Fuzzy portfolio optimization for time-inconsistent investors: a multi-objective dynamic approach

Li, You; Wang, Bo; Fu, Anrui; Watada, Junzo

doi:10.1007/s00500-019-04504-3

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…e theory designs a multiobjective control model based on the change of urban rail environment. It ensures the accuracy of the control strategy and the robustness of the control system and meets the target requirements of multiobjective train operation (He and Xiong 2018) [13]. Li et al discussed the multiobjective dynamic portfolio optimization model of investment.…”

Section: Related Workmentioning

confidence: 99%

“…e model designs the multicomponent optimal solution for investors through evolutionary algorithm. e experimental results show that, through this algorithm, the inconsistent model time between investors is optimized, and the algorithm can solve complex nonlinear problems (Li et al 2020) [14]. Zhao et al proposed a multiobjective evolutionary intuitionistic fuzzy clustering algorithm (MOEIFC-MSI) with multi-image spatial information for image segmentation.…”

Section: Related Workmentioning

confidence: 99%

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Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set

Guo¹

2022

Mathematical Problems in Engineering

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With the development of China’s economy, it is required to change the mode of economic growth, from extensive growth mode to intensive growth mode. Therefore, the research on industrial structure is of great significance. This study proposes a regional industrial structure optimization method based on multiobjective optimization and fuzzy set. Firstly, the multiobjective industrial structure evaluation model is constructed, then the evaluation model based on improved fuzzy industrial structure is proposed, and finally the application effect of improved fuzzy industrial structure evaluation model is analyzed. The results show that the first generation iteration speed of the improved fuzzy industrial structure evaluation model is fast, but it is slow in the second to eighth iterations. Compared with the traditional square method, the convergence of the fuzzy structure is improved to a certain extent. The improved evaluation model has also been improved. This is better than other algorithms. Using K-means and Manhattan distance to initialize the clustering method, the results are relatively stable. In terms of running time, Manhattan distance initialization method and K-means clustering method have less iterations and short time-consuming. Therefore, when the data sample is large, the application of K-means clustering algorithm will be more effective.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set

Guo¹

2022

Mathematical Problems in Engineering

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“…Recently, fuzzy optimization has been applied to uncertain environments, especially in financial markets as indicated by Bisht and Srivastava (2019). For example, Li et al (2020) design a multi-objective fuzzy optimization algorithm for portfolio selection of time-inconsistent investors.…”

Section: Studies Of the Exploration-exploitation Trade-offmentioning

confidence: 99%

“…For example, Li et al. ( 2020 ) design a multi-objective fuzzy optimization algorithm for portfolio selection of time-inconsistent investors.…”

Section: Related Workmentioning

confidence: 99%

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

2021

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The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company's revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration-exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

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“…Lee et al [19] consider the limited control of total funds, such as the total, risk, and liquidity, to achieve a distributed strategic asset allocation with global constraints. Li et al [20] discuss fuzzy multiobjective dynamic portfolio optimization for time-inconsistent investors, establish a model to simultaneously maximize the cumulative combined objective function and minimize the cumulative portfolio variance, and design and propose a multiobjective dynamic evolutionary algorithm as a possible solution to the proposed model. A novel approach based on the genetic algorithm (GA) for feature selection and parameter optimization of support vector machine (SVM) is proposed in literature [21].…”

Section: Introductionmentioning

confidence: 99%

Research on Probability Mean-Lower Semivariance-Entropy Portfolio Model with Background Risk

Gao

Sun

2020

Mathematical Problems in Engineering

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In the financial market, investors must deal with uncertain risk, and they also face background risk and many uncertain factors caused by their own characteristics. Considering the fuzzy nature of these factors as well as investors’ risk preferences, transaction costs, and so on, in order to reduce investment risk, an improved probability entropy measure is introduced, and a probability mean-lower semivariance-entropy model with different risk attitudes is established by using fuzzy sets and probability theory. To solve the portfolio model, an improved differential evolution algorithm is proposed and a numerical example is given. The numerical results show that the proposed algorithm is effective and that the model can disperse the financial risk to a certain extent and reasonably solve the portfolio problem under many different conditions.

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Fuzzy portfolio optimization for time-inconsistent investors: a multi-objective dynamic approach

Cited by 10 publications

References 32 publications

Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set

Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set

Novel pricing strategies for revenue maximization and demand learning using an exploration–exploitation framework

Research on Probability Mean-Lower Semivariance-Entropy Portfolio Model with Background Risk

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