachA novel multi-objective portfolio model is proposed to assess investment decisions under sectoral diversification, where the objective functions and constraints are interval-valued. The objective functions used are risk minimization (through the semi-absolute deviation measure of risk), maximization of liquidity (using turnover as a proxy) and the maximization of logarithmic return. Besides coherence constraints (imposing that the sum of the percentages of investment assigned to each stock should be equal to 100%), constraints regarding the maximum proportion of capital that can be invested (ensuring a minimum level of diversification) and cardinality constraints (to account for transaction costs) are also imposed.FindingsBesides the trade-off between return and risk, the study findings highlight a trade-off between liquidity and return and a positive relationship between risk and liquidity. Under an economic crisis scenario, the trade-off between return and liquidity is reduced. With the economic recovery, the levels of risk increase when contrasted with the setting of the economic crisis. The highest liquidity levels are reached with the economic boom, whereas the highest returns are obtained with the economic recession.Originality/valueThis paper suggests a new modeling approach for assessing the trade-offs between liquidity, risk and return under different scenarios and investment strategies. A new interactive procedure inspired on the reference point approach is also proposed to obtain possibly efficient portfolios according to the investor's preferences. Regarding previous approaches suggested in the literature, this new procedure allows obtaining both supported and unsupported efficient solutions when cardinality constraints are included.

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“…Wu et al [32] proposed an interval portfolio selection model in which both the returns and the risks of the assets are considered as intervals, and obtained a non-inferior solution that improves and generalizes the Markowitz's MV model. A portfolio rebalancing model is studied by Kumar et al [33] with return, risk, fixed transaction cost and variable transaction cost as closed intervals, which can be applied to obtain optimal investment strategy for multiple time horizons. An extension of Lai et al [27]'s model is discussed in Kumar et al [34][35][36] considering proportion of total investment of each stock in terms of closed intervals.…”

Section: Introductionmentioning

confidence: 99%

Stochastic programming technique for portfolio optimization with minimax risk and bounded parameters

2018

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In this paper a portfolio optimization problem with bounded parameters is proposed taking into consideration the minimax risk measure, in which liquidity of the stocks is allied with selection of the portfolio. Interval uncertainty of the model is dealt with through a fusion between interval and random variable. As a result of this, the interval inequalities are converted to chance constraints. A solution methodology is developed using this concept to obtain an efficient portfolio. The theoretical developments are illustrated on a large data set taken from National Stock Exchange, India.

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Portfolio rebalancing model with transaction costs using interval optimization

Cited by 12 publications

References 18 publications

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Exploring the trade-off between liquidity, risk and return under sectoral diversification across distinct economic settings

Stochastic programming technique for portfolio optimization with minimax risk and bounded parameters

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