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Behind the numbers.

Code examples, market analysis, and data quality deep-dives.

How to Get Historical Market Cap Data in Python
How Much of the S&P 500 Survives 20 Years? Index Turnover Analysis in Python
Does Joining the S&P 500 Bring New Institutional Owners? 13F Event Study in Python
Is Volatility Seasonal? Calendar Month Analysis of Realized Volatility in Python
Does Fast Revenue Growth Force Companies to Borrow? Cash Funding Analysis in Python
How Far Back Does SEC EDGAR Data Go?
Are One-Time Charges Really One-Time? Charge Frequency Analysis in Python
Does Buying the Dip Work? Short-Term Reversal by Volatility Regime in Python
Alpha Vantage vs Massive vs xfinlink for Fundamentals
How Long Does a Stock Take to Recover From a 50% Fall? Drawdown Analysis in Python
Do Companies That Shrink Their Share Count Outperform? Net Buyback Yield in Python
How Much Does the Dow's Price Weighting Distort It? Index Weighting Analysis in Python
How to Get SEC Form 4 Insider Trading Data in Python
Can Anything Predict Next Month's Stock Returns? Out-of-Sample R-Squared Testing in Python
How Much of a Stock's Return Comes From Its Sector? Variance Decomposition in Python
Altman Z-Score: Where To Get It in Python
Do Value Screens Agree on Which Stocks Are Cheap? Multiple Overlap Analysis in Python
Annual vs Quarterly Financial Data: Which to Use
Do High Returns on Capital Persist? ROIC Fade Analysis in Python
Does Past Beta Predict Future Beta? Beta Stability Testing in Python
Do Defensive Sectors Actually Defend? Up and Down Capture in Python
How to Choose a Financial Data API
Do Small Caps Actually Beat Large Caps? Size Premium Test in Python
What If You Miss the Market's Best Days? Extreme-Day Analysis in Python
Does Rebalancing Add Return? Fixed-Weight vs Drift Portfolios in Python
Which S&P 500 Companies Are Closest to Default? Merton Distance-to-Default in Python
What Happens to Stocks Removed From the S&P 500? Replacement Pair Analysis in Python
Does the Golden Cross Work? 50/200 Moving Average Crossover Backtest in Python
Financial Data for Academic Finance Research
Does Skipping the Most Recent Month Improve Momentum? S&P 500 Decile Sorts in Python
Does Cointegration Survive Out of Sample? Pairs Trading Validation in Python
Which Dividends Are Not Covered by Cash? Free-Cash-Flow Coverage Screening in Python
GICS vs SIC vs NAICS: Which Industry Classification to Use
How Many Stocks Does It Take to Diversify? Random Portfolio Simulation in Python
How Many Days of Data Does a Volatility Estimate Need? Range-Based Estimators in Python
How Much of S&P 500 Cash Flow Is Stock Compensation? Cross-Sectional Analysis in Python
What Is a 13F Filing? Institutional Holdings Explained
Does Revenue Growth Explain Profit Growth? Cross-Sectional Decomposition in Python
How Much of the Nasdaq 100 Is Already in the S&P 500? Index Overlap Analysis in Python
How Often Does a 99% Value-at-Risk Limit Actually Break? VaR Backtesting in Python
Real-Time vs End-of-Day Market Data: Which Do You Need?
How Concentrated Are S&P 500 Earnings? Point-in-Time Index Analysis in Python
Does Volatility Scale With the Square Root of Time? Variance Ratio Test in Python
Does Goodwill Distort the Price-to-Book Screen? Goodwill-Adjusted Valuation in Python
How Are Shares Outstanding Reported (and Why They Disagree)
Do Low-Volatility Stocks Deliver Better Risk-Adjusted Returns? S&P 500 Quintile Sorts in Python
Does Trend Following Beat Buy and Hold? Time-Series Momentum in Python
Has the Stock-Bond Correlation Flipped? 60/40 Portfolio Risk in Python
What API to Use for a Stock Screener
Which Assets Hedge Inflation Shocks? Macro Factor Betas in Python
Does Covariance Shrinkage Beat the Sample Covariance? Minimum-Variance Portfolios in Python
Do Faster Inventory Turns Mean Thinner Margins? Gross Margin Return on Inventory in Python
SEC EDGAR API vs Fundamentals API: Which to Use
Does Fast Earnings Growth Persist? Rank Correlation Analysis in Python
Split Adjustment Explained: Adjusted Close vs Close
Which Trading Day of the Month Pays Best? Turn-of-the-Month Analysis in Python
Can You Use Yahoo Finance Data Commercially?
How Many Independent Bets Does a Nine-Sector Portfolio Give You? Eigenvalue Analysis in Python
Which Volatility Forecast Wins One Month Ahead? HAR vs EWMA in Python
How Concentrated Are Institutional Equity Portfolios? Form 13F Concentration Analysis in Python
