Short books are notoriously difficult to interpret. They are small in gross exposure, noisy in return contribution, and often implemented through synthetic or options-based instruments. Traditional factor models, designed with long-only equity portfolios in mind, can misrepresent their behavior entirely. The result is that attribution on the short side often looks like statistical fog: exposures flicker in and out, betas appear inverted, and alpha signals get lost in translation.
We recently built a delta-adjusted short book attribution engine to bring more structure into that fog. The goal was to measure the short side the way portfolio managers actually take risk, net of option deltas and directional overlays. But the implementation required rethinking several steps of the attribution process.
For books that include listed options, swaps, or custom synthetic positions, nominal notional exposure tells very little. The first task was to translate every instrument into an effective delta-equivalent position. This means adjusting the exposure by the option’s delta and implied leverage, then aggregating these to reflect the underlying security or index.
That process produces what we can think of as a “reconstructed short”, the actual directional bet being expressed, stripped of derivative noise. Once mapped, these delta-adjusted shorts can be slotted into factor models in a way that makes sense: sector, style, or thematic factors apply to the underlying exposure, not the instrument itself.
With positions reclassified by their effective factor category, we can decompose short-side P&L into systematic and idiosyncratic parts. This is crucial for evaluating whether a manager’s short alpha is genuine insight or just market beta inverted. When the attribution is done on a delta-adjusted basis, factor sensitivities align more cleanly with true exposure, so the remaining residual becomes a more reliable measure of skill.
We’ve found that the difference between raw and delta-adjusted attribution can be large, especially in concentrated multi-manager platforms. Option overlays or synthetic hedges that appear neutral under a naïve model can reveal strong hidden factor tilts once adjusted. Conversely, what seemed like “alpha” often turns out to be a leveraged reflection of a broad index movement.
The utility of this engine extends beyond post-hoc attribution. Because it expresses short exposures in effective delta terms, it feeds directly into sizing diagnostics and portfolio construction logic. Position limits, netting rules, and hedging ratios can be set on a consistent scale across instruments, essentially letting PMs compare a synthetic short on the NASDAQ with an ETF or single-name short on the same footing.
This work sits at the intersection of factor analytics and portfolio engineering. It’s less about creating new factors and more about measuring existing ones correctly when derivatives blur the picture. The short book, once the least understood component of multi-manager portfolios, starts to look coherent when mapped through delta-adjusted space.
At the platform level, this approach also improves how short-side risk aggregates across managers. When every exposure is expressed in delta-adjusted terms, overlaps become visible, and net factor tilts can be measured with real precision. That matters for allocators: a dozen seemingly independent short books may, in fact, be the same macro bet in disguise. By normalizing how short exposures are represented, we can see not just what each manager is doing, but how their risks combine into a collective posture.
