US Analyst Estimates Consensus — Quarterly, 10y (2016–2025)

DatasetCSV.GZOpenFinanceAnalystEstimatesConsensusFundamentalsFree

Ten years of sell-side consensus quarterly estimates for 8,480 US-listed issuers, covering 175,834 quarter-periods. Each row carries low / avg / high consensus values across six fundamental lines — revenue, EBITDA, EBIT, net income, EPS, SG&A — plus analyst-coverage counts. Read with pandas.read_csv(path, compression='gzip', parse_dates=['date']). Useful for: pairing with the Earnings Surprises listing for analyst-accuracy / drift research, tracking estimate revisions over time, building consensus-bias factors, training models on the dispersion (high − low) as a proxy for fundamental uncertainty.

Use Cases

Analyst-accuracy and dispersion research
Estimate-revision momentum factors (consensus drift)
Pairing with Earnings Surprises listing for full beat/miss + estimate-history analysis
Fundamental-uncertainty signal: range (high − low) / |avg| as confidence proxy
Consensus-bias studies by sector and market-cap bucket

Methodology

Universe: every USD-reporting US-listed issuer (~17,500) from FMP's financial-statement-symbol-list. For each, fetched quarterly consensus from FMP's /analyst-estimates?symbol=…&period=quarter&limit=80. Filtered to date >= 2016-01-01 AND date <= today (forward-looking estimates excluded).

Update Schedule

Static snapshot. Reflects what FMP had indexed as of the build date.

Attribution

Source: aggregated sell-side analyst estimates via Financial Modeling Prep.

Schema

name	type
symbol	string
date	date
revenueAvg	number
revenueLow	number
revenueHigh	number
ebitdaAvg	number
ebitdaLow	number
ebitdaHigh	number
ebitAvg	number
ebitLow	number
ebitHigh	number
netIncomeAvg	number
netIncomeLow	number
netIncomeHigh	number
epsAvg	number
epsLow	number
epsHigh	number
sgaExpenseAvg	number
sgaExpenseLow	number
sgaExpenseHigh	number

Showing 20 of 22 rows

Sample Data

date	epsAvg	epsLow	symbol	ebitAvg	ebitLow	epsHigh	ebitHigh	ebitdaAvg	ebitdaLow	ebitdaHigh	revenueAvg	revenueLow	revenueHigh	netIncomeAvg	netIncomeLow	netIncomeHigh	sgaExpenseAvg	sgaExpenseLow	numAnalystsEps	sgaExpenseHigh	numAnalystsRevenue
2016-03-29	0.38	0.3	BHE	19318250	15454600	0.46	23181900	35881500	28705200	43057800	675938461	540750768	811126152	13124250	10499400	15749100	37112937	29690350	16	44535525	16
2016-06-29	0.33	0.26	BHE	18294375	14635500	0.4	21953250	33137156	26509725	39764587	597987096	478389676	717584515	13081406	10465125	15697687	32321437	25857150	7	38785725	7
2016-09-29	0.34	0.27	BHE	17565371	14052297	0.41	21078445	31452914	25162331	37743497	558347222	446677776	670016665	21120800	16896640	25344960	27282571	21826057	14	32739085	14

Get this via API

# 1. Add dAgentBase once, in any MCP client. No install, no vendor keys.
#    Claude.ai / Claude Desktop: Settings -> Connectors -> Add custom connector
#    Cursor / Claude Code / others: mcp.json
{
  "mcpServers": {
    "dagentbase": {
      "url": "https://dagentbase.com/api/mcp",
      "headers": { "Authorization": "Bearer dm_live_YOUR_KEY" }
    }
  }
}

# 2. Then ask your agent, in plain language:
#    "Preview 'US Analyst Estimates Consensus — Quarterly, 10y (2016–2025)' and, if it fits, claim it and download the files."
#    Tools it will use: search_listings -> preview_listing -> purchase_listing -> get_download_urls

# 1. Add dAgentBase once, in any MCP client. No install, no vendor keys.
#    Claude.ai / Claude Desktop: Settings -> Connectors -> Add custom connector
#    Cursor / Claude Code / others: mcp.json
{
  "mcpServers": {
    "dagentbase": {
      "url": "https://dagentbase.com/api/mcp",
      "headers": { "Authorization": "Bearer dm_live_YOUR_KEY" }
    }
  }
}

# 2. Then ask your agent, in plain language:
#    "Preview 'US Analyst Estimates Consensus — Quarterly, 10y (2016–2025)' and, if it fits, claim it and download the files."
#    Tools it will use: search_listings -> preview_listing -> purchase_listing -> get_download_urls

Free

one time · open license

Details

Date Range2016-01-01 — 2026-09-05

Rows175,834

Size11.9 MB

Files1

FormatCSV.GZ

Available formats

CSV.GZ11.9 MB

Freeopen