Do Stocks Earn Their Returns Overnight or Intraday? Return Decomposition in Python
When Do Corporate Insiders Actually Trade? Form 4 Timing Analysis in Python
Data Requirements for Backtesting a Trading Strategy
What Is Survivorship Bias in Backtesting?
Do High Dividend Yields Come From Bigger Payouts or Falling Prices? Yield Decomposition in Python
Do Stocks Fall Harder Than They Rise? Downside Beta vs Upside Beta in Python
Does Volatility Targeting Improve Sharpe Ratios? Seven-Asset Backtest in Python
Free Stock Market Data APIs: What You Actually Get
How to Give an LLM Financial Data With an MCP Server
Does Post-Earnings Announcement Drift Survive Real Filing Dates? PEAD Event Study in Python
Do Insider Buying Clusters Predict Returns? Signal Testing in Python
Does the S&P 500 Index Effect Still Exist? Event Study in Python
Are Companies Leaving the S&P 500 Faster Than They Used To? Index Survival Analysis in Python
Does Gross Profitability Predict Stock Returns? Quintile Factor Test in Python
What Growth Rate Is the Market Pricing In? Reverse DCF in Python
Does a Strong Balance Sheet Cushion Drawdowns? Leverage and Downside Risk in Python
Does Ticker Recycling Corrupt a Mean-Reversion Backtest? Entity-Resolved Z-Scores in Python
How Much of a Growth Screen's Backtested Edge Is Survivorship Bias? Point-in-Time Index Testing in Python
Why Do Leveraged ETFs Decay? Measuring Volatility Drag in Python
Are Consumer Staples Margins Shrinking Under Inflation? Gross Margin Trend Analysis in Python
Do Weak Jobs Reports Predict Market Drawdowns? NFP Surprise Event Study in Python
Is the Rotation From Tech to Industrials Backed by Earnings? Relative EPS Growth Analysis in Python
Is the Semiconductor Rally Broadening Beyond NVIDIA? Return Dispersion Analysis in Python
Which Stocks Benefit Most When Oil Prices Fall? Oil Beta Screening in Python
Do Bond Returns Predict Stock Returns? Granger Causality Test in Python
Which Stocks Actually Drive Portfolio Returns? Shapley Value Attribution in Python
Does "Sell in May" Still Work? Calendar Anomaly Backtest in Python
How to Build Complete Price History Through Ticker Changes? Entity Resolution in Python
Are KO and PEP Cointegrated? Pairs Trading Signal Construction in Python
Which Commodities Have the Strongest Momentum? Rotation Backtest in Python
Which Commodity ETFs Have the Worst Tail Risk? Expected Shortfall in Python
Are Gold Miners Leveraged Gold Bets? Rolling Beta Analysis in Python
Does the Base-Metals-to-Gold Ratio Lead Cyclical Stocks? Signal Test in Python
Can Risk Parity Tame Commodity Volatility? Portfolio Optimization in Python
Are Power Stocks Becoming an AI Infrastructure Trade? Momentum Screening in Python
Which AI Chip Stocks Have Margin Momentum? Profitability Trend Analysis in Python
Which AI Stocks Are Cheapest Relative to Growth? Growth-Adjusted Valuation in Python
Does AI Stock Leadership Persist? Momentum Backtest in Python
Which AI Stocks Have the Cleanest Balance Sheets? Net Cash Screening in Python
Can Risk Parity Reduce Mega-Cap Drawdowns? Portfolio Optimization in Python
Which Growth Stocks Are Self-Funding? Cash-Flow Quality Screening in Python
Which Sectors Struggle When the Dollar Rallies? Sector Rotation Analysis in Python
Do Cheap Stocks Hold Up When Bonds Sell Off? Valuation Rotation in Python
Does the Nasdaq 100 Have Better Growth Quality Than the Dow? Index Constituent Analysis in Python
Do Healthcare Cash-Flow Margins Predict Returns? Signal Evaluation in Python
Which Dividend Stocks Survive a Cash-Flow Stress Test? Dividend Screening in Python
Does Heavy Insider Selling Predict Weak Returns? Insider Flow Test in Python
Can Quality Screens Reduce Small-Cap Balance-Sheet Risk? Russell 2000 Test in Python
Which Retailers Have Positive Operating Leverage? Margin Screening in Python
Is MSTR a Leveraged Bitcoin Proxy? Rolling Beta Analysis in Python
Is Micron's Memory Cycle Recovering? Inventory and Margin Forecasting in Python
Which Sectors Work When Bonds Rally? Rate-Sensitive Rotation in Python
Do One-Month Price Extremes Reverse? Signal Evaluation in Python
Do Low-Volatility S&P 500 Stocks Reduce Drawdowns? Factor Test in Python
Is AI Capex Paying Back Fast Enough? Revenue Hurdle Forecasting in Python
Could Shorter AI Asset Lives Hit Earnings? Depreciation Stress Test in Python
How Much AI Capex Risk Can a Portfolio Remove? Constrained Optimization in Python
Is the AI Capex Trade Crowded? Rolling Volatility and Sector Rotation in Python
Did the AI Boom Come From Existing S&P 500 Members? Point-in-Time Momentum Test in Python
