Seeking Alpha — Coverage, Authors, Psychology

A 20-year archive of 588K articles · 514 authors · 17,966 tickers · cross-author alpha attribution · Gemini-extracted psychological profiles

588K

Articles archived

185K

Earnings transcripts

514

Authors covered

17,966

Unique tickers

20.6 yr

2005–2026

Psych profiles

I. What Seeking Alpha Actually Has

Seeking Alpha (SA) is a crowdsourced equity-research marketplace — ~17K contributing authors, ~7K active tickers, and a paywall-gated API layer that Premium subscribers can drive programmatically. The platform's information architecture has three layers, none of which the marketing pages describe well:

Layer	What's in it	Coverage we observe
Author articles	Long-form bull/bear/neutral theses with sentiment label per article. Each tagged to a primary ticker.	17,966 distinct tickers; ~80K articles/yr post-2022
Editorial feed	SA-written content: `sa-transcripts` (earnings call transcripts, every public-company earnings call), `wall-street-breakfast` (daily market roundup)	185,000 transcripts (2020–2026) + 7,002 WSB issues (2011–2026)
Quant overlay	96 metric fields (PE, EV/EBITDA, growth, momentum, F-score, quant rating, etc.) per ticker, snapshot only	96 valid out of 724 candidate field names probed

SA's defensible asset is not the financials — those are free at EDGAR with 1,000+ XBRL line items. SA's value is opinionated text with sentiment labels + earnings-call transcripts + analyst leaderboards. Everything else is commoditized.

II. How We Access It

The public-facing JSON API is bot-protected by PerimeterX. Standard requests libraries (requests, curl_cffi, even Patchright) are blocked. The bypass:

Launch a dedicated Chrome instance (separate --user-data-dir, satisfies Chrome v136+ "non-default profile" rule for remote debugging)
Sign in once through that Chrome window to seed SA cookies
Attach Patchright via CDP on 127.0.0.1:9222 — all API calls go through the logged-in browser session
Hold a 15-second min-gap throttle between calls + auto-backoff on captcha
For article bodies and comments, render the page in Patchright and read the post-hydration DOM

~6,400 articles/day sustainable rate · PX captcha triggers ~1 per 4 hours, recoverable manually

III. Our Archive

Article corpus

438,480 articles tagged to ticker, sentiment, author, date — 20.6 years of coverage from 2005-10-24 to 2026-06-12.

Sentiment label	Count	Share
bullish	115,520	50.5%
neutral	74,286	32.5%
very-bullish	22,383	9.8%
bearish	19,293	8.4%
very-bearish	3,028	1.3%

4.5× bullish skew. Bullish theses outnumber bearish 138K to 22K. This isn't a bias of the platform's recommendations — it's that authors who write bear cases earn less attention and stop publishing.

Top-covered tickers

AAPL 3,616 · SP500 3,527 · SPY 3,268 · TSLA 2,901 · AMZN 2,369 · META 1,916 · MSFT 1,802 · T 1,754 · NVDA 1,703 · GOOG 1,690

IV. Cross-Author Alpha Attribution

For each of the 304,844 articles where we could fetch forward returns (yfinance prices joined on primary_ticker), we computed: 30/90/180/365-day alpha vs S&P 500 from publish date, then aggregated per author into bull alpha mean/median/hit-rate and Spearman IC of sentiment-label vs forward alpha.

413

Authors with ≥20 articles

+0.031

Mean IC (90d)

62.2%

Authors with IC > 0

60.7%

With |IC| > 0.05

The pool has real, small, positive aggregate skill — mean Spearman IC 0.031, well above the 0 null. But it's noisy: ~38% of authors have negative IC, and only the right tail offers tradeable signal.

Right-tail authors (composite alpha)

Author	Followers	Articles	Bull 90d α	Hit rate	IC 90d
The Investment Doctor	23,807	2,269	+0.2%	44%	+0.076
Elephant Analytics	11,921	2,440	+172.8%*	39%	+0.012
Bill Zettler	14,084	347	+8.2%	65%	+0.120
James Foord	28,438	969	+0.5%	47%	+0.082
Long Term Tips	9,514	179	+10.6%	60%	+0.171
Ahmed Abdelazim	3,264	130	+22.1%	59%	+0.007

* Elephant Analytics — single-name outlier (microcap pick); excluded from production weighting.

