StockProF: a stock profiling framework using data mining approaches

Ng, Keng-Hoong; Khor, Kok-Chin

doi:10.1007/s10257-016-0313-z

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…Ng and Khor (2017) [28] also proposed a stock profiling framework, StockProF, for building stock portfolios rapidly utilizing data mining approaches. The authors utilized the financial data of the plantation stocks listed on Bursa Malaysia and used 1-year stock price movements to evaluate the performance of the clusters and the outliers.…”

Section: Stock Investment Using Data Mining Techniquesmentioning

confidence: 99%

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

Cheng

Huang

et al. 2021

Sustainability

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This study attempts to integrate the decision tree algorithm with the Apriori algorithm to explore the relationship among financial ratio, corporate governance, and stock returns to establish a stock investment decision model. The sports and leisure related industries are employed as the research target. The data are collected and processed for generating decision tree and association rules. Based on the analysis outcome, an investment decision model is constructed for investors expecting to decrease their investment risks and further increase their profits. This stock investment decision model is one type of multiple-criteria decision-making model. This study makes three critical contributions to investors. (1) It proposes a systematical model of exploring related data through the decision tree algorithm and the Apriori algorithm to reveal the implicit investment knowledge. (2) An effective investment decision model is established and expected to provide a reference basis during stock-picking decisions. (3) The investment decision model is enhanced with implicit rules found among variables using association rules.

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Section: Stock Investment Using Data Mining Techniquesmentioning

confidence: 99%

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

Cheng

Huang

et al. 2021

Sustainability

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“…Using the data mining approaches, the Stock Profit Framework (StockProF) has been redefined to create stock portfolios [24]. And, data mining-based performance appraisal framework, to conduct an automatic and comprehensive assessment of the employees on their working ability and job competency [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

Sebt

Komijani

Ghasemi

2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Nowadays, the banking system is known as one of the inherent sectors of customer relationship management systems. Its main advantage is to redesign a more responsive organization to satisfy the customers. The banking system aims to improve the structure of organizations to provide a better customer service through a set of automated and integrated processes. The final goal is to collect and reprocess the personal information of customers. To handle this dilemma, a number of new techniques in data mining provide a powerful tool to explore customers’ information regarding a set of data and tools for customer relationship management. Accordingly, the customers’ classification and coordination of banking system are the main challenging issues of today's world. These reasons motivate the attempts of this study to apply a composition of neural network by considering the C4.5 decision tree and the k-closest neighbor method as a variant of core boosting methodology with maximal strategy. To validate the proposed solution approach, a case study of Ansar Bank in Iran is utilized. From the results, it is observed that the proposed method provides a competitive output with the rate of 95% for the customers’ classification. It also outperforms other existing methods with the rate of C4.5 decision tree, neural network, Naive Bayes and KNN with the rate of 1.04%. The main finding of this research is to propose an algorithm with the error rate of 1.9% and error squared of 0.72% as the best performance among other methods from the literature.<strong></strong></p>

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An Improvement to StockProF: Profiling Clustered Stocks with Class Association Rule Mining

Khor

2016

Advances in Intelligent Systems and Computing

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StockProF: a stock profiling framework using data mining approaches

Cited by 6 publications

References 39 publications

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

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

Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking

An Improvement to StockProF: Profiling Clustered Stocks with Class Association Rule Mining

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