As portfolio managers increasingly deploy leverage and extension strategies to enhance returns, trading desks are facing a new set of execution challenges. In an interview with Traders Magazine, Joel Feinberg, SVP and Head of Trading at Acadian Asset Management, explains where the hidden costs of leverage emerge, how desks are adapting to dispersed liquidity, and why technology’s biggest edge today lies in real-time adaptability rather than pure speed.
How are leverage and extension strategies reshaping execution on the desk?
They’re fundamentally turning execution into a higher-stakes, more interconnected problem. Leverage increases gross exposure without increasing AUM, so you’re trading more dollars for the same portfolio, which means transaction costs and liquidity access have a much bigger impact on returns.
It also changes the nature of the trades themselves. You’re often executing paired or offsetting exposures, so timing and coordination matter; how you trade one leg directly affects the cost and risk of the other. And because you’re operating with higher exposure, market structure frictions like short sale constraints, crowding, and fragmented liquidity show up more quickly in execution outcomes.
In practice, it pushes desks to be more deliberate about where they consume liquidity, more focused on sourcing natural flow, and more disciplined in managing costs—all things that are already central to how we operate day to day.
Where do the hidden trading costs of leverage show up most in practice?
A lot of the hidden costs show up on the short side, particularly through market structure frictions. Things like short sale restrictions, uptick rules, and limited stock borrow availability all reduce the ability to express short positions precisely under normal market conditions, and especially when markets are under duress.
When a name is under a short sale restriction or trades in a market with an uptick rule, for example, you’re effectively removing a class of natural sellers from the market in certain scenarios. That makes liquidity more one-sided and, as a short seller, forces more passive child order placement, limiting discretion around execution timing, which can increase slippage.
On top of that, constraints like limited locate availability or outright short-selling bans shrink the investable universe, altering the supply-demand balance and influences prices away from where they would otherwise clear in a more open market.
So in practice, the cost of leverage isn’t just financing—it’s the reduced flexibility and higher execution costs that come from operating in a constrained market, especially on the short side.
Is liquidity fragmentation making large-scale trades harder to execute?
It’s making them more complex rather than strictly harder. Liquidity is still there, but it’s dispersed across venues, counterparties, and time. That means you need to work harder to source it by improving volume prediction models, customizing algorithms, and being thoughtful about routing decisions. The challenge is that no single venue gives you a complete picture, so execution becomes more about stitching together smaller pockets of liquidity without signaling your full size.
How do you balance alpha with implementation shortfall in crowded trades?
It comes down to being explicit about the trade-off between timing risk and market impact. In crowded trades, moving too quickly can be costly because you’re competing for the same liquidity, but moving too slowly increases the risk of missing the alpha. The key is to adapt—being more patient when liquidity is thin or signaling risk is high, and more aggressive when there’s natural flow to trade against.
What real execution edge is technology delivering today?
The biggest edge is in adaptability and decision-making, not just speed. Technology is helping desks better estimate real-time liquidity, adjust execution strategies dynamically, and learn from past trades through more granular TCA. The advantage today comes from being able to respond to changing market conditions in real time, rather than relying on static execution approaches.
