Turnover Prediction Of Shares using Data Mining techniques : A Case Study

Shashaank, D. S.; Sruthi, V.; Vijayalakshimi, M.L.S; Garcia, Jacob Shomona

doi:10.48550/arxiv.1508.00088

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“…There are also studies using the four methods, including A big data approach to Black Friday sales by Awan et al (2021b) which used linear regression, generalized linear regression, random forest and decision tree to predict market trends, and found that linear regression, random forest and generalized linear regression provide an accuracy of 80%-98%, while the decision tree did not perform as well. Shashaank, Sruthi, Vijayalakshimi and Garcia, (2015) also used a full mix of classification algorithmsrandom forest, decision tree, support vector machine and multinomial logistic regressionto predict the stock price. The results of this Indian study showed that random forest had the best prediction performance, followed by decision tree, then SVM and, lastly, multinomial logistic regression.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%