Bacterial Foraging Optimization with Neighborhood Learning for Dynamic Portfolio Selection

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• 作者：Lijing Tan (21)
  Ben Niu (21)
  Hong Wang (23)
  Huali Huang (22)
  Qiqi Duan (22)
  
• 关键词：Neighborhood learning ; bacterial foraging optimization (BFO) ; von Neumann ; style ; portfolio optimization
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8590
• 期：1
• 页码：413-423
• 全文大小：251 KB
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• 作者单位：Lijing Tan (21)
  Ben Niu (21)
  Hong Wang (23)
  Huali Huang (22)
  Qiqi Duan (22)
  
  21. Management School, Jinan University, Guangzhou, 510632, China
  23. Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong
  22. College of Management, Shenzhen University, Shenzhen, 518060, China
  
• ISSN：1611-3349

文摘

This paper proposes a new variant of bacterial foraging optimization, called Bacterial Foraging Optimization with Neighborhood Learning (BFONL). In the proposed BFO-NL, information sharing among each individual can be realized by using a von Neumann-style neighborhood topology. To demonstrate the efficiency of BFO-NL in dealing with real world problem, this paper improves the original mean-variance portfolio model into Two-Period dynamic PO model considering risky assets for trading, then uses BFO-NL to automatically find the optimal portfolios in the advanced model. With a five stock portfolio example, BFO-NL is proved to outperform original BFO in selecting optimal portfolios.