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Published on: January 23, 2017
Market behavior and performance of different strategy evaluation schemes
Yongjoo Baek1, Sang Hoon Lee, Hawoong Jeong
1Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea. yjbaek@kaist.ac.kr
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 28, 2010
Summary
Agent-based market models rely on strategy evaluation schemes. This study shows the wealth game performs well generally, while majority and minority games suit trendy and zigzag markets, respectively.
Area of Science:
- Computational economics
- Agent-based modeling
- Financial market simulation
Background:
- Strategy evaluation schemes are vital in agent-based market models, influencing agent behavior and market dynamics.
- Understanding how different schemes perform under various market conditions is crucial for accurate modeling.
Purpose of the Study:
- To investigate the impact of different strategy evaluation schemes on agent performance within simulated stock markets.
- To determine the suitability of specific schemes (wealth game, majority game, minority game) under varying market circumstances.
Main Methods:
- Simulated stock market with exogenously determined prices (real indices or Markov chains).
- Evaluation of three schemes: history-dependent wealth game, trend-following majority game, and trend-opposing minority game.
- Quantification of scheme success via average wealth accumulated by agents.
Main Results:
- The wealth game performs well, except in highly unpredictable markets.
- The majority game excels in trendy markets with sustained price movements.
- The minority game is effective in markets with persistent zigzag price patterns.
Conclusions:
- The choice of strategy evaluation scheme significantly impacts agent performance in market simulations.
- Each scheme demonstrates distinct strengths depending on market predictability and price patterns.
- Findings guide the selection of appropriate schemes for modeling real market trader behavior.
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