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research2026-04-209 min read

When does CEO media exposure actually move the stock? A selection bias problem.

factor-investingevent-studyselection-biasrklbquantitative-research

Brendan Chen | April 2026

The narrative

"Peter Beck went on CNBC yesterday and Rocket Lab was up 3%."

It's the kind of sentence you see all over fintwit and YouTube after any high-profile CEO appearance. The implied causal story is clean and satisfying: the CEO got TV airtime, retail piled in, price went up. A measurable exposure-to-price effect.

The problem is that the implied causality is almost certainly backwards — or at least badly confounded. CEOs, especially founder-CEOs of small-cap growth names, do not appear on CNBC at random times. They appear when there is something good to talk about: a launch milestone, a customer contract, a strong earnings beat, a new product announcement. "Beck appeared → stock went up" is very hard to distinguish from "there was good news → Beck appeared to talk about it AND the stock went up."

This is the classic selection bias problem in event studies. I wanted to know whether, after stripping away the catalysts that caused the appearance in the first place, there is any residual causal effect of the CEO being on television at all. So I built the first pass of a research pipeline around it.

This is an early-stage result. The methodology works. The sample is too small to draw conclusions. The pattern it does surface is the one you would expect if the selection bias hypothesis is correct.

The research design

Target: RKLB (Rocket Lab USA), listed 2021-08-25. ~4.6 years of post-IPO daily data.

Tracked individuals:

  • Peter Beck — CEO, founder (P1)
  • Adam Spice — CFO (P1)
  • Frank Klein — COO (P2)
  • Sandy Tirtey — Senior Director (P2)

Richard Hunter has left the company and is excluded.

Data collectors (7 of them):

  1. Prices — daily OHLCV from Yahoo Finance for RKLB, SPY, ARKX (space ETF), and peer names (LMT, LHX, BA, ASTS, ACHR) used for robustness.
  2. Catalysts — SEC 8-K Item 2.02 earnings filings (the independent variable that controls for "good news exists").
  3. Social heat — daily mention volumes from public sources.
  4. Twitter — Beck-specific mentions via Nitter + snscrape (was not run live).
  5. YouTube — CNBC / Bloomberg / major-channel appearances via YouTube Data API v3.
  6. News — GDELT (graceful skip without GCP credentials).
  7. Podcast — Listen Notes (graceful skip without key).

Event-study methodology:

  • CAPM (Method 2) and Two-Factor SPY + ARKX (Method 3) as primary abnormal-return models.
  • Method 1 (raw returns vs market) as sanity check.
  • Event window: CAR[-1, +1] around each appearance.
  • Estimation window: rolling 120-day window before the event.

Selection-bias controls:

  • Independent catalysts.csv with every RKLB Item 2.02 earnings date (19 rows across the full sample).
  • Each event tagged with a context label: proactive (Beck went out of his way to appear), reactive (earnings-related media day), or routine (ongoing PR).
  • "Clean events" filter: any event ≥3 trading days from any catalyst. This is the subset where, if there is a residual CEO-exposure effect, it should show up unconfounded.

Methodology highlight: the two-factor model

One of the genuinely surprising findings fell out of the market-model estimation itself, before we even got to the event study.

Standard CAPM:

α = +0.002,  β_SPY = 2.019

That looks like a normal high-beta growth name. But RKLB is a pure-play space company, and the space sector has its own idiosyncratic dynamics (launch cadence, customer concentration in government contracts, DoD budget risk) that have very little to do with the broader S&P 500. So I added ARKX (ARK Space Exploration & Innovation ETF) as a second factor:

Two-Factor:  α = +0.002,  β_SPY = -0.958,  β_ARKX = +2.367

Once you control for the space sector explicitly, RKLB's β to the broader market is actually negative, and essentially all of its volatility is explained by sector beta at ~2.4x. RKLB is not a market stock. It is a 2.4x-levered bet on the space sector itself.

This has two practical consequences for the event study:

  • CAPM-based abnormal returns will systematically misattribute sector moves to event windows.
  • The two-factor model is the right lens. All subsequent CARs reported below are from Method 3.

Pipeline gotchas

The data pipeline surfaced a few things worth noting, some of them methodology-relevant:

  • SEC CIK typo. The implementation plan had RKLB's CIK written as 0001819810, which is actually Redwire (RDW). The first catalyst scrape pulled Redwire's earnings into my RKLB file. The real CIK is 0001819994. Caught on manual review, fixed, re-ran. A reminder that "authoritative" identifiers in your own plan documents deserve the same skepticism you apply to external data.
  • Reddit PRAW deprecation. Reddit has effectively killed unauthenticated PRAW access — requests now get redirected into the Researcher Program gate. Switched to the public RSS endpoints (/r/{sub}/top.rss?t={filter}&limit=100) and walked through 5 time windows (all/year/month/week/day) with post-ID dedupe. You lose comment counts and scores but daily post volume is intact.
  • YouTube noise. The first pass on YouTube just searched Beck's name with a disambiguation phrase and returned 282 candidates — the overwhelming majority of which were third-party reaction videos, analysis channels, and clickbait re-cuts. After adding (a) a channel whitelist via the existing youtube_channel_filter column in the people registry, (b) a title blacklist for things like 3+ consecutive ALL-CAPS non-acronyms and common reaction/clickbait phrases, and (c) a minimum subscriber threshold of 10k, the set dropped to 11 clean CNBC/Bloomberg appearances. Precision way up, recall much lower — that's the obvious trade-off, and expanding the whitelist is item #1 on the next-phase list.

