How Market Conditions Affect Strategy Evaluation in Agent-Based Models

Summary

This study compares how three strategy-scoring schemes shape agent performance in a stock market with externally determined prices. The schemes are a history-dependent wealth game, a trend-opposing minority game, and a trend-following majority game. Prices come either from observed stock indices or from a Markov chain with limited memory, and performance is measured by the average wealth of agents using each scheme.

The reported patterns depend on market behavior: history-based scoring performs relatively well except when the market is highly unpredictable; trend-following agents fare better in sustained directional markets; and trend-opposing agents suit persistent zig-zag patterns. The study also considers finite memory in strategy scoring. These findings can inform the design of agent-based market models, but the setup fixes prices outside the agents' actions, and the results concern simulated or historical price inputs rather than the effect of the schemes on an endogenous market.

Key ideas

• The study compares wealth-based, trend-following, and trend-opposing strategy evaluation schemes.\nIt evaluates agents by average accumulated wealth under externally specified price paths.\nHistory-dependent scoring performs relatively well unless market behavior is highly unpredictable.\nTrend-following evaluation suits sustained price moves, while trend-opposing evaluation suits persistent zig-zag patterns.\nFinite memory in strategy scoring is also examined.

Tags

# Market behavior and performance of different strategy evaluation schemes


# Market behavior and performance of different strategy evaluation schemes









Strategy evaluation schemes are a crucial factor in any agent-based market model, as they determine the agents' strategy preferences and consequently their behavioral pattern. This study investigates how the strategy evaluation schemes adopted by agents affect their performance in conjunction with the market circumstances. We observe the performance of three strategy evaluation schemes, the history-dependent wealth game, the trend-opposing minority game, and the trend-following majority game, in a stock market where the price is exogenously determined. The price is either directly adopted from the real stock market indices or generated with a Markov chain of order $\le 2$. Each scheme's success is quantified by average wealth accumulated by the traders equipped with the scheme. The wealth game, as it learns from the history, shows relatively good performance unless the market is highly unpredictable. The majority game is successful in a trendy market dominated by long periods of sustained price increase or decrease. On the other hand, the minority game is suitable for a market with persistent zig-zag price patterns. We also discuss the consequence of implementing finite memory in the scoring processes of strategies. Our findings suggest under which market circumstances each evaluation scheme is appropriate for modeling the behavior of real market traders.

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