How Heterogeneous Trading Rules Shape Order Books and Price Changes

Summary

This paper models an order-driven market in which agents submit market and limit orders under fixed trading rules. Each agent forms return expectations from fundamental information, chart-based signals, and noise, with agents differing in their time horizons, risk aversion, and weighting of these inputs. Order sizes arise from utility maximization, linking submitted volume to the agents' evolving market decisions instead of assuming identical mechanical orders.

The authors examine how these strategies affect simulated prices and order flows. They report that chartist behavior is the main source of fat-tailed returns and volatility clustering in the model, and that large price changes are associated with substantial gaps in the order book. These are results from an artificial market, not direct proof about every live venue. The abstract provides no calibration details or empirical validation, so the findings should be treated as model-based evidence about how heterogeneous rules and book structure can interact.

Key ideas

• The model combines fundamental, chartist, and noise-based return expectations across heterogeneous agents.
• Utility maximization determines order submissions and sizes.
• In the simulated market, chartist strategies drive much of the fat-tailed behavior and clustering in prices.
• Large simulated price moves are linked to gaps in the limit order book.
• The findings are model-based and the abstract does not report live-market validation.

Tags

# The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows


# The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows









In this paper we develop a model of an order-driven market where traders set bids and asks and post market or limit orders according to exogenously fixed rules. Agents are assumed to have three components to the expectation of future asset returns, namely-fundamentalist, chartist and noise trader. Furthermore agents differ in the characteristics describing these components, such as time horizon, risk aversion and the weights given to the various components. The model developed here extends a great deal of earlier literature in that the order submissions of agents are determined by utility maximisation, rather than the mechanical unit order size that is commonly assumed. In this way the order flow is better related to the ongoing evolution of the market. For the given market structure we analyze the impact of the three components of the trading strategies on the statistical properties of prices and order flows and observe that it is the chartist strategy that is mainly responsible of the fat tails and clustering in the artificial price data generated by the model. The paper provides further evidence that large price changes are likely to be generated by the presence of large gaps in the book.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.