# SEC EDGAR Earnings Transcripts Scraper (`jungle_synthesizer/sec-edgar-earnings-transcripts-scraper`) Actor Extract earnings transcripts and press releases from SEC EDGAR 8-K filings (Exhibit 99.1 / 99.2). Search by ticker, CIK, or date range. Returns structured records with full exhibit text, speaker labels, and filing metadata. - **URL**: https://apify.com/jungle\_synthesizer/sec-edgar-earnings-transcripts-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Business, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## SEC EDGAR Earnings Transcripts Scraper Extracts earnings transcripts and press releases from [SEC EDGAR](https://www.sec.gov/) 8-K filings. Returns structured records with full exhibit text, speaker labels, and filing metadata for Exhibit 99.1 (earnings press release) and Exhibit 99.2 (verbatim transcript) — the official government-filed source for US public company earnings calls. --- ### SEC EDGAR Earnings Transcripts Scraper Features - Extracts Exhibit 99.1 (earnings press releases) and Exhibit 99.2 (verbatim transcripts) as plain text from 8-K filings - Filters to Item 2.02 filings — Results of Operations — so you get earnings-relevant records without wading through every 8-K - Detects verbatim transcripts heuristically and extracts speaker labels (OPERATOR, CEO, CFO, analyst names) - Supports three input modes: ticker list, CIK list, or full-text search query - Returns complete filing metadata: accession number, filing date, period of report, fiscal quarter, SIC code - Handles iXBRL-wrapped exhibits gracefully — strips inline XBRL tags before returning clean text - Respects SEC fair-access policy (5 req/sec, descriptive User-Agent) out of the box - Works without proxy or credentials — SEC EDGAR is public-domain US government data --- ### Who Uses SEC EDGAR Earnings Transcripts Data? - **Quantitative researchers** — Build NLP models on verbatim management language; sentiment analysis across quarters; tone-shift detection - **Equity analysts** — Pull earnings call text programmatically instead of paying Bloomberg Terminal rates for the same data - **Financial AI startups** — Fine-tune LLMs on earnings calls; build RAG pipelines over the official source corpus - **Hedge funds** — Systematic analysis of 8-K Item 2.02 filings across the entire US-listed universe - **Academic researchers** — Longitudinal studies of earnings disclosure language from 2001 to present --- ### How SEC EDGAR Earnings Transcripts Scraper Works 1. Resolves input (ticker symbols, CIK numbers, or search query) to a list of SEC filers using the official `company_tickers.json` registry 2. Fetches the `submissions/CIK{n}.json` API endpoint for each filer to enumerate recent 8-K filings and filters to Item 2.02 entries within the requested date range 3. For each matching filing, fetches the filing index page to discover Exhibit 99.1 and 99.2 filenames, then fetches each exhibit HTML body 4. Extracts plain text, detects verbatim transcripts, pulls speaker labels, and saves a structured record per filing --- ### Input ```json { "tickers": ["AAPL", "MSFT"], "dateFrom": "2025-01-01", "dateTo": "2025-12-31", "fetchExhibitText": true, "maxItems": 50 } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `tickers` | array | `["AAPL"]` | Ticker symbols to look up (resolves to CIKs automatically) | | `cikNumbers` | array | — | SEC CIK numbers (use instead of tickers when you know the CIK) | | `searchQuery` | string | — | Full-text search query across all 8-K filings (e.g. `"earnings call"`) | | `dateFrom` | string | — | Earliest filing date to include (YYYY-MM-DD) | | `dateTo` | string | — | Latest filing date to include (YYYY-MM-DD) | | `fetchExhibitText` | boolean | `true` | Fetch and extract plain text from exhibits. Set to `false` for metadata-only runs | | `maxItems` | integer | `10` | Maximum number of filing records to return | **Mode selection:** Provide `tickers`, `cikNumbers`, or `searchQuery` — exactly one is required. If you provide `tickers`, the actor resolves them to CIKs via the SEC company registry automatically. *** ### Output Fields ```json { "cik": "0000320193", "ticker": "AAPL", "company_name": "Apple Inc.", "sic_code": "3571", "form_type": "8-K", "accession_number": "0000320193-26-000011", "filing_date": "2026-04-30", "period_of_report": "2026-03-28", "items_reported": "2.02,9.01", "has_item_202": true, "fiscal_quarter": "Q1 