Short Selling: Weaponized against some companies but not others
Learn how to identify companies that are more likely/less likely to get suppressed by relentless short selling.
Short Selling: Weaponized against some companies but not others
When you look into how Shorting actually works, and the laws and selective enforcement around them, you understand that the entire market is completely rigged by design.
However, the most important point is that the “rigging” isn’t random.
If you understand how the rigging works, you exploit it, and if you don’t, you get wrecked.
TL;DR (what actually matters)
Shorting is “elastic by design.” The rule-set (locates, Market Maker exemptions, synthetics, rehypothecation, dark plumbing) makes downside liquidity abundant on demand. That elasticity is switched on selectively to keep certain equities “orderly”.
Containment is the default for rails-threats; tailwind for state-embedded companies. If a name routes around policy levers (money, ID, sanctions), expect suppression capacity. If it is a lever (defense/ID/compliance clouds), persistent suppression is counterproductive to the system and rare.
Your edge: read incentives, not ideals. Score Suppression Incentive up front; choose instruments (spot vs options vs pairs) accordingly; buy blood in state-embedded names, don’t fight the tape in rails-threats just because they’re “right”.
I. Mechanics that make downside “elastic”
1) Locate (not pre-borrow)
Brokers green-light shorts with a “locate” attestation, not a locked borrow. Lets size now, solve borrow later (buy-ins are a selective risk).
2) Market-maker carve-outs
“Bona fide” MM/hedging exemptions tolerate temporary Failures-To-Deliver (FTD); fines < profits; resets via close-outs and rolling inventory.
3) Rehypothecation & reuse
One share can sit under multiple economic shorts along a lend chain; owners get paid, float is effectively multiplied.
4) Synthetic shorting
Reverse conversions (long put + short call), TRS/forwards deliver short exposure without printed short interest; optics of FTD shrink.
5) Internalization/dark pools
Retail buys are absorbed off-exchange; lit price impact blunted; momentum capped while shorts roll.
6) Ex-clearing netting
Prime-to-prime/bilateral nets delay visibility of stress; real fails show late (if at all).
None of this is “broken”. It’s tuned. Elastic short supply + selective enforcement = quiet control surface.
II. “If they wanted to ‘fix’ it” (they don’t)
True pre-borrow + public borrow tape (borrow = SI one-for-one; FTDs collapse).
Kill MM FTD/stock-substitute carve-outs (reverse-conv optionality shrinks).
Central Counterparty for lending, with public lendable/on-loan/reuse + teeth.
Shift flow to lit books without widening spreads.
They won’t: elasticity is the feature.
III. Suppression Incentive Score (SIS)
Score 0–5 unless noted; 30 max. Higher = more likely to face abundant, cheap, opaque shorting when inconvenient.
Policy Friction (0–5) — Undermines monetary/ID/sanctions optics?
Index Optics Risk (0–5) — Inclusion or run would distort “widows & orphans” benchmarks?
Rail Threat (0–5) — Routes around bank/KYC/app-store/payments choke points?
Retail-Mania Sensitivity (0–5) — Meme-prone squeezes stress plumbing?
Clearing/Systemic Risk (0–5) — Tight float, high utilization, option crowding → Central Counterparty/Prime Broker stress?
Data Opacity & MM Carve-Outs (0–3) — Easy synthetics; locate exemptions shield FTD optics?
Prime-Broker Rent (0–2) — Rich lend churn, borrow fees, reuse chains = PB incentive?
SIS = sum(1..7)
Examples (your cases, cleaned):
MicroStrategy (MSTR): 23/30 (5,5,3,3,3,2,2) → Bitcoin proxy; index optics; easy synthetics.
China ADR sensitive tech (BABA/BIDU/BILI): ~20 → dual policy headwinds; optics.
GME/AMC during mania: ~22 → clearing risk + meme beta.
Pure-play Bitcoin Miners stressing rails (MARA): ~19 → policy narratives + synthetic ease.
Defense primes (LMT/NOC/RTX/GD): ~4 → policy instruments; suppression is self-defeating.
Decision/ID/cloud rails (MSFT/ORCL/AMZN/GOOGL): ~5–7 → embedded rails; some MM opacity/rent, but no reason to pin.
Gov/defense IT & cyber (PLTR/LDOS/ACN/PANW): ~8 → valuation draws shorts, but program outcomes trump persistent suppression.
IV. How to measure suppression
Hard tells
Short interest vs. borrow capacity & fees: persistent high OI puts + low reported SI = synthetic short preference.
Reverse-conversion footprint: elevated put OI at round strikes paired with short calls; box spreads rich to funding.
Utilization + on-loan (if you get access): high + stable while price drops on heavy off-exchange = classic.
FTD cadence: clustered spikes around expiries/rolls (with no news) → stock-sub synthetics rolling.
Dark % of volume: retail buy surges + dark/internalized prints ↑ + muted lit impact.
ETF create/redeem basis kinks: pressure routed via baskets to avoid single-name prints.
Soft tells (structural)
Index gatekeeping chatter; committee “optics” talking points.
Policy heat (hearings, sanctions talk) + broker locate generosity.
Prime Broker borrow “always available” at modest fees in names with clear squeeze math.
V. Trading playbook (longs)
1) State-embedded (low Suppression Incentive Score)
Bias: accumulate weakness; use vol events to add (policy panic, hearings).
Instruments: common + covered calls on clarity ramps.
Stop fighting: don’t over-optimize fills; flows are programmatic.
2) Rails-threat (high Suppression Incentive Score)
Bias: trade, don’t marry. Expect capping, synthetic overhang.
Instruments: calls/call-spreads, not common; reduce gap-down jump risk.
Timing: buy blood, sell euphoria; respect index-optics veto (e.g., S&P committee).
3) Value at Risk shock windows
Buy state-embedded into VIX/MOVE spikes, FTD clusters, margin hikes; sell into regulatory “clarity” PR.
VI. Quick red-flag checklist (pre-trade)
Crypto-adjacent economics?
Index committee risk?
Prior meme/squeeze history + thin float?
Sensitivity to sanctions/ID/KYC routing around choke points?
Options crowding at round strikes + put OI heavy vs SI?
Dark/internalized share of volume abnormally high during buy waves?
If ≥3 flags, assume downside elasticity is available to the other side.
VII. Why MARA underperformed other Bitcoin miners (Example)
Miners leaning AI/HPC signal policy alignment → lower Suppression Incentive Score; allowed to run.
Pure Bitcoin beta miners during policy heat → higher Suppression Incentive Score; abundant synthetic shorts; momentum capped.
VIII. Where the alpha is (and isn’t)
Is: buying policy-aligned rails on vol/clarity shocks; pairing them against labor-heavy SIs with no software annuity; exploiting index-boundary delays.
Isn’t: moralizing the tape; assuming “truth” closes mispricings; fighting a containment regime in common stock.
IX. Bottom line
Incentives > ideals. Control > fairness. Stability > truth.
Ask: Who benefits from the price being “orderly”? If the answer is “policymakers/benchmarks/clearing”, assume elastic shorts exist.
Don’t be the bid in names designed for containment. Be the bid when the system wants higher prints (state-embedded).
None of this should be considered investment advice.