# Fundamental Data

> Fundamental data covers company financials, estimates and economic statistics. Learn where it comes from, point in time issues, restatements and how quants use it.

Source: https://learn.tradelabsai.com/alternative-data/fundamental-data/  
Track: Data and Alternative Data · Level: Intermediate · Updated: 2026-10-03  
Publisher: TradeLabs AI (https://tradelabsai.com). Education, not financial advice.  
Cite as: TradeLabs Learn, "Fundamental Data", https://learn.tradelabsai.com/alternative-data/fundamental-data/

Fundamental data describes the underlying businesses and economies behind market prices: revenue, earnings, cash flow, balance sheets, analyst estimates, ownership, and macroeconomic statistics. Discretionary investors read it company by company; quantitative traders process it for thousands of companies at once to build value, quality and growth signals. Using fundamental data correctly requires care, because when data was available, and how it has been revised, can make the difference between a realistic backtest and a fantasy.

## Types of fundamental data

| Category | Examples | Lesson |
|---|---|---|
| Financial statements | Revenue, net income, assets, cash flow | [Reading Financial Statements](https://learn.tradelabsai.com/fundamentals/reading-financial-statements/) |
| Per share and ratio data | EPS, book value per share, P/E, ROE | [P/E and Forward P/E](https://learn.tradelabsai.com/fundamentals/p-e-and-forward-p-e/) |
| Analyst estimates | Consensus EPS and revenue, revisions, recommendations | [Guidance and Earnings Revisions](https://learn.tradelabsai.com/fundamentals/guidance-and-earnings-revisions/) |
| Corporate actions | Dividends, splits, mergers | [Corporate Actions, Delistings and Rolls in Backtests](https://learn.tradelabsai.com/research/corporate-actions-in-backtests/) |
| Ownership data | Institutional holdings (13F filings), insider trades | |
| Segment data | Revenue and profit by division or region | |
| Macroeconomic data | GDP, inflation, employment | [GDP](https://learn.tradelabsai.com/macro/gdp/) |

## Where it comes from

- **Company filings:** 10 K, 10 Q and 8 K reports on the SEC's EDGAR system, with structured XBRL data.
- **Commercial vendors:** firms such as S&P Global (Compustat, Capital IQ), FactSet, Bloomberg, LSEG and Morningstar standardise data across companies.
- **Free sources:** EDGAR, company websites and some free APIs, with more cleaning required.
- **Government agencies:** for economic data, such as the BLS, BEA and Federal Reserve (FRED database).

## Point in time vs restated data

Companies revise past figures, and vendors may update their databases with restated numbers. A backtest that uses today's version of historical data may use numbers that were not available at the time.

**Example: A look ahead trap**
A company reported Q2 earnings of $1.00 per share on 5 August. In March of the next year, it restated Q2 to $0.80 after an accounting review. A database that stores only the latest version shows $0.80 for Q2. A backtest selecting stocks with weak Q2 earnings on 6 August would wrongly treat the company as weak, using information that only became public seven months later. Point in time databases store each value as it was known on each date. See [Point-in-Time and Survivorship-Free Data](https://learn.tradelabsai.com/programming/point-in-time-data/) and [Look-Ahead Bias](https://learn.tradelabsai.com/research/look-ahead-bias/).

## Reporting lags

Fundamental data becomes available only when it is released. Quarterly results arrive weeks after the quarter ends; annual reports even later. A common backtest rule is to assume data is available only after a lag, such as 45 to 90 days after the period end, unless precise filing dates are known.

## Standardisation challenges

- **Different accounting standards:** US GAAP vs IFRS.
- **Different fiscal years:** must be aligned to calendar periods.
- **Adjusted vs reported figures:** non GAAP numbers differ by company. See [Earnings Quality and Cash Conversion](https://learn.tradelabsai.com/fundamentals/earnings-quality/).
- **Currency conversion** for international companies.
- **Industry differences:** banks and insurers have very different statements.

## How quants use fundamental data

| Signal family | Examples | Lesson |
|---|---|---|
| Value | Book to market, earnings yield, FCF yield | [Value Factor](https://learn.tradelabsai.com/research/value-factor/) |
| Quality | ROE, ROIC, accruals, leverage | [Quality and Profitability Factors](https://learn.tradelabsai.com/research/quality-factor/) |
| Growth | Sales and earnings growth | [Growth and Dividend Factors](https://learn.tradelabsai.com/research/growth-and-dividend-factors/) |
| Revisions | Changes in analyst estimates | [Guidance and Earnings Revisions](https://learn.tradelabsai.com/fundamentals/guidance-and-earnings-revisions/) |
| Ownership | Institutional and insider activity | |

See [Factor Investing Explained](https://learn.tradelabsai.com/research/factor-investing-explained/).

## Common pitfalls

- **Look ahead bias** from restated or late data.
- **Survivorship bias** from missing delisted companies. See [Survivorship and Selection Bias](https://learn.tradelabsai.com/research/survivorship-and-selection-bias/).
- **Mismatched dates** between prices and fundamentals.
- **Outliers** from tiny denominators, such as P/E with near zero earnings. See [Outliers and Robust Statistics](https://learn.tradelabsai.com/math/outliers-and-robust-statistics/).

## Frequently asked questions

### What is fundamental data?

Information about the financial health and performance of companies and economies, such as earnings, balance sheets, analyst estimates and economic statistics.

### What is point in time data?

Data stored exactly as it was known on each historical date, including original values before any restatements, to avoid look ahead bias.

### Where can I get fundamental data?

From company filings on the SEC's EDGAR system, commercial vendors such as FactSet and S&P Global, and government statistics agencies.

Next, learn about news as data in [News Feeds](https://learn.tradelabsai.com/alternative-data/news-feeds/).

## Continue learning

- Next lesson: [News Feeds](https://learn.tradelabsai.com/alternative-data/news-feeds/)
- Previous lesson: [Market Data Explained](https://learn.tradelabsai.com/alternative-data/market-data-explained/)
- Related: [Market Data Explained](https://learn.tradelabsai.com/alternative-data/market-data-explained/): Market data covers trades, quotes, order book depth and reference data. Learn the main types, levels of data, real time vs delayed feeds, costs and common pitfalls.
- Related: [Reading Financial Statements](https://learn.tradelabsai.com/fundamentals/reading-financial-statements/): Learn how the income statement, balance sheet and cash flow statement fit together, where to find them and what traders look for first in a company's filings.
- Related: [Point-in-Time and Survivorship-Free Data](https://learn.tradelabsai.com/programming/point-in-time-data/): Point in time data records what was known on each date, including restated figures and index changes. Learn why it matters and how to build point in time datasets.
- Related: [Look-Ahead Bias](https://learn.tradelabsai.com/research/look-ahead-bias/): Look ahead bias happens when a backtest uses information that was not available at the time. Learn common sources, real examples and how to prevent it in code.
- Related: [Factor Investing Explained](https://learn.tradelabsai.com/research/factor-investing-explained/): Factor investing targets traits linked to long run returns, such as value, momentum and quality. Learn the main factors, the evidence and how they are traded.
- Related: [Guidance and Earnings Revisions](https://learn.tradelabsai.com/fundamentals/guidance-and-earnings-revisions/): Company guidance and analyst estimate revisions shape expectations. Learn how guidance works, why revisions predict returns and how traders track them.