Hybrid Kansei-SOM model using risk management and company assessment for stock trading

  • Authors:

  • Hai V. Pham;Eric W. Cooper;Thang Cao;Katsuari Kamei

  • Affiliations:

  • Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan;College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan;College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan;College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2014

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Abstract

Risk management and stock assessment are key methods for stock trading decisions. In this paper, we present a new stock trading method using Kansei evaluation integrated with a Self-Organizing Map model for improvement of a stock trading system. The proposed approach aims to aggregate multiple expert decisions, achieve the greatest investment returns, and reduce losses by dealing with complex situations in dynamic market environments, such as downward, upward, steady market trends, and other uncertain conditions. Kansei evaluation and fuzzy evaluation models are applied to quantify trader sensibilities about stock trading, market conditions, and stock market factors with uncertain risks. In Kansei evaluation, group psychology and sensibility of traders are quantified that represent in fuzzy weights. Kansei and stock-market data sets are visualized by SOM, together with aggregate expert preferences in order to find potential companies, matching with trading strategies at the right time and eliminating risky stocks. The proposed approach has been tested and performed well in daily stock trading on the HOSE, HNX (Vietnam), NYSE and NASDAQ (US) stock markets. The experiments through case studies show that the new approach, applying Kansei evaluation enhances the capability of investment returns and reduce losses. The experimental results also show that the proposed approach performs better than other current methods to deal with various market conditions.