Abstract
Stock markets are dynamically changing in dynamics under uncertainty and risk. The notion of stock trading under uncertainty in stochastic investment trading systems that satisfies dynamic trading problems in market dynamics. This paper presents a novel approach for stock trading; we describe a framework which provides an effective basis upon which expert preferences, together with trader intuition, can be expressed about market dynamics under uncertainty. The proposed framework aims to aggregate collective expert preferences, including group sensibility and intuition, in order to provide a basis upon which stochastic investment trading systems can maximize investment returns and reduce high risk stocks in stock portfolio investments. Our approach uses Kansei evaluation and fuzzy reasoning with inference; this quantifies trader intuition relating to trading decisions based on dynamic market conditions under uncertainty. The framework is used in the quantification of Kansei, quantitative, and qualitative data sets; these are visualized using a Self-Organizing Map (SOM) to enable the selection of the optimal trading actions (buying, selling, and holding stocks) selected from stock portfolio investments. To confirm the model's performance the proposed approach has been tested in experiments using case studies based on stock trading; the results demonstrate that it performed well in real-world stock trading in various market conditions.
