Trading Platforms AI Visibility Index 2026

Fidelity led measured AI visibility for Trading & Investing Platforms, appearing in 79.9% of 288 returned answers. Charles Schwab followed at 77.8%, while Robinhood ranked third at 71.2%. The leading intervals overlap, so this sample does not establish a visibility difference among those brands. A brand that is absent from an engine's answers loses a chance to enter consumer selection lists.

This Trading & Investing Platforms report is one segment of the Fintech AI Visibility Index 2026. It covers only the measured trading and investing brands. Payments & Processors, BNPL & Consumer Credit, and B2B Fintech Infrastructure are covered in separate reports.

The study tested 24 stratified questions three times in each of four engines. All 288 returned answers included retrieval evidence, so appearance and citation rates use their respective full denominators in this measurement.

Which brands lead the trading platform leaderboard?

Fidelity ranked first with a 79.9% appearance rate and a 74.9% to 84.1% interval. Charles Schwab ranked second at 77.8%, followed by Robinhood at 71.2%, Interactive Brokers at 65.6%, and Webull at 53.8%.

The first five brands were named across all four engines. The middle and lower ranks show more engine dependence, including brands that appeared in only one engine's 72 answers.

Appearance leaderboard

Appearance rate is the share of the 288 returned AI answers in which each brand was named.

Fidelity's interval overlaps Charles Schwab, Robinhood, Interactive Brokers, and Webull. The difference among those five brands is within the margin of this sample. Charles Schwab's interval also overlaps Robinhood and Interactive Brokers. Rankings organize the measured tally, but overlapping intervals limit comparative claims at the top.

The profiles below start with each brand's weakest measured engine. Appearance rates use 288 returned answers. Owned-domain citation rates use 288 answers with retrieval evidence.

Where do the engines disagree?

The four engines disagree most on brands with uneven engine-level appearance. Each engine returned 72 answers, which makes engine percentages directly comparable within this study. Differences can reflect the engine's model and retrieval tool, not only a brand's visibility.

Engine-by-brand appearance

Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.

Perplexity and ChatGPT named Fidelity and Charles Schwab in more than 90% of their 72 answers. Gemini and Claude named both less often. Robinhood appeared in 94.4% of ChatGPT answers but 33.3% of Claude answers. Webull appeared in 77.8% of ChatGPT answers and 5.6% of Claude answers. Vanguard appeared in 77.8% of ChatGPT answers and 15.3% of Claude answers.

Public was strongest in Perplexity, at 63.9% of its 72 answers, versus 23.6% in ChatGPT, 20.8% in Gemini, and 11.1% in Claude. ETRADE appeared in 58.3% of ChatGPT answers but did not appear in Gemini's 72 answers. SoFi Technologies, Inc. appeared in 50% of ChatGPT answers but did not appear in Claude's 72 answers.

Several lower-ranked brands had one-engine exposure. Firstrade appeared in 16.7% of Perplexity answers and did not appear in the other engines' 72 answers. TradeStation Group, Inc. appeared in Perplexity, ChatGPT, and Gemini, but not Claude. Moomoo appeared in 29.2% of Perplexity answers, 1.4% of ChatGPT answers, no Gemini answers, and 2.8% of Claude answers. These patterns make a single-engine audit inadequate for this category.

Which demand clusters favor which brands?

Demand clusters show that the brands engines name depend on the type of trading and investing question. The cluster figure separates broad buyer-intent questions from sub-category and specific-need questions, so the two groups should not be read as one measure of consumer demand.

Brand leaders by demand cluster

The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.

For general buyer-intent questions, the figure identifies the brands that engines most often used in broad selection answers. For specific needs, the figure identifies where named brands surfaced under narrower question types. A brand can be strong in a specific cluster without leading the overall appearance leaderboard. The cluster results apply only to the measured questions.

Which sites receive citations in AI answers?

Third-party publishers received many of the citations in the measured answers. Citation rates use 288 answers with retrieval evidence, not the 288 returned answers used for appearance rates.

Owned-domain citations

Share of the 288 answers with retrieval evidence that cited a URL on the brand's own website.

Among citation-eligible brands, Fidelity had the highest owned-domain citation rate at 30.9%. Charles Schwab followed at 10.1%. Robinhood and Interactive Brokers each had 5.9%. Webull had 2.1%, TradeStation Group, Inc. had 1.7%, and Firstrade had 0%.

Publishers cited by the engines

Publishers cited as evidence, counted once per answer rather than once per link.

StockBrokers.com appeared in 174 answers with retrieval evidence, or 60.4%. NerdWallet appeared in 170, or 59%, and Forbes.com appeared in 147, or 51%. Kiplinger appeared in 115 answers, or 39.9%, while Fool.com appeared in 110, or 38.2%. The tally also recorded 4 unresolved publisher receipts.

Who is winning and who is at risk?

Brands with the strongest measured positions

Fidelity, Charles Schwab, Robinhood, and Interactive Brokers have the strongest measured appearance positions. Fidelity ranked first at 79.9% and had an average first mention position of 2.3. Charles Schwab ranked second at 77.8% and appeared in all four engines. Robinhood ranked third at 71.2% and reached 94.4% in ChatGPT. Interactive Brokers ranked fourth at 65.6% and reached 84.7% in ChatGPT.

