# Monthly Stop-Losses: Every Threshold We Tested Lost

- Author: Alex Vidovich (https://alexvidovich.com)
- Published: 2026-08-04
- Canonical: https://alexvidovich.com/writing/monthly-stop-losses-every-threshold-we-tested-lost
- Source: Notes

*Working note No. 10 — on portfolio-level circuit breakers and the clustering of losses with their cures.*

**Abstract.** Having established in [an earlier note](https://alexvidovich.com/writing/every-protective-exit-we-tested-made-the-strategy-worse) that trade-level protection sells the right tail, we examined whether protection fares better one level up: a portfolio-level stop that halts the strategy after a sufficiently bad month. Across every threshold tested, the answer was negative over a decade of evaluation — the stop consistently destroyed value. The mechanism is a clustering fact with some claim to generality: for a strategy with a genuine edge, the periods that produce concentrated losses and the periods that produce concentrated recoveries are not merely adjacent, they are frequently the same period. A monthly circuit breaker is, in every backtest we ran, an instrument for attending the first half and missing the second.

## 1. Introduction

The monthly stop-loss is the most institutional of all protective devices. It is not a trading rule; it is governance. Institutions require it, risk committees codify it, and its logic — *when the machine misbehaves, turn it off and look* — is imported directly from engineering, where it serves well.

The import smuggles in an assumption: that the machine's bad output signals a fault that persists until repaired. For a strategy whose edge is real, the assumption fails in a specific and expensive way, and the failure is the subject of this note.

## 2. Design

The rule family is one line. For threshold *θ* in a pre-registered grid **Θ**, define the indicator

`S_t(θ) = 1{ r_month,t ≤ −θ },`

and let the stopped portfolio stand aside for the period following any month in which *S_t(θ)* fires, re-entering under a fixed re-arm rule. The quantity of interest is *ΔW(θ)*, the difference in terminal backtest wealth between the stopped and unstopped portfolio over the evaluation decade, computed under the standard folds.[^1]

## 3. The result

Every member of the family reduced terminal backtest wealth: *ΔW(θ) < 0* for all *θ* ∈ **Θ** — the tight thresholds severely, the loose ones insultingly, since a stop loose enough to avoid damage is also loose enough to never matter, and pays for its existence in the one month per decade it does.

The post-mortem was uniform across thresholds, and Fig. 1 renders it in stylized form. The months bad enough to trigger the stop were overwhelmingly *high-dispersion* months rather than *dead-edge* months: regimes in which the strategy's underlying phenomenon was operating violently in both directions. Losses clustered there — and so did the largest recoveries, often inside the same month or the one immediately following. The stop, mechanically, harvested the cluster's losses and donated its recoveries.[^2] It performed, with institutional solemnity, the exact trade documented in our note on exits: selling the distribution's best region to buy a feeling.

![The stop triggers at the bottom of the cluster and sits out the recovery](https://alexvidovich.com/notes/fig-10.svg)

***Fig. 1.** This figure shows a stylized monthly return sequence for a strategy with a genuine edge. Ordinary months (gray) are unremarkable in both directions. A high-dispersion episode produces a cluster of large losses (red) that breaches the monthly stop threshold −θ (dashed red line); the stop triggers (vertical dashed line), and the halted portfolio sits out the immediately following months (shaded region) — which contain the episode's concentrated recoveries (black). The sequence is synthetic; levels are intentionally unspecified.*

## 4. Discussion

We are wary of overclaiming. A portfolio-level stop is a rational device wherever the loss process signals *decay* — an edge that has eroded, a market whose structure has moved, a bug. The device fails where the loss process signals *dispersion* — an edge alive and swinging. The practitioner's problem is that a drawdown number cannot distinguish the two; it is one statistic wearing two meanings. Our resolution is procedural rather than clever: decay is monitored by dedicated diagnostics on the signal itself — degradation tests with their own pre-registered thresholds — while the return stream, which conflates decay with dispersion, is denied the authority to halt anything on its own.[^3]

The clustering fact deserves one more sentence, because it echoes the academic record: volatility clusters (a fact old enough to be furniture), and momentum-family strategies earn their worst and best months in tight sequence — the crash-and-rebound anatomy documented by Daniel and Moskowitz (2016). A calendar-month trigger laid over that anatomy is not risk management. It is a standing order to sell the rebound.

## 5. Conclusion

The stop-loss question, properly posed, is not "how much loss will we tolerate?" but "what does a loss of this size *mean*, and what evidence would distinguish its meanings?" Where the honest answer is *dispersion, usually* — as a decade of our own backtest history answers — the circuit breaker belongs to the same family as the trailing stop and the earnings filter: instruments that convert statistical discomfort into certain cost.

The machine analogy fails for a reason worth keeping: an engine that runs rough is telling you something is broken. A strategy that runs rough may be telling you it is working — loudly.

**Notes**

- The grid **Θ** and the re-arm rule were fixed before evaluation; we report no threshold values. We have verified that the sign of *ΔW(θ)* is unchanged under reasonable variations of the re-arm rule, including re-entry rules more forgiving than any institutional convention we know of.

- Formally: conditional on a stop-triggering month, the distribution of the following months' returns first-order stochastically dominated the unconditional distribution in our sample. We resist reporting the magnitude, but the reader may infer its sign from the fact that this note exists.

- The degradation diagnostics operate on signal-level statistics — hit rates, rank stability, decay of the conditioning relationship — none of which require a losing month to move, and all of which can fire in a *winning* month. Divorcing "is the edge alive?" from "did the backtest just print a losing month?" is, in our estimation, the single most valuable governance decision described in this series.

**References**

Daniel, K., Moskowitz, T.J., 2016. Momentum crashes. *Journal of Financial Economics* 122, 221–247.

*Keywords: stop-loss, drawdown governance, volatility clustering, momentum crashes, decay monitoring.*

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*Originally published at https://alexvidovich.com/writing/monthly-stop-losses-every-threshold-we-tested-lost. Research and educational content only; nothing here reports the performance of any live system or makes any profitability claim.*