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

2021 ◽  
Vol 13 (6) ◽  
pp. 3100
Author(s):  
Kuo-Chih Cheng ◽  
Mu-Jung Huang ◽  
Cheng-Kai Fu ◽  
Kuo-Hua Wang ◽  
Huo-Ming Wang ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Association Rules ◽  
Decision Model ◽  
Investment Decision ◽  
Multiple Criteria Decision Making ◽  
Apriori Algorithm ◽  
Decision Tree Algorithm ◽  
Stock Investment ◽  
Decision Making Model

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.

scholarly journals Association Rules as a Decision Making Model in the Textile Industry

2017 ◽  
Vol 25 (0) ◽  
pp. 8-14 ◽  
Author(s):  
Višnja Istrat ◽  
Nenad Lalić
Keyword(s):  
Decision Making ◽  
Association Rules ◽  
Open Source Software ◽  
Textile Industry ◽  
Real Life ◽  
Apriori Algorithm ◽  
East Europe ◽  
South East Europe ◽  
Sales Process ◽  
Decision Making Model

Sales process disfunctions in the textile industry are problems that cause loss of customers, incomplete market supply, etc. The objective of the research is to analyse transactions from the textile industry database in order to find patterns in buyers’ behavior and improve the model of decision-making. Association rules, one of the most noticeable data mining techniques, is used as methodology to learn rules and market patterns that occur in sales in the textile industry, which will enhance the decision-making process, by making it more effective and efficient. The Apriori algorithm was applied and open source software Orange was used. It has been shown using a real-life dataset containing 2000 transactions from the textile industry of the South East Europe region that the approach proposed is useful in discovering effective knowledge in data associated with sales. The study reports new interesting rules and the dependence of the following parameters: Support, Confidence, Lift and Leverage on making more customized offers in the textile industry.

Applying the Multi-Criteria Decision Making Model for Ranking Commercial Banks: The Case of Vietnam

2019 ◽  
pp. 125-133
Author(s):  
Duong Truong Thi Thuy ◽  
Anh Pham Thi Hoang
Keyword(s):  
Decision Making ◽  
Commercial Banks ◽  
Decision Model ◽  
Business Models ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Decision Making Model

Banking has always played an important role in the economy because of its effects on individuals as well as on the economy. In the process of renovation and modernization of the country, the system of commercial banks has changed dramatically. Business models and services have become more diversified. Therefore, the performance of commercial banks is always attracting the attention of managers, supervisors, banks and customers. Bank ranking can be viewed as a multi-criteria decision model. This article uses the technique for order of preference by similarity to ideal solution (TOPSIS) method to rank some commercial banks in Vietnam.

Research on Investment Decision-Making of the Low-Carbon Industrial Listed Company Based on the Uncertainty Set Pair Analysis

2010 ◽  
Vol 39 ◽  
pp. 568-574
Author(s):  
Jun Fei Chen ◽  
Jian Qiao Lin
Keyword(s):  
Decision Making ◽  
Decision Model ◽  
Listed Company ◽  
Investment Decision ◽  
Set Pair Analysis ◽  
Low Carbon ◽  
Development Mode ◽  
Investment Decision Making ◽  
Uncertainty Set ◽  
Pair Analysis

As a new economical development mode, “low-carbon economic” is attracting more and more attention all over the world. In this paper, associating with the development background of the low-carbon industry, we applied the uncertainty set pair analysis (SPA) into the investment decision-making of the listed company, and established the investment decision model based on the uncertainty SPA. As a case, we made investment decision analysis to 12 typical low-carbon industrial listed companies selected. The results show that it is effective and applicable, and the research is helpful for the investors conducting decision-making.

Applied Rough Set Logics for Multi-Criteria Decision Analysis in Stock Index Volatility Projection

2011 ◽  
Vol 14 (04) ◽  
pp. 715-735
Author(s):  
Wen-Rong Jerry Ho
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Multiple Criteria Decision Making ◽  
Stock Index ◽  
Rule Based ◽  
Set Up ◽  
Decision Making Model

The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authenticated to employ in forecasting the stock index volatility appropriately.

Application and comparative study of new optimization method for performance ranking of friction materials

2017 ◽  
Vol 232 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Vishal Mahale ◽  
Jayashree Bijwe ◽  
Sujeet Sinha
Keyword(s):  
Decision Making ◽  
Comparative Study ◽  
Decision Model ◽  
Evaluation Method ◽  
Multiple Criteria Decision Making ◽  
Optimization Method ◽  
Friction Material ◽  
Multiple Criteria ◽  
Friction Materials ◽  
Performance Ranking

Good friction materials should satisfy diverse and contradictory performance requirements such as adequate friction ( µ ≈ 0.35–0.45), resistance to wear, fade, squeal, judder, etc. in consort with good recovery and less noise producing tendency. To achieve center point of all these conflicting criteria and selection of best overall performing friction material is multiple criteria decision making (MCDM) problem and very difficult task. Decision maker can easily make decision with single criteria without the help of any optimization tool by maximizing beneficial criteria and minimizing non-beneficial criteria. However, it is extremely challenging task if decision making involves several number of conflicting criteria. Few techniques are reported in the literature such as ‘multiple criteria decision model’, ‘Multi-attribute decision model’, ‘extension evaluation method’ (EEM), etc. for performance ranking of friction materials. However, the simplicity, reliability, applicability, time devoted for the analysis, etc. are always most important aspects of selecting a right tool for the analysis. In this paper application of a technique ‘multiple objective optimization on the basis of ratio analysis’ (MOORA) has been first time employed for performance ranking of friction materials. A comparative study of MOORA and currently used methods MCDM and EEM are also presented. MOORA proved to be the best tool based on the criteria such as simple to use, fast, flexible, and efficient one.

Group Decision Model Based on Ordered Weighted Distance to Aid Decisions on Logistics

2018 ◽  
Vol 26 (02) ◽  
pp. 233-254 ◽  
Author(s):  
Jose Leao E Silva Filho ◽  
Danielle Costa Morais
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Decision Model ◽  
Group Decision ◽  
Decision Rules ◽  
Decision Makers ◽  
Weighted Distance ◽  
Model Based ◽  
Decision Making Model

This paper presents a group decision-making model using a distance aggregator based on Ordered Weighted Distance (OWD) which offers a solution that can reduce disagreement between decision makers (DMs). This paper discusses decision rules and sets out measures to evaluate compensatory effects that have a bearing on DMs’ opinions. The model uses formulations of distances to reveal the differences in opinion among DMs and discusses the meanings of distance and the information presented by each DM. Finally, a case study of a logistics problem is used to illustrate how the model is applied.

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