Left-tail authors

Author	Followers	Bull 90d α	Hit rate	IC 90d
Main Street Investor	4,508	−5.9%	28%	−0.128
Investor Trip	3,860	−2.0%	30%	+0.117
QuandaryFX	6,001	−8.3%	30%	+0.157
Avisol Capital Partners	18,329	−5.7%	38%	−0.010

Follower count is a weak proxy for alpha. Some 20K-follower authors land in the bottom decile; some 3K-follower authors land in the top. Audience size is signal of writing engagement, not stock-picking skill.

V. Psychological Profiling — Gemini Deep Think

For the 73 authors where we'd collected ≥4 article bodies, we ran a 10-dimension structured extraction with Gemini 3.1 Pro (thinking_budget=−1): cognitive style, decision anchor, time horizon, conviction 1–10, risk posture, top-3 cognitive biases, communication pattern, self-awareness 1–10, regime sensitivity, sophistication tier, plus a Chinese personality archetype.

7.8/10

Avg conviction

5.7/10

Avg self-awareness

10D

Per-author features

Sophistication tier

Tier	N	Notes
Institutional-grade	8	Buyside-style write-ups, multi-quarter modeling
Professional	29	Sellside fluency, valuation rigor
Semi-Professional	15	Strong domain expertise, weaker on portfolio math
Retail Sophisticated	21	Self-taught, narrative-heavy

Decision anchor

Valuation 33 Mechanics 11 Data 9 Narrative 8 Macro 7 Momentum 3 Mean-Reversion 2

SA selects strongly for value/DCF-style writers (45% anchor on valuation). Momentum and mean-reversion writers are rare — they show up on r/wallstreetbets instead.

Most common cognitive biases (from text evidence)

Bias	Authors	Behavioral signature
Anchoring	63 / 73	Sticky entry prices, target prices unchanged after thesis breaks
Confirmation	49 / 73	Selective evidence in updates; ignores disconfirming earnings
Loss-aversion	29 / 73	Averages down, refuses to cut losing names
Narrative-fallacy	19 / 73	Builds elaborate story arcs around random walks
Hindsight	15 / 73	"As I said in March…" follow-ups
Authority	15 / 73	Cites named investors / institutions disproportionately

Chinese personality archetypes (sample)

深度价值坚守者 (×4) · 主题狂热狙击手 (×3) · 深度价值挖掘者 (×3) · 宏观末日预言家 (×2) · 战术波段猎手 · 医药稳健解剖者 · 图表宿命论者 · 基金结构解剖者

VI. Cross-Platform Discourse — SA vs Reddit

To benchmark SA's signal density against the broader retail-investor conversation, we pulled the same ticker's discussion from r/wallstreetbets, r/stocks, and r/investing (via pullpush.io archive). The contrast on a topical name (SpaceX/IPO):

Surface	Vocabulary	Citation density	Signal-to-noise
Seeking Alpha	Thesis numbering, DCF, sector terms	~3–8 named comps per article	High — survives length filter
r/wallstreetbets	Tickers + emoji + politics	~0 explicit citations	Low — but trade flow proxy
r/stocks	News-headline summaries	~1 link per post	Mid — momentum surface
r/investing	Index/ETF questions	Few	Mid — long-horizon allocation

SA and Reddit measure different things. SA captures the marginal opinion of fundamentally-trained writers — slow, valuation-anchored, often early-and-wrong. Reddit captures the trade-flow expectations of leveraged retail — fast, momentum-anchored, often right-then-wrong. Cross-platform divergence is itself a signal.

VII. Methods & Reproducibility

Pipeline: Patchright + CDP-attached Chrome → SA JSON-API → parquet panel · yfinance forward returns · Gemini 3.1 Pro for psychological extraction · Spearman IC per author.
Sentiment ground truth: SA's own author-declared sentiment labels (very-bullish → very-bearish, 5-tier), not inferred by NLP.
Forward-return benchmark: IVV (BlackRock S&P 500) as SPY proxy; multi-window (30/90/180/365d) to avoid horizon-cherry-picking.
Survivorship: Authors are not filtered out for closing their account — discontinued accounts kept in panel.
Throttle & ethics: 15s min-gap between API calls; only paywall content owned by author of Premium subscription (no resale or redistribution).
Reproducibility: Analysis scripts at QihongRuan/seekingalpha-analysis (private).

Caveat. Forward-return data depends on yfinance, which is rate-limited and occasionally returns malformed prices (e.g., a single decimal-shift bug produced a $3×10¹⁷ close that had to be filtered). Hit-rate numbers should be read as ±2pp.