Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

Market Making and Liquidity Provision › The Role of Speed and Technology

Michael BrenndoerferDecember 31, 20255 min read

Quantitative FinanceAll chapters

Part 12 of 15Market Making and High-Frequency Trading

Chapter 65Market Making and Liquidity Provision › The Role of Speed and Technology

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Modern market making is as much about technology as about models. The concepts we've discussed in this chapter assume you can update quotes instantaneously, but in practice there's always latency, the time between deciding to change a quote and that change taking effect in the market. This latency creates vulnerability to adverse selection, as faster participants can trade against stale quotes.

The Speed Arms RaceLink Copied

When prices move, you must cancel or update your stale quotes. If you are slow to cancel a sell order when prices rise, you risk being "picked off," having your order executed at a now-unfavorable price. This creates intense competition on latency, where firms invest enormous resources to gain even microsecond advantages:

Co-location: Placing servers in the same data center as the exchange minimizes the physical distance that signals must travel
FPGA/ASIC: Custom hardware for sub-microsecond processing bypasses the overhead of general-purpose computing
Microwave links: Faster-than-fiber communication between exchanges exploits the physics of electromagnetic wave propagation
Optimized code: Every nanosecond matters, driving investment in highly optimized, often hardware-specific software

The upcoming chapter on High-Frequency Trading and Latency Arbitrage will explore these technological considerations in depth.

Quote Lifetime and Fill RatesLink Copied

Your speed determines how long your quotes are "live" and vulnerable to adverse selection. We can estimate the expected adverse price movement during a latency period $\Delta t$ using the square-root-of-time rule for diffusion processes. This rule follows from the properties of Brownian motion: the standard deviation of price changes grows with the square root of time.

E[\Delta P] \approx \sigma_{\text{ms}} \sqrt{\Delta t}

where:

$E[\Delta P]$ : expected price move magnitude
$\sigma_{\text{ms}}$ : price volatility per millisecond
$\Delta t$ : latency in milliseconds

Assuming a portion of these moves (e.g., 50%) result in you being "picked off" by faster traders, we can quantify the cost of latency.

In[33]:

Code

import numpy as np


def adverse_selection_by_latency(latencies_ms, price_vol_per_ms=0.001):
    """
    Estimate adverse selection cost for different latencies.

    Parameters:
    -----------
    latencies_ms : array - Latencies in milliseconds
    price_vol_per_ms : float - Expected price volatility per millisecond

    Returns:
    --------
    array: Expected adverse selection cost in price units
    """
    # Adverse selection proportional to price move during quote lifetime
    # Simplified model: informed traders can pick off quotes that are
    # stale by more than the current price move

    expected_move = price_vol_per_ms * np.sqrt(latencies_ms)
    adverse_cost = expected_move * 0.5  # Assume 50% of moves are exploitable

    return expected_move, adverse_cost


latencies = np.array([0.001, 0.01, 0.1, 1.0, 10.0, 100.0])  # 1 μs to 100 ms
moves, costs = adverse_selection_by_latency(latencies, price_vol_per_ms=0.0005)

# Format data for display
latency_data = []
for lat, move, cost in zip(latencies, moves, costs):
    latency_data.append(
        {"latency_val": lat, "move_bps": move * 10000, "cost_bps": cost * 10000}
    )

Out[34]:

Console

Adverse Selection Cost by Quote Latency
=======================================================
Latency         Expected Move        Adverse Cost   
-------------------------------------------------------
1 μs            0.16 bps           0.08 bps
10 μs           0.50 bps           0.25 bps
100 μs          1.58 bps           0.79 bps
1 ms            5.00 bps           2.50 bps
10 ms           15.81 bps           7.91 bps
100 ms          50.00 bps           25.00 bps

Out[35]:

Visualization

Log-log chart showing adverse selection cost and expected price move in basis points rising with latency from one microsecond to one hundred milliseconds. — Estimated adverse selection cost as a function of system latency. The cost scales with the square root of time, following the diffusion of asset prices. While the absolute cost reduction diminishes at lower latencies, the relative competitive advantage of microsecond-level speed remains critical in winner-take-all liquidity provision.

This simplified analysis shows why latency matters: if you are slower, you face higher adverse selection costs. A 100-millisecond delay might seem negligible to human perception, but in fast-moving markets, it can mean significant losses to faster traders who exploit stale quotes. The economics of latency explain why market making has become dominated by technology-intensive firms with the resources to compete at the frontier of speed.