EX-12. Beginner Investing Education Apps. AI visibility audit.

Fidelity

Fidelity leads Beginner Investing Education Apps in both category-named and problem-first responses.

Across 217 responses, Fidelity appears in 48.4% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

8/22

Fidelity in problem-first

Fidelity's presence in symptom-shaped queries.

Overall category presence: 105 of 217 (48.4%). In problem-first specifically, 11 of 22 responses in this bucket name no vendor at all (50.0%). That's empty inventory the leader could plausibly claim.

How AI answered a real buyer question in this category.

Query. Bucket 8. Problem-first. Excerpted from published audit.

I keep buying stocks at the top — how do I stop doing that

Ah, the classic "buy high, sell low" trap — you're definitely not alone in this. This is one of the most common and psychologically painful patterns in investing. Let me break down why it happens and how to stop.

## Why you're doing this

**It's not a skill problem, it's a psychology problem.** Buying at tops usually happens because:

1. **FOMO (Fear of Missing Out)** — By the time a stock is all over the news and your group chat, it's already had its big run. You're buying the excitement, not the value.

2. **Recency bias** — Your brain assumes what just happened (going up) will keep happening.

3. **Herd behavior** — When everyone's bullish, it *feels* safe. Ironically, that's often peak risk.

4. **Confirmation bias** — Once you're interested in a stock, you seek out bullish takes and ignore warning signs.

## How to actually stop

### 1. Automate your buying (the single biggest fix)

Set up **dollar-cost averaging (DCA)** — automatic purchases on a fixed schedule (e.g., $500 every 2 weeks into an index fund). This removes emotion entirely. You'll buy some at tops, some at bottoms, and average out. Most people who try to time the market underperform this simple approach.

### 2. Impose a "cooling off" rule

When you feel the urge to buy something because it's ripping higher, force yourself to wait **72 hours** before pulling the trigger. Write down *why* you want to buy it....

Vendors named in this response NONE

Appearance rank and pick-share rank tell different stories.

Every brand ranked two ways. Appearance = how often the brand is named at all. Pick share = how often the brand is the SOLE or PRIMARY pick, as a percentage of all 217 responses. The gap between the two is the recommendation gap.

#	Brand	Appear	App %	Pick %
01	Fidelity	105	48.4%	12.4%
02	Schwab	80	36.9%	0.0%
03	Robinhood	78	35.9%	0.5%
04	Investopedia	75	34.6%	8.8%
05	Public	58	26.7%	1.8%
06	Charles Schwab	51	23.5%	0.0%
07	SoFi	47	21.7%	0.5%
08	Acorns	46	21.2%	1.4%
09	SoFi Invest	41	18.9%	0.5%
10	Webull	40	18.4%	0.0%
11	Vanguard	32	14.7%	0.0%
12	Khan Academy	30	13.8%	1.4%

Fidelity appears in 48.4% of responses and is the sole or primary pick in 12.4%. That is a 36.0-point gap. 40 of 105 appearances land as NEUTRAL (cited but not recommended).

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	Fidelity	29	Schwab 23
Comparison	Schwab	11	Public 10
Segment fit	Fidelity	30	Acorns 23
Evaluation	Investopedia	17	Robinhood 13
Pricing	Investopedia	20	Fidelity 13
Trust	Khan Academy	5	Fidelity 2
Switching	Investopedia	12	Webull 9
Problem-first	Fidelity	8	Schwab 8

Problem-first zoom.

The highest-leverage queries: the buyer describes a symptom rather than naming the category.

#	Brand	Count	%
01	Fidelity	8	36.4%
02	Schwab	8	36.4%
03	Vanguard	7	31.8%
04	Investopedia	4	18.2%
05	Charles Schwab	3	13.6%
n/a	Zero-vendor responses	11	50.0%

The domains the assistants pulled from.

Two lists. Vendor-owned and editorial sources on the left. SEO listing sites on the right. The split reveals which channel is doing the citation work.

Established, own content

wallstreetsurvivor.com Wall Street Survivor14
firstcard.app115
fool.com99
nerdwallet.com74
financer.com65
finder.com59
stockbrokers.com58
apps.apple.com55
play.google.com53
easyfinanceinsights.wordpress.com44

SEO listing sites

none in this class0

Methodology and caveats.

Run date. 2026-07-24 UTC.
Sample. 30 buyer questions across 8 buckets, 3 samples per engine. Target 270 responses.
Engines. anthropic, openai, perplexity.
API is not the consumer app. Directionally representative, not literal.
Answers vary. Every prompt sampled 3 times per engine. Frequencies, not binaries.
Two-stage matcher (v0.4). Brand extraction is case-insensitive, word-boundary-anchored. Vendors whose canonical name overlaps with a common English word are flagged ambiguous in the config, and every candidate hit goes to a second-stage LLM adjudication (Claude Haiku 4.5) that reads the surrounding context and answers whether the token is the company or generic usage.
Recommendation classification (v0.5). Every confirmed brand mention runs through a second LLM pass (Claude Haiku 4.5) that classifies the treatment of that brand in that response as one of six codes: SOLE (only vendor named as the answer), PRIMARY (named as the top pick), ALTERNATIVE (named in a short recommended set), CONDITIONAL (recommended only for a stated narrow case), NEUTRAL (mentioned without endorsement, or cited as a source only), or UNFAVORABLE (named with a negative or dismissive frame). Pick share of total = (SOLE + PRIMARY) ÷ total responses.
No editorializing. This exhibit reads the data. It does not claim causation.
Right of reply. Any named brand can request their own free audit. Findings publish alongside.

Want an audit like this for your brand?

Every 30-day pilot starts with one. Fixed fee. Ad spend billed to your account. Written verdict on day 30.

Apply for a pilot

Or read the rest of the research library.