Impact of meta-order in the Minority Game
arXiv:1112.3908 · doi:10.1080/14697688.2012.756146
Abstract
We study the market impact of a meta-order in the framework of the Minority Game. This amounts to studying the response of the market when introducing a trader who buys or sells a fixed amount h for a finite time T. This perturbation introduces statistical arbitrages that traders exploit by adapting their trading strategies. The market impact depends on the nature of the stationary state: We find that the permanent impact is zero in the unpredictable (information efficient) phase, while in the predictable phase it is non-zero and grows linearly with the size of the meta-order. This establishes a quantitative link between information efficiency and trading efficiency (i.e. market impact). By using statistical mechanics methods for disordered systems, we are able to fully characterize the response in the predictable phase, to relate execution cost to response functions and obtain exact results for the permanent impact.
18 pages, 4 figures
References in corpus (7)
- Market impact and trading profile of large trading orders in stock markets
- Anomalous price impact and the critical nature of liquidity in financial markets
- Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode
- Increasing market efficiency: Evolution of cross-correlations of stock returns
- Critical comparison of several order-book models for stock-market fluctuations
- How markets slowly digest changes in supply and demand
- Financial correlations at ultra-high frequency: theoretical models and empirical estimation
Cited by in corpus (5)
- Agent-based models for latent liquidity and concave price impact
- Self-organization and phase transition in financial markets with multiple choices
- Self-reinforcing feedback loop in financial markets with coupling of market impact and momentum traders
- Evolutionary dynamics in financial markets with heterogeneities in strategies and risk tolerance
- Modelling stock correlations with expected returns from investors