Penske Automotive Group, Inc.
AI-native vector embeddings for Penske Automotive Group, Inc. quarterly earnings calls
Ticker
PAG
Coverage
2020–present
Embedding dims
768
Model
gemini-embedding-2-preview
About PAG Earnings Embeddings
VectorFin provides vector embeddings for every Penske Automotive Group, Inc. earnings call from 2020 through the present quarter. Each earnings call transcript is chunked into semantically coherent segments and vectorized using Google's gemini-embedding-2-preview model, producing 768-dimensional dense vectors optimized for cosine similarity search.
All data is bitemporal: every embedding chunk carries an effective_ts (when the earnings call occurred) and a knowledge_ts (when VectorFin ingested and vectorized the data). This enables point-in-time backtesting — query the data as it was known at any historical date.
Embedding data is updated within 24 hours of each earnings call. The full history is available via REST API (all plans) or as Apache Iceberg tables on GCS (Pro+ plans), queryable natively from Snowflake, BigQuery, or Databricks.
Common PAG use cases include quarter-over-quarter sentiment comparison, similarity search for analogous commentary at peers, and Retrieval-Augmented Generation (RAG) — grounding LLM answers about Penske Automotive Group, Inc. in citable transcript chunks.
Available fiscal periods
Showing recent 8 quarters. 2020–present available via API (beta).
Access via API
# Fetch PAG embeddings for the latest quarter
curl https://api.vectorfinancials.com/v1/embeddings/PAG \
-H "X-API-Key: vf_sk_your_key_here" \
-G \
-d "fiscal_period=2024-Q4" \
-d "limit=10"
# Response schema
{
"data": [{
"ticker": "PAG",
"fiscal_period": "2024-Q4",
"chunk_idx": 0,
"text": "...",
"embedding": [0.023, -0.091, ...],
"effective_ts": "2025-01-30T00:00:00Z",
"knowledge_ts": "2025-01-31T06:00:00Z",
"model_version": "gemini-embedding-2-preview"
}],
"next_cursor": "..."
}Start using PAG embeddings today
Free tier includes top 100 tickers with 1,000 API calls/month.