Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Chen, Yan; Mo, Dongxu; Zhang, Feipeng

doi:10.1080/1331677x.2022.2043762

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…When using the association rule mining algorithm to study the relationships of components in a database, it is difficult to determine factors with large weights and their potential linkages, or it is easy to mine little association rules of relevance (Chen, Mo et al., 2022). As a result, we employ the pre‐weighted Eclat (Weclat) algorithm to enhance the association rule mining technique, as depicted in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Risk coupling analysis under accident scenario evolution: A methodological construct and application

Yao,

Zhang,

Wang

et al. 2023

Risk Analysis

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Dynamic processes in various fields exhibit risk coupling phenomena, but existing risk analysis studies tend to ignore the risk coupling effects of dynamic scenarios. Considering the principles of digitization, objective quantification, and the full process that should be adopted in the risk coupling analysis, an integrated risk coupling analysis framework is proposed. Specifically, the weighted Eclat algorithm is used to mine the risk association rules, then the key risk factors are extracted by social network analysis, and the stochastic Petri net is used to complete the construction, simulation, and evolution of accident scenarios. This universal framework can analyze the risk phenomena of accident scenario evolution in a process‐oriented manner and decouple risks based on key risk factors and disconnect the chain of the accident scenario evolution process. Finally, the proposed framework is applied to the coupled analysis of fire risk in Chinese urban communities to verify its feasibility and scientific validity.

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Section: Methodsmentioning

confidence: 99%

Risk coupling analysis under accident scenario evolution: A methodological construct and application

Yao,

Zhang,

Wang

et al. 2023

Risk Analysis

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“…As another promising data science technique, association rule mining can also be used for making decisions in financial institutions (Pawar et al, 2019;Chen et al, 2022;Kartal et al, 2022). The association rule mining aims to discover a pattern of frequently used items appearing together in data sets.…”

Section: Introductionmentioning

confidence: 99%

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

Ataman

Kahraman

2022

Sci. Ann. Econ. Bus.

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With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions.

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Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Cited by 2 publications

References 58 publications

Risk coupling analysis under accident scenario evolution: A methodological construct and application

Risk coupling analysis under accident scenario evolution: A methodological construct and application

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

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