Human Reasoning Awareness Quantified by Self-Organizing Map Using Collaborative Decision Making for Multiple Investment Models

Abstract

Collaborative Decision Making (CDM) is one of the concepts of human reasoning awareness, which refers to expert knowledge of the group and its preferences in a dynamic market environment. In this paper, we present a new approach, which is a framework for collaborative decision making, together with expert feelings about market dynamics to deal with multiple models of stock investment portfolios. The framework aims to aggregate collective expert preferences, including of group expert psychology and sensibility, assists a dynamic trading support system and achieve the greatest investment returns. Kansei evaluation uses to quantify trader sensibilities about trading decisions, market conditions with uncertain risks. Collective group psychology and preference of traders are quantified that represent in membership weights. The framework is used to quantify Kansei, quantitative and qualitative data sets, which are visualized by Self-Organizing Map (SOM) in order to select the best alternatives with dynamic solutions for investment. To confirm the model's performance, the proposed approach has been tested and performed well in stock trading for stock investment portfolios. The experiments through case studies show that the new approach, applying Kansei evaluation enhances the capability of investment returns and reduce losses to deal with various financial investment models.