An Agent Based Trading Game for Risk Adversity Level Estimation

Author

Pandey, Pawan ; Hemant, Sambatur ; Van Khanh, D.

Author_Institution

Bus. Sch., Nanyang Technol. Univ., Singapore, Singapore

fYear

2009

fDate

4-7 Dec. 2009

Firstpage

117

Lastpage

122

Abstract

Portfolio optimization based on the behavior and risk appetite of the heterogeneous investor community in financial markets has been very difficult to model and predict accurately. In this paper, firstly we attempt to simulate a multi-agent based stock market; where different types of agents are modeled to trade stocks using various strategies. The observations from trading activity of the user are in turn used to assess the risk adversity level (RAL) by using a suitable fuzzy logic model. RAL score from the fuzzy model serves as input to perform portfolio optimization using genetic algorithm. We further analyze and evaluate the optimum portfolio performance for different risk adversity level.

Keywords

fuzzy set theory; game theory; genetic algorithms; investment; multi-agent systems; stock markets; agent based trading game; financial markets; fuzzy logic model; genetic algorithm; multi-agent based stock market; portfolio optimization; risk adversity level; risk adversity level estimation; Computational modeling; Electronic mail; Fuzzy logic; Genetic algorithms; Pattern recognition; Performance analysis; Portfolios; Predictive models; Risk analysis; Stock markets; Genetic algorithm; agent based modelling; fuzzy logic;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of

Conference_Location

Malacca

Print_ISBN

978-1-4244-5330-6

Electronic_ISBN

978-0-7695-3879-2

Type

conf

DOI

10.1109/SoCPaR.2009.34

Filename

5368618