Summary of "Don't Wait For The Drop: Mastering Offensive Sell Rules | Investing with IBD Podcast"

Guests / Context

Episode of Investing with IBD Podcast (dated May 13, 2026).
Presenters:
- Justin Nielsen (host)
- Connor Bates and Ted Zang — Rever Asset Management
Also referenced: Don Vanderborg (working alongside the team).

Core Market / Leadership Monitoring (Trend-Gauge Framework)

They use a “trend gauge” style monitoring approach at the start of videos, organized into four sections, with leaders emphasized (based on price action relative to moving averages).

21 over 21

Built every weekend.
Includes 21 stocks trading above the 21-day moving average.
Broad coverage across many industry groups (not limited to one theme like semiconductors/AI).
Composition includes:
- Stocks they own
- Stocks they are actively looking to buy

Turbo 12

Similar concept to 21/21, but targeted for:
- Lower liquidity
- More high-beta names
Liquidity rule of thumb:
- Turbo 12 includes names trading about ≥ $60M average daily dollar volume
- Versus 21/21 at ≥ ~$100M
Access constraint:
- Mentioned Turboction AUM ~ $40M+ (vs Growction ~ $300M–$400M), which influences what they can access.

Additional “leaders grading” lenses

They also reference RG8 and “Turbo2” style categorization as additional lenses for leader selection and risk grading (without precise numerical definitions in the excerpt).

Handling Correlation / Concentration Risk

Even “different” industry group picks can remain highly correlated to AI.
They avoid forced diversification:
- Not forcing uncorrelated names like BABA
- Not adding random staples “just to diversify”
Key caution:
- When broad market correlation rises (especially during selloffs), diversification often doesn’t help much.
- So they rely more heavily on risk rules.

Risk Management Methodology: “Rebar”

Definition

Rebar = Rever Estimated Balance at Risk

How it’s calculated (explicit process)

Updated every night after the market close.
Uses closing values for each stock.
Adjusts stop losses for every position, including layered stops (e.g., sell half at a first stop, half at a lower stop).
Computes estimated portfolio downside if every stock hits its stop the next day.

Why they use it

Helps avoid intraday panic driven by open P&L swings.
If rebar becomes very high, it’s a warning that positions are too extended.
- In that case, it may be prudent to trim into strength or reduce exposure.
They try to incorporate “overshoot” risk for rare outliers (e.g., DeepSeek), while acknowledging black swan limits.

Offensive Selling (Selling into Strength) Rules & Actions

General stop/exit behavior

New positions:
- If they hit their stop → exit (“out”).
Profitable positions:
- Prefer to wait for market close and avoid intraday panic.
- Stops are typically based on closing basis and trailing moving averages such as:
  - 8 EMA, 21 EMA
  - sometimes references like 50-day / 10-week
Exogenous shocks / extreme breaks:
- If a stock drops dramatically (example given: down ~30% back to break-even), they may cut immediately (“ask questions later”),
- with the possibility of reentering if conditions reset.

ATR-based trimming (key explicit rule)

A primary offensive selling rule:

Trim when price is ≥ 10 ATR above the 50-day moving average

Example: Micron (MU)

Built through a base breakout
- Started April 21 (inside day)
- Added April 22 (base breakout)
Continued trailing higher via EMAs until:
- It hit the 10 ATR level around May 6–May 7
Action:
- Cut ~50% once it became > ~10% of the portfolio
After trimming:
- The remainder trails 8/21 EMA, accepting profit volatility.
If acceleration continues into “climax” behavior:
- They reference William O’Neal IBD climax sell rules, including:
  - Exhaustion often occurs 15–20 ATRs above the 50-day
  - Watch for large daily spreads/gaps
  - Multiple gaps in a row
  - Mentioned extreme moves above the 200-day can reach roughly ~100%–200%+ (as stated)

“Stop-too-far” problem when extended

When the moving-average stop is very far away, behavior depends on:
- how much you’re willing to give back
- your cushion/personality
Mitigation:
- Sell into strength as it rises
- Holding through huge ATR extensions is difficult
Example idea:
- Holding something like AOI to the 50-day could imply around ~40% pullback risk (illustrative statement).

Acceptance: you don’t need the exact top

They emphasize you don’t need perfect timing.
Concept attributed to Livermore: you can’t catch the first or last eighth.
Climax suspicion signals:
- “shooting-star” type candles
- multiple gap-ups in a row followed by closes near lows
- very high volume on exhaustion days
Even then, you can still sell a “good area” without knowing the precise peak.

Buyback Rules (Re-entry After a Trim/Sell)

Re-entry must be based on a valid entry point

Buying back requires an independent valid entry, not just chasing missed upside.
If the next move suggests you were wrong:
- Reenter right away (mentally: the market showed your decision was wrong).
They reject re-buying just because it’s “more extended than when you sold.”

Example of a valid buyback

Cipher (CIFR):
- Setup described around the 8/21 reclaim area
- Includes earnings-day behavior and reclaim of the 8A.

Earnings gappers / retakes

Re-entry for earnings gappers can occur on:
- Retake of the gap day low
- Reclaim of key levels / flags

News + Macro Filter (How They Use It)

They monitor upcoming events such as:
- Fed minutes
- CPI, PPI
- retail and job numbers
Main rule:
- Price reaction matters more than the news itself.

“News failure event” example

In the discussed day:
- Hottest PPI since Dec 2022
- plus earlier bad CPI
That would typically hurt, especially for:
- small caps
- speculative growth
Yet markets did not sell off → interpreted as a bullish “news failure event.”
Concept referenced from Jason Shapiro:
- “news failure event”
Emphasis:
- Markets are forward-looking, so the move may already be priced in.

