Worked examples, in Market Microstructure
Statistical Arbitrage Capacity: Examples
Net edge enters the capacity formula squared, which means small changes in costs or the impact coefficient move capacity dramatically. That sensitivity is what these scenarios are designed to show. Inputs are alpha per trade in basis points, slippage, fees, a market-impact coefficient, the daily volatility of the traded name, and daily dollar volume traded against. Max AUM is the level at which net alpha is fully consumed; practical AUM is the level at which half the gross alpha survives. Every example uses 2% daily volatility, so impact per unit of √participation is the coefficient × 0.02. The figures scale linearly with the dollar-volume universe, with net alpha squared, and inversely with volatility squared.
4 examples. Published May 26, 2026. Updated Oct 5, 2026. By AI Fin Hub Research.
Worked examples
See the inputs and outcome together
Each scenario keeps the starting point, the outcome, and the actual lesson in one place so the page reads like a decision notebook, not a data dump.
- 1
Baseline mid-frequency strategy
A 20-basis-point edge per trade against a $50M daily-volume universe, with 5 bps slippage, 2 bps fees, a five-day signal half-life and a moderate impact coefficient.
Max AUM $21.1M, practical AUM $1.13M, trades per year 50.4.
Alpha per trade
20 bps
Slippage
5 bps
Fees
2 bps
Impact coefficient
0.1
Daily volume
$50M
Daily volatility
2%
Signal half-life
5 days
Net edge of 13 bps is 0.0013 as a fraction. Impact per unit of √participation is 0.1 × 2% = 0.002, so 0.0013 ÷ 0.002 = 0.65, and 0.65 squared times $50M is the $21.1M ceiling. The practical $1.13M is far lower because keeping half the alpha leaves only 3 bps for impact, and capacity moves with the square. The safety margin is not a 50 percent cut to capacity; it is a 95 percent cut.
- 2
Same strategy, twice the market impact
Identical edge and costs, but the impact coefficient doubles from 0.1 to 0.2, modeling a less liquid name or a more aggressive execution style.
Max AUM $5.28M, practical AUM $281,250, trades per year 50.4.
Alpha per trade
20 bps
Slippage
5 bps
Fees
2 bps
Impact coefficient
0.2
Daily volume
$50M
Daily volatility
2%
Signal half-life
5 days
Doubling the impact coefficient quarters the capacity, from $21.1M to $5.28M, because impact enters the denominator and is then squared. Execution quality is not a minor tuning knob; halving your market footprint quadruples how much you can run.
- 3
Fast signal, thinner universe
A one-day half-life signal trading a smaller $10M-volume universe with a 15-basis-point edge. High turnover, less liquidity.
Max AUM $1.6M, practical AUM $6,250, trades per year 252.
Alpha per trade
15 bps
Slippage
5 bps
Fees
2 bps
Impact coefficient
0.1
Daily volume
$10M
Daily volatility
2%
Signal half-life
1 day
The thinner universe and lower edge drop max capacity to $1.6M, but the practical figure craters to $6,250 because keeping half the alpha leaves only 0.5 bps of net edge to square. Fast, thin strategies look larger at full alpha and tiny once you apply a realistic safety margin.
- 4
Edge too thin to survive the haircut
A marginal 10-basis-point edge with 5 bps slippage and 3 bps fees, leaving only 2 bps net. The strategy works at full alpha but not with a safety margin.
Max AUM $500,000, practical AUM $0, trades per year 50.4.
Alpha per trade
10 bps
Slippage
5 bps
Fees
3 bps
Impact coefficient
0.1
Daily volume
$50M
Daily volatility
2%
Signal half-life
5 days
At full alpha there is a $500,000 ceiling, but keeping half the alpha needs 5 bps that the 2 bps net edge does not have, so practical capacity is zero. A strategy whose practical capacity is zero is not undersized; it has no margin for the costs you have not measured yet.
Patterns
Run the numbers next
- Playgrounds Execution Simulator
Estimate execution cost in closed form — square-root permanent impact, linear temporary impact, half-spread, and a latency-drift band. See the slippage a naive backtest hides.
- Playgrounds Cointegration Half-Life Solver
Engle-Granger residual ADF + Ornstein-Uhlenbeck half-life from any two price/return series. Hedge ratio, p-value, spread chart. Browser-only.
Sources
- Optimal Execution of Portfolio Transactions , Almgren, R. and Chriss, N., Journal of Risk (2001)
- Direct Estimation of Equity Market Impact , Almgren, Thum, Hauptmann and Li (2005)
Keep the topic connected
- Learn Slippage
Slippage as the gap between expected and executed price: the components (spread, market impact, latency), and how to model each in a backtest.
- Learn Latency Arbitrage
Latency arbitrage: cross-venue price discrepancies exploited by being faster than the slowest replicator. Why the game is mostly won at the cable layer.
- Learn Bid-Ask Spread
Bid-ask spread defined: quoted vs effective vs realized spread, why the touch isn't the cost you actually pay, and how to measure each.