Market Manipulation. Search

Enforcement data

These 8 charts are computed from this site's own library of 2117 market manipulation enforcement actions, rebuilt on every deployment. Each has a stated methodology, an explicit list of what it does not show, and a downloadable CSV that is free to reuse with attribution.

A standing caveat that applies to every chart here. Enforcement data measures enforcement, not conduct. It records manipulation that was detected, charged and announced — which is a different population from manipulation that occurred, in ways that are unknown and unknowable from this data alone. Nothing on these pages should be read as a measure of how much market manipulation there is.

Enforcement actions per year, by agency

This library holds 2117 manipulation enforcement actions across 2 agencies. 2026 carries 85 actions against 103 in 2025, but the most recent year is always incomplete and year-on-year comparisons are dominated by the timing of large multi-defendant sweeps rather than by any underlying trend.

Market manipulation enforcement actions per year, by agencyA stacked column chart of enforcement actions per year from 2013 to 2026, with each column divided by the agency that brought the action. Agencies are distinguished by ink density rather than by colour, in the order SEC, CFTC. 0 62.5 125 187.5 25020132014201520162017201820192020202120222023202420252026Actions filed2013 - CFTC: 282014 - CFTC: 272015 - SEC: 1822015 - CFTC: 232016 - SEC: 1572016 - CFTC: 222017 - SEC: 1892017 - CFTC: 212018 - SEC: 1802018 - CFTC: 412019 - SEC: 1472019 - CFTC: 342020 - SEC: 1462020 - CFTC: 322021 - SEC: 1162021 - CFTC: 262022 - SEC: 1482022 - CFTC: 262023 - SEC: 1712023 - CFTC: 312024 - SEC: 1592024 - CFTC: 232025 - SEC: 982025 - CFTC: 52026 - SEC: 752026 - CFTC: 10SECCFTC

Methodology and caveats · CSV

Monetary relief ordered per year

Aggregate relief is dominated by a small number of very large settlements: in most years, the single largest action accounts for a substantial share of the annual total, which is why the median penalty on each technique page is a better guide to a typical outcome than any yearly total.

Civil penalties and disgorgement ordered per yearA stacked column chart of monetary relief per year, separating civil penalties from disgorgement. Penalties are punitive; disgorgement returns gains. They are shown separately because adding them together and calling the result a fine double-counts the harm. $0 $5bn $10bn $15bn $20bn20132014201520162017201820192020202120222023202420252026USD ordered2013 - Civil penalty: $907m2013 - Disgorgement: $6m2014 - Civil penalty: $1.6bn2014 - Disgorgement: $1m2015 - Civil penalty: $1.4bn2015 - Disgorgement: $312m2016 - Civil penalty: $527m2016 - Disgorgement: $752m2017 - Civil penalty: $181m2017 - Disgorgement: $782m2018 - Civil penalty: $854m2018 - Disgorgement: $163m2019 - Civil penalty: $120m2019 - Disgorgement: $102m2020 - Civil penalty: $199m2020 - Disgorgement: $572m2021 - Civil penalty: $63m2021 - Disgorgement: $685m2022 - Civil penalty: $1.3bn2022 - Disgorgement: $322m2023 - Civil penalty: $1.9bn2023 - Disgorgement: $107m2024 - Civil penalty: $305m2024 - Disgorgement: $14.9bn2025 - Civil penalty: $6bn2025 - Disgorgement: $56m2026 - Civil penalty: $17m2026 - Disgorgement: $25mCivil penaltyDisgorgement

Methodology and caveats · CSV

Penalty distribution by technique

Penalty distributions within every technique are heavily right-skewed: the median is far below the mean, and outliers extend well beyond the upper whisker. Any single "typical penalty" figure for a technique is misleading, which is why this site reports medians alongside the spread.