2026", "exhibit_99_1_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019326000011/a8-kex991q2202603282026.htm", "exhibit_99_1_text": "Apple reports second quarter results...", "exhibit_99_2_url": "", "exhibit_99_2_text": "", "transcript_present": false, "transcript_speakers": "", "filing_index_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019326000011/", "filing_html_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019326000011/aapl-20260430.htm", "filer_full_filename": "aapl-20260430.htm", "source_endpoint": "ticker" } ``` | Field | Type | Description | |-------|------|-------------| | `cik` | string | 10-digit zero-padded SEC Central Index Key | | `ticker` | string | Primary common-stock ticker symbol | | `company_name` | string | Company legal name as registered with the SEC | | `sic_code` | string | Standard Industrial Classification code | | `form_type` | string | SEC form type (always 8-K) | | `accession_number` | string | Canonical SEC filing identifier | | `filing_date` | string | Date the filing was accepted by the SEC (YYYY-MM-DD) | | `period_of_report` | string | Quarter-end date the filing reports on (YYYY-MM-DD) | | `items_reported` | string | Comma-separated 8-K item codes (e.g. `2.02,9.01`) | | `has_item_202` | boolean | True when the filing includes Item 2.02 (Results of Operations) | | `fiscal_quarter` | string | Human-readable fiscal quarter (e.g. `Q2 2026`) | | `exhibit_99_1_url` | string | Direct URL to Exhibit 99.1 (earnings press release) when present | | `exhibit_99_1_text` | string | Plain text extracted from Exhibit 99.1 | | `exhibit_99_2_url` | string | Direct URL to Exhibit 99.2 (verbatim transcript) when present | | `exhibit_99_2_text` | string | Plain text extracted from Exhibit 99.2 | | `transcript_present` | boolean | True when Exhibit 99.2 appears to be a verbatim earnings call transcript | | `transcript_speakers` | string | Pipe-separated speaker labels extracted from the transcript | | `filing_index_url` | string | URL of the SEC filing index page | | `filing_html_url` | string | URL of the primary 8-K HTML document | | `filer_full_filename` | string | Primary submission filename | | `source_endpoint` | string | Which input mode produced this record (`ticker`, `cik`, `search`) | *** ### FAQ #### Does this require an API key or login? No. SEC EDGAR is public-domain US government data. The actor handles the required User-Agent header automatically. #### What is Exhibit 99.1 vs Exhibit 99.2? Exhibit 99.1 is the earnings press release — the formatted document companies publish alongside quarterly results. Exhibit 99.2, when filed, is a verbatim transcript of the earnings call. Roughly 20-30% of filers include a verbatim transcript; the rest file only the press release. #### How far back does the data go? EDGAR HTML-format filings extend to approximately 2001. Older filings may lack proper Exhibit 99.x labeling — the actor handles these gracefully and returns `transcript_present: false` rather than failing. #### Can I scrape the full S\&P 500 earnings history? Yes. Provide a ticker list of up to 500 companies and set `dateFrom`/`dateTo` to the range you need. Each company typically has 4 quarterly 8-K Item 2.02 filings per year, so the full S\&P 500 for one year is roughly 2,000 records. #### What rate limits apply? SEC EDGAR enforces a 10 req/sec fair-access ceiling. This actor defaults to 5 req/sec with automatic backoff on 429 responses. *** ### Need More Features? To request additional fields or input modes, file an issue on the actor page. Custom builds for specific filer sets or date ranges are available. ### Why Use SEC EDGAR Earnings Transcripts Scraper? - **Canonical source** — Official SEC filings, not third-party republication. The same documents Bloomberg Terminal charges SaaS prices for. - **No paywalls** — Public-domain US government data. The only gate is a proper User-Agent header, which the actor handles. - **Per-exhibit extraction** — Returns Exhibit 99.1 and 99.2 separately with distinct fields, not a raw dump of the full filing HTML. - **Speaker detection** — Extracts analyst and executive names from verbatim transcripts so you can filter by speaker without NLP preprocessing. - **Three input modes** — Ticker lookup, direct CIK, or full-text search — covering every discovery workflow. