1. Intelligent Database Systems Lab Presenter : BEI-YI JIANG Authors : HAI V. PHAM, ERIC W. COOPER, THANG CAO, KATSUARI KAMEI 2014. INFORMATION SCIENCES Hybrid Kansei-SOM Model using Risk Management and Company Assessment for Stock Trading

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation Even using several commercial trading stock software applications and intelligent systems to make trading decisions, investors may face uncertain conditions in dynamic stock market environments.

4. Intelligent Database Systems Lab Objectives To evaluate companies, select potential companies (superior stocks) and eliminate risky stocks at the right trading time, using Group Decision Making (GDM), together with investment risks, reducing losses and achieving the greatest investment returns.

5. Intelligent Database Systems Lab Methodology

6. Intelligent Database Systems Lab Methodology

7. Intelligent Database Systems Lab Methodology Hybrid Kansei-SOM model

8. Intelligent Database Systems Lab Proposed model and mechanisms of data process Methodology Screen out companies Input data Select potential companies Calculate expert preference distances Compare stock matrix & risk matrix 2.1 Expert preferences 2.2 In Kansei stock matrix 2.3 In Kansei risk matrix 3.1 Visualizing Kansei stock matrix 3.2 Visualizing Kansei stock matrix 4.1 Calculating weights 4.2 Updating weights a. Risk decision matrix b. Expert decision matrix 1. 2. 3. 4. 5.

9. Intelligent Database Systems Lab Methodology Screen out companies Input data Select potential companies Calculate expert preference distances Compare stock matrix & risk matrix 1. 2. 3. 4. 5.

10. Intelligent Database Systems Lab Methodology Screen out companies Input data Select potential companies Calculate expert preference distances Compare stock matrix & risk matrix 2.1 Expert preferences 2.2 In Kansei stock matrix 2.3 In Kansei risk matrix 3.1 Visualizing Kansei stock matrix 3.2 Visualizing Kansei stock matrix 4.1 Calculating weights 4.2 Updating weights 1. 2. 3. 4. 5. a. Risk decision matrix b. Expert decision matrix

11. Intelligent Database Systems Lab Methodology Screen out companies Input data Select potential companies Calculate expert preference distances Compare stock matrix & risk matrix 1. 2. 3. 4. 5.

12. Intelligent Database Systems Lab Fuzzy evaluation model for company assessments and risk management – Kansei evaluation – Quantitative factor for Data Normalization Methodology

13. Intelligent Database Systems Lab Fuzzy evaluation model for company assessments and risk management – Qualitative factor Evaluation using Fuzzy Expression and Inference ›Fuzzy Expression Methodology

14. Intelligent Database Systems Lab Fuzzy evaluation model for company assessments and risk management – Qualitative factor Evaluation using Fuzzy Expression and Inference ›Fuzzy Inference Process Methodology

15. Intelligent Database Systems Lab Fuzzy evaluation model for company assessments and risk management – Kansei risk matrix in an evaluation Methodology

16. Intelligent Database Systems Lab Fuzzy evaluation model for company assessments and risk management – Kansei stock matrix in an evaluation Methodology

17. Intelligent Database Systems Lab Experiments

18. Intelligent Database Systems Lab Experiments

19. Intelligent Database Systems Lab Experiments

20. Intelligent Database Systems Lab Experiments

21. Intelligent Database Systems Lab Experiments

22. Intelligent Database Systems Lab Experiments

23. Intelligent Database Systems Lab Experiments

24. Intelligent Database Systems Lab Conclusions This approach of the proposed system using GDM focuses on applying Kansei evaluation integrated with SOM model to enhance investment capability of trading systems, reduce risky stocks and obtain the greatest investment returns.

25. Intelligent Database Systems Lab Comments Advantages -reduce risky stocks -obtain the returns Applications -stock trading system -risk management