Pruning Pareto optimal solutions for multi-objective portfolio asset management

Petchrompo, Sanyapong; Wannakrairot, Anupong; Parlikad, Ajith Kumar

doi:10.1016/j.ejor.2021.04.053

Cited by 45 publications

(11 citation statements)

References 50 publications

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“…In view of the variants of the MV model, incorporating additional constraints, e.g., short-selling [73,74], borrowing [75,76], et al, or considering portfolios of short positions [77], exchange-traded funds (ETFs) for multi-asset-class investing [78], et al, will make the portfolio optimization model more suitable for specific application scenarios, as there is an urgent need for the DT-PM to be extended for modeling a DM's complicated preferences in interactive approaches. In addition, while the number of non-dominated solutions of a multi-objective optimization problem such as the MV model produced at each interaction is very large, the pruning method [79] is a very effective way of generating a set with a greatly reduced number of representative non-dominated solutions; the integration of the pruning method into our MV-IMCDM is a promising research topic that presents the potential of enhancing the system's performances.…”

Section: Discussionmentioning

confidence: 99%

A Self-Learning Based Preference Model for Portfolio Optimization

Jia

2021

Mathematics

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An investment in a portfolio can not only guarantee returns but can also effectively control risk factors. Portfolio optimization is a multi-objective optimization problem. In order to better assist a decision maker to obtain his/her preferred investment solution, an interactive multi-criterion decision making system (MV-IMCDM) is designed for the Mean-Variance (MV) model of the portfolio optimization problem. Considering the flexibility requirement of a preference model that provides a guiding role in MV-IMCDM, a self-learning based preference model DT-PM (decision tree-preference model) is constructed. Compared with the present function based preference model, the DT-PM fully considers a decision maker’s bounded rationality. It does not require an assumption that the decision maker’s preference structure and preference change are known a priori and can be automatically generated and completely updated by learning from the decision maker’s preference feedback. Experimental results of a comparison show that, in the case that the decision maker’s preference structure and preference change are unknown a priori, the performances of guidance and fitness of the DT-PM are remarkably superior to function based preference models; in the case that the decision maker’s preference structure is known a priori, the performances of guidance and fitness of the DT-PM is approximated to the predefined function based model. It can be concluded that the DT-PM can agree with the preference ambiguity and the variability of a decision maker with bounded rationality and be applied more widely in a real decision system.

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Section: Discussionmentioning

confidence: 99%

A Self-Learning Based Preference Model for Portfolio Optimization

Jia

2021

Mathematics

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“…information transfer between solutions in the population). EAs achieve SOTA results on a variety of benchmark and real-world problems [4,12,38].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Evolutionary Neural Cascade Search across Supernetworks

Chebykin,

Alderliesten,

Bosman

2022

Preprint

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To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage supernetworks whose subnetworks encode candidate neural network architectures. These subnetworks can be trained simultaneously, 1 This holds for modern state-of-the-art approaches [8,46,55] but does not hold for all, especially older, approaches [30,49].

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“…Generalized autoregressive conditional heteroscedasticity (GARCH) is a common approach for estimating the future volatilities of stocks and portfolios [10]. The use of metaheuristics in solving multi-objective optimization problems for portfolio design, eigen portfolios using principal component analysis, and linear and non-linear programming-based approaches are proposed by some researchers [11][12][13]. Further, fuzzy logic, genetic algorithms (GAs), particle swarm optimization (PSO) are also some of the popular approaches for portfolio design [14][15].…”

Section: Related Workmentioning

confidence: 99%

Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model

Sen

Mondal

Nath

2021

2021 IEEE 18th India Council International Conference (INDICON)

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Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk, and eigen, for four critical economic sectors of India. The prices of the stocks are extracted from the web from Jan 1, 2016, to Dec 31, 2020. Sector-wise portfolios are built based on their ten most significant stocks. An LSTM model is also designed for predicting future stock prices. Six months after the construction of the portfolios, i.e., on Jul 1, 2021, the actual returns and the LSTM-predicted returns for the portfolios are computed. A comparison of the predicted and the actual returns indicate a high accuracy level of the LSTM model.

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Pruning Pareto optimal solutions for multi-objective portfolio asset management

Cited by 45 publications

References 50 publications

A Self-Learning Based Preference Model for Portfolio Optimization

A Self-Learning Based Preference Model for Portfolio Optimization

Evolutionary Neural Cascade Search across Supernetworks

Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model

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