Is AI Revenue Circular? Customer-Vendor Capex Loop Analysis in Python
Is the AI Trade Connected to Private Credit? Rolling Correlation Network in Python
Is Apollo More Balance-Sheet Sensitive Than Peers? Leverage Screen in Python
Are AI Earnings Supported by Cash Flow? Accrual and Capex Screen in Python
Can Defensive Stocks Hedge AI Drawdowns? Basket Regime Test in Python
How Fast Does the Market Price In Fed Decisions? FOMC Event Study in Python
How Much Are Options Sellers Overpaid? The Variance Risk Premium in Python
Which Companies Have the Worst Earnings Quality? Sloan Accrual Screen with Geographic Revenue Data in Python
Does the Oil-to-Gold Ratio Signal Recessions? XLE/GLD Backtest in Python
Is AI Spending Crowding Out Free Cash Flow? Capex Sustainability Across the Mag 7 in Python
Does a Long Energy / Short Bonds Portfolio Capture Inflation Surprises? Factor Construction in Python
Can a Hidden Markov Model Detect Oil Market Regimes? HMM Analysis in Python
Do Grain Prices Predict Food Inflation? Granger Causality Test in Python
Does the Corporate Credit Spread Predict Stock Market Crashes? BAA-AAA Spread Analysis in Python
Do Oil Stocks Hedge Inflation? Rolling Beta Analysis in Python
Which Stocks Are Most Rate-Sensitive? Equity Duration via Bond Beta in Python
Which Companies Have the Highest Accrual Ratios? Earnings Quality Screening in Python
Is Alpha Persistent or Decaying? Rolling Sharpe Ratio Analysis in Python
Are Markets Trending or Mean-Reverting? Hurst Exponent Analysis in Python
Is Consumer Discretionary vs Staples a Leading Indicator? XLY/XLP Ratio Analysis in Python
Does Heavy Capex Predict Future Stock Returns? Capital Expenditure Analysis in Python
How to Estimate Cost of Equity Using CAPM in Python
Is Volatility Predictable? Testing for Volatility Clustering in Python
Which Industrials Are Overleveraged? Net Debt to EBITDA Screening in Python
GM Before and After Bankruptcy: Why Entity Resolution Matters for Financial Data
What Is Adjusted Beta? Merrill Lynch Beta Shrinkage in Python
How Good Is a Stock Pick? Information Ratio and Tracking Error in Python
Do Stock Returns Follow a Normal Distribution? Testing for Fat Tails in Python
Which Large Caps Have the Highest Free Cash Flow Yield? FCF Screening in Python
Which Sectors Won Over 5 Years? Sector Rotation Analysis in Python
How to Forecast Stock Volatility with GARCH Models in Python
Are Stock Prices Mean-Reverting? Augmented Dickey-Fuller Test in Python
How to Calculate CAPM Alpha and Beta with Regression in Python
How to Compare Sector Sharpe Ratios and Sortino Ratios in Python
DELL: Why Stitching Historical Price Data Together Is Wrong
How to Analyze Drawdown and Recovery for Bank Stocks in Python
How to Screen SaaS Stocks by Revenue Growth and Cash Flow in Python
How to Screen REITs by Dividend Yield and Valuation in Python
How Correlated Are the Magnificent 7? Intra-Group Correlation in Python
AAPL vs XOM: Do Individual Stocks Have Seasonal Patterns?
How to Rank Large-Cap Stocks by Momentum in Python
How to Build a Multi-Endpoint Financial Dashboard in Python
How to Compare Volatility Across Energy Stocks in Python
How to Screen Healthcare Stocks by Valuation in Python
How to Build a Sector Correlation Matrix for Portfolio Diversification in Python
How to Find Oversold and Overbought Stocks Using Z-Scores in Python
How to Measure Earnings Quality: Cash Flow vs Net Income in Python
How to Build a Multi-Factor Stock Screen in Python (Value + Momentum + Quality)
How to Build a Simple DCF Model for Any Stock in Python
How to Screen Tech Stocks by Revenue Growth in Python
How to Screen Stocks by Balance Sheet Health in Python
Is "Sell in May" Real? SPY Monthly Seasonality Over 10 Years
How to Compare Sector Performance YTD Using Python
How to Screen Dividend Stocks by Yield and Quality in Python
How to Calculate Max Drawdown and Recovery Time for Any Stock in Python
How to Compare Profitability Across Mega-Cap Tech Stocks in Python
Why Ticker Symbols Are Unreliable: The Recycling Problem Every Quant Should Know
How to Calculate and Compare Stock Volatility in Python
How to Screen Blue-Chip Stocks by P/E Ratio in Python
How to Track Companies Through Ticker Changes, Bankruptcies, and Renames in Python
S&P 500 Turnover: How Much the Index Has Changed Since 2010
How to Calculate Stock Beta and Correlation in Python
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Annual vs Quarterly Financial Data: Which to Use