ETFs in Their Process

They do use ETFs.
Benefits cited:
- diversification
- volatility smoothing

Leveraged S&P exposure

Mentioned leveraged S&P ETFs and 1x leveraged S&P ETFs for trend-following using a moving average “power trend” framework (referenced via 20/21/50/200 style MA language).

Sector ETFs to reduce single-stock risk

Example approach:
- WGMI as a “basket cover” for certain theme names, to avoid having to pick only one “leader” among correlated small caps.

Hedging vs smoothing

ETFs can smooth drawdowns:
- helpful when clients are closer to retirement and need smaller portfolio swings.

Single-Stock Leveraged / Inverse Hedging (MUD and Inverse ETFs)

They generally prefer selling/trimming over hedging with inverse ETFs.
Example discussed:
- MUD (single-stock bear 1x on Micron) as a potential hedge.
Their view:
- hedging too much can be risky because it’s easy to hedge at the wrong spot.
Semiconductors hedge attempt:
- They tried a semiconductors “Sox/s hedge” (subtitle-corrupted wording)
- got blown out when the market stayed strong
- closed it out.

Taxable accounts

For taxable-client portfolios:
- Growction hedges more often.
- Rationale:
  - legacy positions with huge unrealized gains are hedged using inverse ETFs
  - inverse ETFs can help when options aren’t available, avoiding option selection/timing complexities.

Notable Tickers / Instruments Mentioned

Individual stocks / tickers

Micron (MU)
AOI
Aehr (AEHR)
Corning (GLW)
Akamai (AKAM) (referred to as “Akami, aka M”)
Fastly (FSLY) (earnings drop around ~27% mentioned)
Cipher (CIFR)
IRNE, APLD (as referenced in the discussion)
Bloom Energy (ticker not explicitly stated)
DeepSeek (exogenous shock example; not a ticker)
BABA (example they do not include just for diversification)
QUALCOMM (mentioned conceptually)
Uranium-related references:
- URA, UR
- CCJ, UU
Uranium basket and ETF mention:
- WGMI (also tied to uranium basket discussion)
Other mentions:
- Rocket Lab (ticker not explicitly stated)
- AMPX
- Intel (generic reference)
- AMD/ARM and other AI/semicon leaders (generic references)
- Subtitle-corrupted “Marll/MAR LL” likely intended as Marvell (MRVL)

ETFs / instruments

S&P ETFs (leveraged and 1x leveraged)
WGMI
MUD (single-stock inverse/bear 1x concept discussed)
A subtitle-corrupted “Sox/s socks S hedge” appears, but is not reliably identifiable in the excerpt.

Themes / Sectors Referenced

AI infrastructure
Semiconductors
Memory / DRAM
Cloud / edge computing / data center / hosting agents
Fiber optics (via Corning)
Uranium
Growth & protection / volatility smoothing
Leveraged momentum / high-beta momentum

Key Numbers / Thresholds Mentioned

21/21 and Turbo 12 liquidity thresholds:
- 21/21 around $100M+ average daily dollar volume
- Turbo 12 around $60M+
Rebar:
- no single numeric threshold quoted, but “very very high” implies positions are too extended.
MU offensive selling example:
- Trim trigger: ≥ 10 ATR above the 50-day
- Action timing: around May 6–May 7
- Position reached > 10% of portfolio → trimmed ~50%
Climax / O’Neal-style guidance:
- Exhaustion often ~15–20 ATRs above the 50-day
- Extreme moves above the 200-day discussed as roughly 100%–200%+
AOI risk illustration:
- holding to the 50-day could imply roughly ~40% pullback risk (illustrative)
FSLY:
- earnings drop cited around ~27%
Volatility smoothing target example for certain clients:
- 10–15% drawdown tolerance (vs higher-ATR growth tolerance)

Explicit Cautions / Disclosures (from the excerpt)

No explicit “not financial advice” disclaimer appears in the transcript excerpt.
Repeated risk cautions:
- AI-driven correlation risk
- black swan / overshoot limits
- importance of cutting losses early
- avoid hedging at the wrong time; prefer plan-based trims

Losers / Cutting Losses (Risk Control Examples)

URA example (uranium-related)

Entered May 4 after breakout above an inside day high.
Squatted, then broke out May 6, then became choppy.
Eventually closed out below key moving averages:
- mentions 8/21 levels and a recent day
Stop detail (subtitle-imprecise):
- around ~53.5 / 53.3 and ~3.5 area

AMPX (earnings cushion checklist)

They used implied move to estimate earnings risk:
- “If the stock did 2x the implied move, what would the damage be to the portfolio?”
AMPX had enough “cushion,” so they held into earnings.
It then gapped down ~20% on earnings, wiping profits.
They sold immediately after the adverse reaction—described as a failed “plan,” but consistent with the process.

Rocket Lab

Held into earnings without enough cushion.
Exited after price violated:
- “closed below 8 and 21”
Takeaway:
- Process over outcome
- reduce downside quickly when the plan is invalidated.

Presenter / Source Names Mentioned

Justin Nielsen (host)
Connor Bates — Rever Asset Management
Ted Zang — Rever Asset Management
Don Vanderborg — Rever Asset Management (referenced as co-decision maker / frequent guest)
Jason Shapiro (source for “news failure event” concept)
Jesse Livermore (quoted conceptually)
William O’Neal (referenced for climax sell rules)

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