A company's defined accounting period (quarter or full year) that may differ from the calendar year, used as the primary temporal key in VectorFin's embedding and signal tables.
In Plain English
Most people think of the year as ending on December 31. But public companies have the flexibility to define their own fiscal year — the 12-month accounting period that determines when they close their books and file their reports. Many companies align to the calendar year, but a surprising number don't.
Apple's fiscal year ends in September. Walmart's fiscal year ends in January. Microsoft's ends in June. This creates an important complication when analyzing companies across industries: a "Q3 2024" report from Apple covers April-June 2024, while "Q3 2024" from a December-fiscal-year company covers July-September 2024. They're the same label but different calendar periods.
The mismatch matters for several reasons. If you're analyzing supply chain dynamics across the semiconductor industry, you need to be careful about comparing Apple's fiscal Q3 (April-June) to Intel's fiscal Q3 (July-September) — they're looking at different parts of the economic cycle. For companies in the same supply chain, fiscal period alignment reveals whether reported results are contemporaneous or lagged.
For AI systems and databases, the fiscal period is also the natural primary key for time-series financial data. "AAPL 2024-Q4" uniquely identifies Apple's fourth fiscal quarter of fiscal year 2024 (which corresponds to July-September 2024 on the calendar). This is the identifier VectorFin uses throughout its system — every earnings call embedding, every signal tied to a quarterly period, is indexed by this (ticker, fiscal_period) key.
Companies are required to disclose their fiscal year end in their SEC filings. Calendar-year companies (FYE December 31) typically have their Q4 earnings calls in January-February of the following calendar year.
Technical Definition
Fiscal period format used by VectorFin: {YYYY}-Q{N}
Where:
YYYY= the company's fiscal year (as labeled by the company)N= quarter number within that fiscal year (1-4)
Examples:
- Apple (FYE September):
2024-Q4= July 1 – September 30, 2024 - Microsoft (FYE June):
2024-Q4= April 1 – June 30, 2024 - Walmart (FYE January):
2025-Q1= February 1 – April 30, 2024 (calendar 2024) - Calendar-year company (FYE December):
2024-Q3= July 1 – September 30, 2024
VectorFin stores both fiscal_period (company-labeled) and calendar_quarter (ISO calendar quarter) for cross-company comparisons. When comparing companies in the same supply chain, always align on calendar_quarter, not fiscal_period.
The effective_ts in VectorFin's tables is set to the end date of the fiscal period:
- Fiscal period
2024-Q4for a September FYE company → effective_ts = 2024-09-30 - Fiscal period
2024-Q4for a December FYE company → effective_ts = 2024-12-31
Earnings call timing: companies typically hold their earnings call 2-4 weeks after fiscal quarter end. This gap is the pipeline lag between fiscal period end (effective_ts) and when VectorFin ingests the transcript (knowledge_ts).
How VectorFin Uses This
The fiscal period is the primary lookup key for all time-series data in VectorFin's API. Every embedding and sentiment-drift signal is indexed by (ticker, fiscal_period):
GET https://api.vectorfinancials.com/v1/embeddings/{ticker}?period=2024-Q4
GET https://api.vectorfinancials.com/v1/signals/sentiment-drift/{ticker}?period=2024-Q3For cross-company analysis, use the calendar_quarter parameter instead of period to normalize across fiscal calendars:
GET https://api.vectorfinancials.com/v1/embeddings/{ticker}?calendar_quarter=2024-Q3This returns the earnings call data that covers July-September 2024 regardless of the company's fiscal year end, enabling apples-to-apples comparison across companies with different fiscal calendars.
Code Example
import requests
import pandas as pd
API_BASE = "https://api.vectorfinancials.com"
API_KEY = "vf_your_api_key_here"
# Cross-company comparison normalized to calendar quarter
# (not fiscal period, which differs across companies)
tickers = ["AAPL", "MSFT", "NVDA", "GOOGL", "META"]
calendar_quarter = "2024-Q3" # July-September 2024
print(f"Sentiment drift for {calendar_quarter} (calendar-normalized):")
print(f"{'Ticker':<8} {'Fiscal Period':<15} {'Calendar Q':<12} {'Drift Score'}")
print("-" * 50)
for ticker in tickers:
# Use calendar_quarter for cross-company alignment
resp = requests.get(
f"{API_BASE}/v1/signals/sentiment-drift/{ticker}",
params={"calendar_quarter": calendar_quarter},
headers={"X-API-Key": API_KEY},
)
if resp.ok:
data = resp.json()
print(f"{ticker:<8} {data.get('fiscal_period', 'N/A'):<15} "
f"{data.get('calendar_quarter', 'N/A'):<12} "
f"{data.get('drift_score', 'N/A')}")
# Note: AAPL 2024-Q4 and MSFT 2024-Q1 both cover July-September 2024
# Using calendar_quarter ensures you're comparing the same real-world periodExternal References
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