Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model

ArXiv ID: 2309.10220 “View on arXiv”

Authors: Unknown

Abstract

The prevention of rapidly and steeply falling market prices is vital to avoid financial crisis. To this end, some stock exchanges implement a price limit or a circuit breaker, and there has been intensive investigation into which regulation best prevents rapid and large variations in price. In this study, we examine this question using an artificial market model that is an agent-based model for a financial market. Our findings show that the price limit and the circuit breaker basically have the same effect when the parameters, limit price range and limit time range, are the same. However, the price limit is less effective when limit the time range is smaller than the cancel time range. With the price limit, many sell orders are accumulated around the lower limit price, and when the lower limit price is changed before the accumulated sell orders are cancelled, it leads to the accumulation of sell orders of various prices. These accumulated sell orders essentially act as a wall against buy orders, thereby preventing price from rising. Caution should be taken in the sense that these results pertain to a limited situation. Specifically, our finding that the circuit breaker is better than the price limit should be adapted only in cases where the reason for falling prices is erroneous orders and when individual stocks are regulated.

Keywords: price limit, circuit breaker, market regulation, artificial market model, agent-based model, Equities

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs an agent-based model with defined stochastic equations (e.g., expected return calculation) but remains relatively accessible compared to high-frequency trading models; it scores high on empirical rigor due to the heavy use of simulation parameters and model calibration, though it lacks backtesting on real market data.

  flowchart TD
    A["Research Goal"] --> B["Methodology"]
    B --> C["Key Parameters"]
    B --> D["Agent-Based Model"]
    C --> D
    D --> E["Simulation Run"]
    E --> F["Outcome Analysis"]
    F --> G["Key Findings"]

    %% Node content
    A["Research Goal<br/>Compare PL vs CB in<br/>preventing rapid price falls"]

    B["Methodology<br/>Artificial Market Model<br/>(Agent-Based Simulation)"]

    C["Key Parameters<br/>Price Limit Range<br/>Circuit Breaker Time<br/>Cancel Time Range"]

    D["Agent-Based Model<br/>Agents placing buy/sell orders<br/>Market matching mechanism"]

    E["Simulation Run<br/>Simulate market conditions<br/>(e.g., erroneous orders)"]

    F["Outcome Analysis<br/>Evaluate price volatility<br/>and order accumulation"]

    G["Key Findings<br/>1. PL & CB have same effect when params equal<br/>2. PL less effective if Cancel Time < Limit Time<br/>3. Accumulated sell orders act as 'wall'<br/>4. CB better for erroneous orders in individual stocks"]