First-pass findings

Strong caveat: N = 11 clean events across 4.6 years. This is underpowered. The numbers below are directional, not conclusive. The point of this first pass was to check whether the methodology surfaces the patterns you would expect to see if the selection-bias story is real.

CAR[-1, +1] across subsets (Two-Factor model)

| Subset | N | Mean CAR | p-value | |---|---|---|---| | All events | 10 | +4.0% | 0.38 | | Beck only | 7 | +6.1% | 0.36 | | Beck — proactive | 4 | −3.9% | 0.34 | | Beck — tier 1 | 7 | +6.1% | 0.36 | | Beck — clean (≥3d from catalyst) | 4 | −3.9% | 0.34 |

The core finding

Look at the two rows I've bolded against the "Beck only" row right above them:

  • All Beck appearances: +6.1% abnormal return. Looks bullish. "CEO on TV → stock goes up."
  • Clean Beck appearances (away from earnings): −3.9% abnormal return. Goes the other way.

This is exactly what the selection-bias hypothesis predicts. The unconditional "Beck appeared → stock up" signal is almost entirely driven by the subset of appearances that cluster around earnings days. Strip out every appearance that is within three trading days of an earnings catalyst and the residual effect flips sign. When Beck goes on CNBC away from an obvious news catalyst, the average 3-day abnormal return is mildly negative.

If this pattern holds up with a bigger sample, the interpretation is essentially: the headline "CEO on TV moves the stock" isn't a CEO-exposure effect at all — it's an earnings-beat effect showing up in both variables simultaneously. The CEO appearance is a downstream marker of the news, not an independent driver of price.

The critical caveat

N = 4. p = 0.34. The clean-Beck sign flip is directionally interesting and consistent with theory, but I have zero statistical confidence in it yet. With N = 4, you need roughly a 2σ move just to register on the p < 0.05 threshold — and the magnitude here (-3.9% over three days, against RKLB's ~5% daily vol) is nowhere near that. This is a hypothesis the methodology has generated, not a hypothesis it has confirmed.

Heat vs price (Granger)

Separately, I tested whether Reddit discussion volume (reddit_rocketlab_posts) leads or lags RKLB daily returns, at daily lags 1 through 10.

  • reddit_rocketlab_posts → returns: min-p = 0.195
  • returns → reddit_rocketlab_posts: min-p = 0.339

Nothing significant in either direction. Reddit chatter on r/RocketLab has no detectable lead/lag relationship with RKLB daily returns. This is a small-scale replication of a pattern I've seen across multiple retail-sentiment-vs-price studies: aggregate Reddit volume by itself does not contain tradable advance information for single-name mid-caps.

What this means and what comes next

What it means

At first pass, and subject to all the sample-size caveats above, the data is consistent with the hypothesis that the observed "CEO appearance → stock moves" correlation is almost entirely a selection-bias artifact. Once you filter out appearances that cluster around earnings and other catalysts, the average abnormal return around a pure CEO-exposure event is at best zero, and possibly mildly negative.

That is an interesting result in its own right, because it pushes back against a very common implicit model in retail investing — that management visibility is itself a price-moving event. For RKLB, on this sample, it is not. The methodology is working as designed: it is giving us back precisely the pattern we would see if selection bias is doing all the work.

What comes next

The priority queue for the next iteration is all about sample size:

  1. Expand the YouTube channel whitelist — add Yahoo Finance, CNBC Mad Money, CNBC Prime, Fortune, Motley Fool, WIRED, The Verge. Probably takes the event set from 11 → 30-50.
  2. Additional search queries — "Rocket Lab CEO", "Rocket Lab interview", not just "Peter Beck". Should add another tranche of high-quality candidates.
  3. Google Trends — full 4.6-year stitched pytrends run. Would give a second heat series to pair with Reddit.
  4. Manual Bloomberg Terminal additions — 20-30 notable appearances I can remember (conferences, non-Bloomberg/CNBC podcasts, Forbes coverage) added directly to the curated events file.

Takeaway

Separating correlation from causation in event studies requires explicit selection-bias controls, not just good intentions. The "CEO appeared, stock moved" narrative almost certainly contains real selection bias — and for this first pass at RKLB, when you control for it, the residual "just the CEO being on TV" effect appears to be small, possibly negative, and statistically indistinguishable from zero.

Whether that holds up at N = 40 or N = 100 is an empirical question. The scaffolding to answer it is now in place.

This is a work-in-progress research note, not investment advice. All numbers above are from the first-pass run on 11 curated YouTube events plus 19 SEC catalysts; the sample is explicitly underpowered and the conclusions are hypotheses to be tested, not findings to be acted on.