Market behavior and performance of different strategy evaluation schemes

TL;DR

This paper compares three strategy evaluation schemes in agent-based market models, analyzing their performance under different market conditions and determining their suitability for modeling real trader behavior.

Contribution

It introduces a comparative analysis of history-dependent, minority, and majority evaluation schemes in stock market simulations, highlighting their effectiveness in various market scenarios.

Findings

Wealth game performs well in predictable markets.

Majority game excels in trending markets.

Minority game is effective in zig-zag markets.

Abstract

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.