Distribution of civil penalties by manipulation techniqueA horizontal box plot for 12 techniques on a logarithmic scale, because penalties span six orders of magnitude. Each row shows the interquartile range as a box, the median as a vertical line, whiskers to the furthest values within 1.5 times the interquartile range, and individual outliers as points. Sample size is printed beneath each label.Civil penalty (USD), logarithmic scale $1k $10k $100k $1m $10m $100m $1bn $10bnLogarithmic scale: each gridline is ten times the previous one.Price manipulation n=49 Price manipulation median: $30m$325m$400m$475m$475m$800m$1.2bn$1.4bn$1.7bnBoiler rooms n=4 Boiler rooms median: $2m$37mPonzi schemes n=90 Ponzi schemes median: $1m$12m$13m$17m$19m$20m$23m$27m$32m$32m$100m$111m$147m$5.9bnSpoofing n=63 Spoofing median: $600k$9m$12m$15m$17m$24m$25m$30m$38m$41m$45m$50m$1.7bnWash trading n=45 Wash trading median: $600k$5m$5m$5m$6m$7m$9m$35m$41m$325mPaid stock promotion n=40 Paid stock promotion median: $238k$2m$3m$3m$6m$6mPump and dump n=11 Pump and dump median: $223k$910k$26mUnregistered distributions n=33 Unregistered distributions median: $150k$3m$19m$37mInsider trading n=120 Insider trading median: $100k$581k$682k$1m$1m$1m$1m$1m$2m$2m$2m$2m$3m$3m$4m$4m$10m$14m$40m$41m$55m$91m$1.7bnReverse merger schemes n=15 Reverse merger schemes median: $100k$575k$1m$2mNaked short selling debate n=6 Naked short selling debate median: $45kUndisclosed control blocks n=10 Undisclosed control blocks median: $31k$2m

Methodology and caveats · CSV

Criminal parallel share by technique

The likelihood that a manipulation action runs alongside a criminal prosecution varies sharply by technique. Schemes with identifiable individual victims and clear documentary evidence attract criminal attention far more often than order-book conduct, where intent must be inferred from data.

Share of actions with a parallel criminal proceeding, by techniqueA horizontal bar chart showing, for each manipulation technique, the percentage of enforcement actions in this library whose release references a parallel criminal matter. Sample size is given in each label.Share of actions referencing a criminal parallel (%)Ponzi schemes (n=532) Ponzi schemes (n=532): 47% 47%Price manipulation (n=72) Price manipulation (n=72): 46% 46%Benchmark submission rigging (n=32) Benchmark submission rigging (n=32): 44% 44%Pump and dump (n=121) Pump and dump (n=121): 43% 43%Boiler rooms (n=71) Boiler rooms (n=71): 42% 42%Insider trading (n=608) Insider trading (n=608): 36% 36%Matched orders (n=24) Matched orders (n=24): 33% 33%Spoofing (n=114) Spoofing (n=114): 30% 30%Paid stock promotion (n=219) Paid stock promotion (n=219): 28% 28%Wash trading (n=90) Wash trading (n=90): 22% 22%Reverse merger schemes (n=66) Reverse merger schemes (n=66): 20% 20%Undisclosed control blocks (n=119) Undisclosed control blocks (n=119): 12% 12%Unregistered distributions (n=136) Unregistered distributions (n=136): 10% 10%Naked short selling debate (n=41) Naked short selling debate (n=41): 0% 0%

Methodology and caveats · CSV

Asset-class mix over time

Equities remain the largest single category in every year of this data, but the share of actions touching digital assets has grown from negligible to a substantial minority, and a growing number of actions are tagged with more than one asset class.

Enforcement actions by asset class, per yearA line chart with one line per asset class, showing the number of enforcement actions per year touching each. Lines are distinguished by ink density rather than colour. Actions touching several asset classes are counted in each. 0 25 50 75 10020132014201520162017201820192020202120222023202420252026Actions2013 - equities: 22014 - equities: 12015 - equities: 932016 - equities: 882017 - equities: 992018 - equities: 922019 - equities: 592020 - equities: 622021 - equities: 462022 - equities: 562023 - equities: 702024 - equities: 782025 - equities: 322026 - equities: 252013 - futures: 82014 - futures: 102015 - futures: 132016 - futures: 152017 - futures: 142018 - futures: 272019 - futures: 182020 - futures: 202021 - futures: 82022 - futures: 112023 - futures: 122024 - futures: 112025 - futures: 22026 - futures: 22013 - crypto: 02014 - crypto: 02015 - crypto: 42016 - crypto: 02017 - crypto: 32018 - crypto: 102019 - crypto: 52020 - crypto: 132021 - crypto: 142022 - crypto: 162023 - crypto: 332024 - crypto: 152025 - crypto: 52026 - crypto: 12013 - fx: 02014 - fx: 22015 - fx: 42016 - fx: 32017 - fx: 52018 - fx: 02019 - fx: 12020 - fx: 22021 - fx: 22022 - fx: 42023 - fx: 42024 - fx: 52025 - fx: 12026 - fx: 02013 - commodities: 32014 - commodities: 42015 - commodities: 92016 - commodities: 32017 - commodities: 62018 - commodities: 142019 - commodities: 152020 - commodities: 122021 - commodities: 62022 - commodities: 42023 - commodities: 72024 - commodities: 52025 - commodities: 22026 - commodities: 1equitiesfuturescryptofxcommodities

Methodology and caveats · CSV

Months from filing to recorded resolution

Most actions in this library are recorded as resolved on the same date they are announced, because regulators frequently publish settled matters as a single release rather than filing and settling separately. The distribution therefore describes announcement practice as much as litigation duration.

