Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques

Cheng, Kuo-Chih; Huang, Mu-Jung; Fu, Cheng-Kai; Wang, Kuo-Hua; Wang, Huo-Ming; Lin, Lan-Hui

doi:10.3390/su13063100

Cited by 17 publications

(9 citation statements)

References 25 publications

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“…The data were collected from two sources:Stock market data were collected for companies in Boursa Kuwait from January 6, 2015 to November 26, 2019.Based on the literature review, the available fundamental and economic variables are selected in related work, such as oil price, gold price, the exchange rate of Kuwaiti dinar (KWD) to US dollar (USD), money supply, interest rate, earnings per share (EPS), dividends per share (DPS) and Gulf stock market index namely, Kuwait, Oman, Saudi Arabia, Bahrain and Dubai (Chatzis, Siakoulis, Petropoulos, Stavroulakis, & Vlachogiannakis, 2018; Cheng et al. , 2021; Kim, 2021; Ou & Wang, 2009; Yu et al.…”

Section: Methodsmentioning

confidence: 99%

Stock market prediction by applying big data mining

Alshammari

Aldhmour²,

AlQenaei³

et al. 2022

AGJSR

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PurposeThere is a gap in knowledge about the Gulf Cooperation Council (GCC) because most studies are undertaken in countries outside the Gulf region – such as China, India, the US and Taiwan. The stock market contains rich, valuable and considerable data, and these data need careful analysis for good decisions to be made that can lead to increases in the efficiency of a business. Data mining techniques offer data processing tools and applications used to enhance decision-maker decisions. This study aims to predict the Kuwait stock market by applying big data mining.Design/methodology/approachThe methodology used is quantitative techniques, which are mathematical and statistical models that describe a various array of the relationships of variables. Quantitative methods used to predict the direction of the stock market returns by using four techniques were implemented: logistic regression, decision trees, support vector machine and random forest.FindingsThe results are all variables statistically significant at the 5% level except gold price and oil price. Also, the variables that do not have an influence on the direction of the rate of return of Boursa Kuwait are money supply and gold price, unlike the Kuwait index, which has the highest coefficient. Furthermore, the height score of the variable that affects the direction of the rate of return is the firms, and the accuracy of the overall performance of the four models is nearly 50%.Research limitations/implicationsSome of the limitations identified for this study are as follows: (1) location limitation: Kuwait Stock Exchange; (2) time limitation: the amount of time available to accomplish the study, where the period was completed within the academic year 2019-2020 and the academic year 2020-2021. During 2020, the coronavirus pandemic (COVID-19), which was a major obstacle, occurred during data collection and analysis; (3) data limitation: The Kuwait Stock Exchange data were collected from May 2019 to March 2020, while the factors affecting the stock exchange data were collected in July 2020 due to the corona pandemic.Originality/valueThe study used new titles, variables and techniques such as using data mining to predict the Kuwait stock market. There are no adequate studies that predict the stock market by data mining in the GCC, especially in Kuwait. There is a gap in knowledge in the GCC as most studies are in foreign countries, such as China, India, the US and Taiwan.

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Section: Methodsmentioning

confidence: 99%

Stock market prediction by applying big data mining

Alshammari

Aldhmour²,

AlQenaei³

et al. 2022

AGJSR

View full text Add to dashboard Cite

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“…For financial data mining, Zhang et al (Zhang et al 2020) propose a hierarchical evolutionary algorithm to locate promising search spaces and mine alpha factors in quantitative investment (Sorensen, Chen, and Mussalli 2021). Cheng et al (Cheng et al 2021) present the way they combine decision tree and Apriori algorithm (Agrawal et al 1996;Han, Pei, and Yin 2000) into investment decision models. Chang et al (Chang, Wang, and Chuang 2021) present a data mining pipeline based on neural network, support vector machine, mixed data sampling, etc.…”

Section: Related Workmentioning

confidence: 99%

Financial data analysis application via multi-strategy text processing

Zhu¹

2022

Preprint

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Maintaining financial system stability is critical to economic development, and early identification of risks and opportunities is essential. The financial industry contains a wide variety of data, such as financial statements, customer information, stock trading data, news, etc. Massive heterogeneous data calls for intelligent algorithms for machines to process and understand. This paper mainly focuses on the stock trading data and news about China A-share companies. We present a financial data analysis application, Financial Quotient Porter, designed to combine textual and numerical data by using a multi-strategy data mining approach. Additionally, we present our efforts and plans in deep learning financial text processing application scenarios using natural language processing (NLP) and knowledge graph (KG) technologies. Based on KG technology, risks and opportunities can be identified from heterogeneous data. NLP technology can be used to extract entities, relations, and events from unstructured text, and analyze market sentiment. Experimental results show market sentiments towards a company and an industry, as well as news-level associations between companies.

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“…The study compares the risk and return of an individual sector with the risk and return associated with the market and creates an optimal portfolio. Cheng et al (2021) have applied data mining techniques and decision tree analysis to explore the relationship between financial ratios, corporate governance, and stock return to form a basis for making stock selection decisions. The study also employs another algorithm, the Apriori in association rules to supplement the explanation of mutual influence between various variables.…”

Section: Stock Selection Using Statistical Analysis and Predictive An...mentioning

confidence: 99%

A Multi-Criteria Based Stock Selection Framework in Emerging Market

Biswas

Bandyopadhyay

Pamučar

et al. 2022

Oper. Res. Eng. Sci. Theor. Appl.

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The present study aims to compare the stock performances of the Fast Moving Consumer Goods (FMCG) and Consumer Durables (CD) firms at the Bombay Stock Exchange (BSE), India. It is evident from the extant literature that investment in the stock market depends on two broad objectives such as maximization of return while minimization of risk. Besides, investment decisions are also influenced by the behavioral nature of the investors. To this end, the current work considers the earning prospect (average market return, return on net worth, earning per share, and yield), marketcentric risk (beta), market perception (price to book value, shares traded), momentum (turnover) and benchmarked performance (alpha) to set the criteria for comparison. The study period considers seven consecutive financial years to discern the performance. For the comparative analysis, a combined multi-criteria decision-making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) (used to determine criteria weights) and Evaluation Based on Distance fromAverage Solution (EDAS) (for ranking) methods has been utilized. Borda Count Method (BC), Copeland Method, and Simple Additive Weighting (SAW) have been used to aggregate the year-wise rankings. The calculated weights show consistency to the modern portfolio theory as average return, beta, and return on net worth obtain higher weightage than others. It is observed that there are variations in the year-wise comparative ranking, while on aggregation, FMCG firms dominate the top positions. The analysis reveals that Avanti Feeds Ltd.

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Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques

Cited by 17 publications

References 25 publications

Stock market prediction by applying big data mining

Stock market prediction by applying big data mining

Financial data analysis application via multi-strategy text processing

A Multi-Criteria Based Stock Selection Framework in Emerging Market

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