Brands with measured exposure risks

Firstrade has the clearest engine concentration risk. It ranked 26th at 4.2%, appeared only in Perplexity's 72 answers, and had a 0% owned-domain citation rate. TradeStation Group, Inc. ranked 14th at 13.5% and did not appear in Claude's 72 answers. Webull ranked fifth overall but appeared in only 5.6% of Claude answers, which creates a major engine-specific gap. ETRADE ranked eighth at 28.8% but did not appear in Gemini's 72 answers.

What does the category pattern show?

The top of the leaderboard is concentrated in five brands. Fidelity ranked first at 79.9%, Charles Schwab ranked second at 77.8%, Robinhood ranked third at 71.2%, Interactive Brokers ranked fourth at 65.6%, and Webull ranked fifth at 53.8%. Fidelity's interval overlaps the intervals of ranks two through five, so this sample does not support a claim that Fidelity is more visible than those brands.

Engine convergence is high for some brands but not for all. Fidelity, Charles Schwab, Robinhood, Interactive Brokers, Webull, Vanguard, Public, Merrill, M1 Finance, Acorns, SoFi Invest, Betterment LLC, J.P. Morgan Self-Directed Investing, and Firstrade were measured across all four engines or carried cross-engine consistency values supplied in the tally. Yet per-engine rates diverged sharply for Webull, Robinhood, Vanguard, Public, ETRADE, SoFi Technologies, Inc., Moomoo, and Firstrade.

Publisher citations outweigh many owned-brand domains in the source layer. StockBrokers.com had 60.4% answer penetration, NerdWallet had 59%, and Forbes.com had 51%. Fidelity's owned domain had 29.5% answer penetration in the source tally. Among brief-named brands, Firstrade had 0% owned-domain citations and TradeStation Group, Inc. had 1.7%.

What changed during this measurement window?

The data contains one measurement window, so it does not show changes over time. It does show cross-engine divergence, including brands concentrated in Perplexity or ChatGPT and brands absent from one or more engines.

What should trading platforms change and re-measure?

• Webull should investigate Claude-specific absence. Claude named Webull in 4 of 72 answers. Re-measure Webull's appearance across the same four engines after publishing clear, retrievable product and account information.
• Firstrade should address single-engine exposure. Perplexity named Firstrade in 12 of 72 answers, while ChatGPT, Gemini, and Claude did not name it. Re-measure engine-by-engine appearance after expanding plain-language pages for the measured selection questions.
• TradeStation Group, Inc. should close its Claude gap. Claude did not name TradeStation Group, Inc. in 72 answers. Re-measure whether that engine begins naming the brand across the same question set.
• ETRADE should inspect its Gemini absence. Gemini did not name ETRADE in 72 answers, despite a 58.3% ChatGPT rate. Re-measure ETRADE's engine-level appearance after reviewing pages that define its platform, account options, and use cases.
• SoFi Technologies, Inc. should test its Claude absence. Claude did not name the brand in 72 answers, while ChatGPT named it in 50%. Re-measure whether the engine gap narrows.
• Webull, TradeStation Group, Inc., and Firstrade should improve their owned-site citation test. Their owned-domain citation rates were 2.1%, 1.7%, and 0%. Re-measure citations using the 288 answers with retrieval evidence after publishing source pages that answer measured comparison needs directly.
• Brands with later average first mention positions should test answer ordering. Webull's average position was 7.5, SoFi Technologies, Inc.'s was 7.9, Merrill's was 9, and Firstrade's was 8.9. Re-measure first-mention position alongside appearance, because being named late is different from leading a recommendation list.

What does this mean for brands in the category?

Interpretation: Trading-platform visibility is not a single score. A brand can rank well overall while missing one engine's answers, as Webull did in Claude. A brand can also appear often while relying on third-party publisher citations rather than its own domain.

Interpretation: Teams should separate three checks in their audits. Measure whether the brand appears, where the brand appears in the answer, and whether the answer cites the brand's own site. The tally shows different patterns across these measures.

How was this trading platform study measured?

This flagship study measured 24 stratified prompts three times in Perplexity, ChatGPT, Gemini, and Claude. The study returned 288 answers, and all 288 had retrieval evidence. Appearance rates use returned answers. Citation and source rates use answers with retrieval evidence. The results describe these prompts on September 14, 2026, not all trading-platform searches or consumer demand.

Measurement summary

How this study was measured

• Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
• Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
• 288 answers returned of 288 attempted, 288 with retrieval evidence.
• Product lines rolled up to parent brands using a registry fixed before scoring.
• Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.

The study measures whether engines named brands and cited domains. It does not measure product quality, fees, investor outcomes, consumer preferences, or market share.

Charts and data tables

These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.

Methodology appendix

This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.

What was measured

• Category: Trading & Investing Platforms
• Measurement date: 2026-09-14
• Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
• Answers attempted: 288
• Answers returned (appearance denominator): 288
• Answers with retrieval evidence (citation denominator): 288 (100% of attempted)
• Answers that failed outright: 0
• Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27

Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.

How the prompts were sampled

Fixed sampling frame: 3 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Trading & Investing Platforms; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.

Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.

How each metric is defined

• Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
• Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
• 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
• Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
• Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
• Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
• 0% — the metric could be evaluated and the event did not occur.
• Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
• Cross-engine consistency — the share of engines that named the brand at least once.
• Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.

Evidence rules

• An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
• Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
• Publisher identity could not be established for 4 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
• Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.

Entity handling

Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.

No roll-ups were applied in this study.

40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.

Limits of these findings

• This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-14. Engine behaviour changes without notice.
• The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
• Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
• A brand not listed here was not measured as absent — it simply was not named in these answers.
• Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
• 2 of 26 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.