Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
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DOI：
10.1016/j.ins.2011.11.036
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
H. Pham;E. Cooper;C. Thang;K. Kamei
中科院分区：
文献类型：
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作者：
H. Pham;E. Cooper;C. Thang;K. Kamei
风险管理和股票评估是股票交易决策的关键方法。在本文中，我们提出了一种使用感性评估与自组织映射模型相结合的新股票交易方法，以改进股票交易系统。该方法旨在通过处理动态市场环境中的复杂情况，如下跌、上涨、稳定的市场趋势和其他不确定条件，聚合多个专家决策，实现最大的投资回报，减少损失。应用感观评估和模糊评估模型来量化交易者对股票交易、市场状况和不确定风险的股票市场因素的敏感性。在感性评估中，交易者的群体心理和敏感性被量化，并以模糊权重表示。感性和股市数据集通过SOM可视化，结合专家偏好的汇总，以发现潜在公司，在正确的时间匹配交易策略并消除风险股票。所提出的方法已经在 HOSE、HNX（越南）、纽约证券交易所和纳斯达克（美国）股票市场的日常股票交易中进行了测试并表现良好。通过案例研究的实验表明，应用感性评估的新方法增强了投资回报和减少损失的能力。实验结果还表明，所提出的方法在处理各种市场条件方面比现有的其他方法表现更好。
Risk management and stock assessment are key methods for stock trading decisions. In this paper, we present a new stock trading method usingKanseievaluation 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.Kanseievaluation and fuzzy evaluation models are applied to quantify trader sensibilities about stock trading, market conditions, and stock market factors with uncertain risks. InKanseievaluation, group psychology and sensibility of traders are quantified that represent in fuzzy weights.Kanseiand 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, applyingKanseievaluation 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.