Median months from recorded filing to recorded resolution, by yearA line chart of the median number of months between the filing date and the resolution date recorded for actions in each year. Values near zero indicate that the action was announced already settled. 0 0.25 0.5 0.75 120132014201520162017201820192020202120222023202420252026Months2013 - Median months: 02014 - Median months: 02015 - Median months: 02016 - Median months: 02017 - Median months: 02018 - Median months: 02019 - Median months: 02020 - Median months: 02021 - Median months: 02022 - Median months: 02023 - Median months: 02024 - Median months: 02025 - Median months: 02026 - Median months: 0

Methodology and caveats · CSV

SEC trading suspensions per month

Trading suspensions arrive in clusters rather than at a steady rate, because the Commission frequently suspends a batch of related issuers at once. A suspension is a protective measure and alleges no wrongdoing by anyone.

SEC trading suspensions per monthA column chart of the number of Exchange Act section 12(k) trading suspensions ordered in each of the last 48 months for which we hold data. Suspensions are frequently issued in batches, producing an uneven distribution. 0 10 20 30 402018-112019-032019-062019-092020-012020-042020-072020-102021-012021-042021-072021-112023-052024-092025-102026-01Suspensions ordered2018-11 - Suspensions: 12018-12 - Suspensions: 22019-02 - Suspensions: 12019-03 - Suspensions: 12019-04 - Suspensions: 12019-05 - Suspensions: 22019-06 - Suspensions: 82019-07 - Suspensions: 172019-08 - Suspensions: 202019-09 - Suspensions: 272019-10 - Suspensions: 22019-11 - Suspensions: 22020-01 - Suspensions: 132020-02 - Suspensions: 32020-03 - Suspensions: 62020-04 - Suspensions: 232020-05 - Suspensions: 82020-06 - Suspensions: 52020-07 - Suspensions: 132020-08 - Suspensions: 132020-09 - Suspensions: 262020-10 - Suspensions: 12020-11 - Suspensions: 12020-12 - Suspensions: 12021-01 - Suspensions: 12021-02 - Suspensions: 112021-03 - Suspensions: 62021-04 - Suspensions: 32021-05 - Suspensions: 352021-06 - Suspensions: 12021-07 - Suspensions: 32021-08 - Suspensions: 172021-09 - Suspensions: 242021-11 - Suspensions: 12022-06 - Suspensions: 12022-07 - Suspensions: 12023-05 - Suspensions: 12023-09 - Suspensions: 12023-11 - Suspensions: 22024-09 - Suspensions: 12024-10 - Suspensions: 12025-09 - Suspensions: 22025-10 - Suspensions: 72025-11 - Suspensions: 22025-12 - Suspensions: 12026-01 - Suspensions: 12026-02 - Suspensions: 12026-06 - Suspensions: 1

Methodology and caveats · CSV

Venues named most often in manipulation actions

Venue counts measure where enforcement activity is concentrated and where regulators have surveillance reach, not which venues have the most manipulation. A venue that runs good surveillance and refers cases will appear more often, not less.

Trading venues named most often in manipulation enforcement actionsA horizontal bar chart of the trading venues mentioned most frequently across the enforcement actions in this library, ranked by the number of actions naming each.Actions naming this venueNasdaq Nasdaq: 148 148NYSE NYSE: 100 100CME CME: 93 93OTC OTC: 88 88CBOT CBOT: 31 31NYMEX NYMEX: 21 21COMEX COMEX: 19 19ICE ICE: 18 18Binance Binance: 5 5Coinbase Coinbase: 4 4

Methodology and caveats · CSV

The underlying data

Every chart above is derived from the same source: 2,117 structured enforcement records, each linked to the regulator's own release. The whole library is downloadable as a single JSON file, and the methodology behind its compilation is described on the sources page.