A multiobjective portfolio rebalancing model incorporating transaction costs based on incremental discounts

Mittal, Garima; Mehlawat, Mukesh Kumar

doi:10.1080/02331934.2014.891032

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Later, Woodside-Oriakhi et al introduced a comprehensive model for portfolio optimization and implemented it by quadratic programming of complex integers [5]. Mittal and Mehlawat developed a model involving transaction costs and used genetic algorithms to optimize [6]. With the development of quantitative investment in the international investment market, the research on quantitative investment has gradually arisen in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm

Wang¹,

Liu²,

Zheng³

et al. 2022

Atlantis Highlights in Computer Sciences

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With the development of science and technology, quantitative investment was involved as the three main methods of stock investment together with fundamental analysis and technical analysis. In this paper, a quantitative investment model is proposed to automatically and accurately capture the real-time updated stock data of CSI 300 stocks. Its specific logic is that the K-means clustering model based on factor analysis is used to screen out several good stocks and then automatically calculate the optimal portfolio mode for investors. In addition, using this model, we conducted an empirical study on the Shanghai and Shenzhen 300 stock data from 2018 to 2020. It is concluded that the top ten stocks during this period are ZTE,

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Section: Literature Reviewmentioning

confidence: 99%

Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm

Wang¹,

Liu²,

Zheng³

et al. 2022

Atlantis Highlights in Computer Sciences

View full text Add to dashboard Cite

show abstract

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

Section: ______________________________mentioning

confidence: 99%

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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“…presented an extensive model of portfolio optimization and solved the stated problem by mixed-integer quadratic programming [7]. In 2014 Mittal and Mehlawat proposed a model including transaction costs and also solved an associated optimization problem but by means of real-coded genetic algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

Best portfolio management strategies for synthetic and real assets

Gruszka

Szwabiński

2020

Physica A: Statistical Mechanics and its Applications

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Managing investment portfolios is an old and well know problem in multiple fields including financial mathematics and financial engineering as well as econometrics and econophysics. Multiple different concepts and theories were used so far to describe methods of handling with financial assets, including differential equations, stochastic calculus and advanced statistics. In this paper, using a set of tools from the probability theory, various strategies of building financial portfolios are analysed in different market conditions. A special attention is given to several realisations of a so called balanced portfolio, which is rooted in the natural "buy-low-sell-high" principle. Results show that there is no universal strategy, because they perform differently in different circumstances (e.g. for varying transaction costs). Moreover, the planned time of investment may also have a significant impact on the profitability of certain strategies. All methods have been tested with both simulated trajectories and real data from the Polish stock market.

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A multiobjective portfolio rebalancing model incorporating transaction costs based on incremental discounts

Cited by 7 publications

References 34 publications

Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm

Research on Quantitative Investment of the CSI 300 Stocks Based on Monte Carlo Algorithm

Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Best portfolio management strategies for synthetic and real assets

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