Analysis of Multi-factor Stock Market Choice Portfolio Model Based on Regression

Shi, Yi

doi:10.1109/icdsba53075.2021.00053

Cited by 3 publications

(3 citation statements)

References 9 publications

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…At present, the back testing research on factors mainly focuses on verifying the effectiveness of factor stock selection [3] while ignoring the adaptability of factors and timing strategies. Even if the factors with high stock selection effectiveness have low adaptability to the timing strategy in the model, the ability of the trading model to obtain excess returns and control the maximum back testing is difficult to be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

The Validity of Quantitative Technical Indicator Factors Based on Back Testing

Zhang,

Ding,

Yang

2024

Advances in Transdisciplinary Engineering

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Technical indicator factors can quickly reflect the transformation of current market behavior. The application system in quantitative trading has become increasingly mature in recent years. Back testing is widely used in factor validity tests because of its validity. However, the current research on the effectiveness of technical indicator factors has ignored the adaptability to the model timing strategy, and the use of factors is not differentiated enough. How to carry out reasonable and effective factor validity research has become a difficult problem for many scholars to discuss. This paper first selected representative technical indicators as the research object and crawled the trading data of Chinese A-share listed companies through Python. It then calculated the sample data using computer databases such as Pandas and NumPy. Furthermore, this paper confirms the optimal interval of each factor with the method of back testing. On this basis, it tests the income distribution of each factor and the maximum pullback. It introduces the timing method of the simple moving average for comparison and discusses the feasibility of using a technical indicator strategy to conduct stock selection trading.

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Optimizing Stock Portfolio Performance with a Combined RG1-TOPSIS Model: Insights from the Chinese Market

et al. 2023

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Analysis of Multi-factor Stock Market Choice Portfolio Model Based on Regression

Cited by 3 publications

References 9 publications

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

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

The Validity of Quantitative Technical Indicator Factors Based on Back Testing

Optimizing Stock Portfolio Performance with a Combined RG1-TOPSIS Model: Insights from the Chinese Market

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