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When a portfolio manager sees their macro risk measured for the first time, the next question is always the same:
"Fine - now show me what to do about it, and show me it works." This study answers it, narrowly and with evidence.

The claim under test is deliberately specific. Using Quant Insight's Macro Factor Equity Risk Model (MFERM), macro-factor-driven drawdowns can be reduced by shifting weights among the stocks a book already holds:
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> No new names
> No cut in gross
> No change in net
> No derivatives.

What we do not claim matters just as much: this is not a route to lower total drawdown, higher return or a higher Sharpe ratio.

Reducing the macro share of risk mechanically raises the idiosyncratic share, and what a book's stock selection then does is its own alpha.

Everything rests on one measure: weekly Expected Shortfall at 5%, computed empirically from the worst 5% of weeks, with no distributional assumption. Isolating each stock's MFERM factor betas gives the macro component of that shortfall - the control variable for the whole process.

An optional guardrail caps macro Expected Shortfall at a chosen share of the book's total (20% throughout the study, purely as an illustration; the level is yours to set, or to omit). Each month, if macro risk sits under the guardrail, nothing trades; if it breaches, the overlay makes the smallest weight change that brings it back, holding gross, net and the name list fixed and moving no position more than ±35% from its original weight.

Five real portfolio structures were tested side by side over up to 4.5 years:
An original book and the same book with the overlay, spanning Japan long/short, US long/short, US market-neutral, global net-long and APAC.
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The maximum drawdown of the macro component of P&L fell in all five, by roughly a quarter to a half.
On the worst macro-factor days, cumulative losses were smaller in every book, and across three real macro shocks intra-episode drawdowns improved in fourteen of fifteen tests. Because the same model both selects the trades and scores the macro component, the result was checked by two independent referees the model does not touch: total P&L (which uses no betas), and regressions on external market proxies - both agreed the overlay books are genuinely less macro-sensitive.

The study is equally clear about limits. Net-long books carry a directional macro exposure no re-weighting can remove; breadth makes the guardrail easier to reach; monthly rebalancing compresses shocks rather than dodging them; and the protection has a price — modest turnover, and on a profitable book some macro-carry return given up in exchange for shallower drawdowns.

The full paper includes the complete methodology, every assumption, and a step-by-step guide to running the same process on your own book.

(Historical simulations only; simulated and past performance are not reliable indicators of future results.)
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