Avramov et al. Find: Highest-Alpha Factor Strategies Have Capacity Under $2 Billion, Profitability Factors Support $33 Billion
Avramov, Bongaerts, Crego, and Kvaerner used NYSE TAQ data from 2010 to 2023 to calculate capacity ceilings for 35 stock anomalies, finding a severe disconnect between alpha rankings and capacity rankings.
Quantitative investment literature has long evaluated whether a signal is worth pursuing using metrics such as alpha, the Sharpe ratio, and statistical significance. A new paper by Avramov, Bongaerts, Crego, and Kvaerner introduces an often-overlooked dimension: capacity—the maximum amount of capital a strategy can deploy before transaction costs erode its market-hedged returns. Using NYSE TAQ data from 2010 to 2023, they estimated spreads, price impact, and liquidity replenishment to calculate deployable capital for 35 characteristic-based anomalies and factors one by one.
The core finding is a severe disconnect between alpha rankings and capacity rankings. The two long legs with the highest gross alphas each support less than $2 billion in market-hedged capital, while strategies with significantly lower alpha in profitability and cash flow can support $20 billion to $33 billion. The profit-margin anomaly has roughly two-fifths the alpha of the highest five-day return strategy, yet its capacity is 97 times greater. For anyone actually allocating capital, this means that the most eye-catching signals in backtests are not necessarily the largest economic opportunities.
The differences in capacity stem primarily from price impact, which is driven by two portfolio characteristics: turnover and the liquidity of the underlying stocks. Short-term reversal strategies turn over 1.8 times per month, incurring 1.6% monthly impact costs at a $10 billion scale; the Amihud liquidity strategy hardly turns over (0.12 times per month), but because it holds illiquid stocks, it still incurs 1.2% monthly impact costs at the same scale. The paths differ, but the result is the same: costs eat away at alpha.
The paper also provides a lever with significant practical implications: rebalancing speed. When the adjustment coefficient τ is less than 1, exiting stocks that leave the target quintile does not involve immediate liquidation but rather a gradual reduction over subsequent months. This single change raises the median capacity of positive-capacity factors from $1.5 billion to $21 billion, a thirteenfold increase at the strategy level. The value-weighted machine-learning composite strategy expands its capacity from $177 million to $19 billion without any change to the signal itself. The rank correlation of capacity rankings is 0.93, indicating that slower rebalancing mainly amplifies scale without drastically altering relative ordering.