Application of Multi-objective Optimization Algorithm in Financial Market Portfolio

Wu, Yue

doi:10.1007/978-981-16-9735-7_11

Cited by 1 publication

(2 citation statements)

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“…Zhong (2021) introduced the role and benefits of market segmentation, and several approaches including K-means, silhouette score, and elbow method that can be used for segmentation. Many researchers developed portfolio models only for financial market (Chaudhary and Bakhshi, 2021; Huang and Tanaka, 2022; Oprisor and Kwon, 2021; Shi, 2021; Wu, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most enterprises only manage the product portfolio, whereas the market portfolio is unregulated. There are no studies that involve product market portfolios, only financial market portfolios were studied (Chaudhary and Bakhshi, 2021; Chawda and Patel, 2016; Gadzinski et al, 2021; Huang and Tanaka, 2022; Morris and Comeau, 2020; Oprisor and Kwon, 2021; Shi, 2021; Wu, 2022). When products are launched in various markets, the market share and profitability are greater for some products than for others.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Selecting and balancing market portfolio using artificial intelligence and fuzzy multiobjective decision-making model

Shih

Wei

Lin

et al. 2022

Concurrent Engineering

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Most enterprises focus on product portfolio management (PPM) and exclude market portfolio management, and individual markets are selected solely based on financial performance which may not be appropriate because other factors may dictate the outcome of the market selection. This study proposes a market portfolio model that considers market share, market growth, market competition, market risk, and market cost to maximize overall profit. A three-stage approach is proposed to identify potential product markets using an AI algorithm. A set of the most promising market is then determined based on the results of Stage 1 using a fuzzy multi-objective mathematical programming model by concurrently maximizing the total market share and market growth, and minimizing the market competition and market risk. A single-objective mathematical programming model is then used to determine a market portfolio to maximize the total profit using the results for Stage 2. The single-objective model is tested using two sets of threshold constraints. The balance of the market portfolio is discussed and the results are compared. The novelty of the article: (1) the first study involves a product market portfolio, (2) the decision of market selection is scientific (using AI instead of personal judgment), holistic (factors include market share, growth, competition, risk, cost, and profit) and optimized (using mathematical optimization), and (3) the market portfolio maximizes total profit. (4) the proposed method produces a portfolio with a higher profit than the TOPSIS method.

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