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `tickers` (type: `array`): One or more stock ticker symbols (e.g. AAPL, MSFT, TSLA). The actor resolves tickers to CIK numbers via the SEC company registry. ## `cikNumbers` (type: `array`): One or more SEC Central Index Key numbers (10-digit, zero-padded or plain integer). Use instead of tickers when you already know the CIK. ## `searchQuery` (type: `string`): Search query for EDGAR full-text search (e.g. 'earnings call', 'quarterly results'). Returns 8-K filings containing the query text. ## `dateFrom` (type: `string`): Earliest filing date to include (YYYY-MM-DD). Applies to all input modes. ## `dateTo` (type: `string`): Latest filing date to include (YYYY-MM-DD). Applies to all input modes. ## `fetchExhibitText` (type: `boolean`): When enabled, fetch and extract plain text from Exhibit 99.1 and 99.2. When disabled, only filing metadata and exhibit URLs are returned (faster). ## `maxItems` (type: `integer`): Maximum number of filing records to return. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tickers": [ "AAPL" ], "fetchExhibitText": true, "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tickers": [ "AAPL" ], "searchQuery": "", "dateFrom": "", "dateTo": "", "fetchExhibitText": true, "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/sec-edgar-earnings-transcripts-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tickers": ["AAPL"], "searchQuery": "", "dateFrom": "", "dateTo": "", "fetchExhibitText": True, "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/sec-edgar-earnings-transcripts-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tickers": [ "AAPL" ], "searchQuery": "", "dateFrom": "", "dateTo": "", "fetchExhibitText": true, "maxItems": 10 }' | apify call jungle_synthesizer/sec-edgar-earnings-transcripts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/sec-edgar-earnings-transcripts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SEC EDGAR Earnings Transcripts Scraper", "description": "Extract earnings transcripts and press releases from SEC EDGAR 8-K filings (Exhibit 99.1 / 99.2). Search by ticker, CIK, or date range. Returns structured records with full exhibit text, speaker labels, and filing metadata.", "version": "1.0", "x-build-id": "8xhq9lJRzbbskErhg" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~sec-edgar-earnings-transcripts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-sec-edgar-earnings-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~sec-edgar-earnings-transcripts-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-sec-edgar-earnings-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~sec-edgar-earnings-transcripts-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-sec-edgar-earnings-transcripts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "tickers": { "title": "Ticker Symbol(s)", "type": "array", "description": "One or more stock ticker symbols (e.g. AAPL, MSFT, TSLA). The actor resolves tickers to CIK numbers via the SEC company registry.", "items": { "type": "string" } }, "cikNumbers": { "title": "CIK Number(s)", "type": "array", "description": "One or more SEC Central Index Key numbers (10-digit, zero-padded or plain integer). Use instead of tickers when you already know the CIK.", "items": { "type": "string" } }, "searchQuery": { "title": "Full-Text Search Query", "type": "string", "description": "Search query for EDGAR full-text search (e.g. 'earnings call', 'quarterly results'). Returns 8-K filings containing the query text." }, "dateFrom": { "title": "Filing Date From", "type": "string", "description": "Earliest filing date to include (YYYY-MM-DD). Applies to all input modes." }, "dateTo": { "title": "Filing Date To", "type": "string", "description": "Latest filing date to include (YYYY-MM-DD). Applies to all input modes." }, "fetchExhibitText": { "title": "Fetch Exhibit Text", "type": "boolean", "description": "When enabled, fetch and extract plain text from Exhibit 99.1 and 99.2. When disabled, only filing metadata and exhibit URLs are returned (faster).", "default": true }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of filing records to return.", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```