Use annual data when the question is about a company’s economics, and quarterly when the question is about timing. Valuation multiples, margin trends, and returns on capital are annual questions: a full year cancels seasonality and matches how companies actually report their audited position. Anything that depends on when information became known, including event studies, earnings-driven signals, and backtests with a rebalancing date, needs quarterly data because annual figures arrive once and sit stale for twelve months. Most people reach for quarterly by default because it looks like more information. It is more observations of a noisier series, which is a different thing.

What is the actual difference?

An annual figure covers a fiscal year and comes from a 10-K. A quarterly figure covers roughly thirteen weeks and comes from a 10-Q, except the fourth quarter, which most companies do not file separately and which has to be derived by subtracting the first three quarters from the annual total.

That derivation is the first practical difference. If a provider does not compute the fourth quarter, the series has a hole every fourth period. If it computes it by subtraction, any adjustment the company booked at year end lands entirely in that quarter.

The second difference is the audit. Annual statements are audited; quarterly statements are reviewed, which is a lighter standard. Figures that move between the last quarterly filing and the annual one are common and are not errors.

When is annual data enough?

For most cross-sectional work, annual data is not a compromise but the correct choice.

Ratios built on a full year are comparable across companies with different seasonal shapes. A retailer earning most of its profit in the December quarter and a software company earning evenly across the year cannot be ranked on a single quarter’s margin without the ranking being mostly a statement about the calendar.

Long-horizon studies also fit annual data naturally. Our analysis of whether high returns on capital persist tracks quintiles of the S&P 500 over nine years, and quarterly figures would have added noise without changing a single conclusion, because the question is about competitive position rather than about any particular quarter.

import xfinlink as xfl

xfl.set_api_key("YOUR_API_KEY")  # free at https://xfinlink.com/signup

annual = xfl.fundamentals("AAPL", period_type="annual",
                          start="2024-01-01", end="2025-12-31",
                          fields=["revenue"])

When do you need quarterly?

Quarterly data earns its place when the answer depends on a date.

Backtests are the clearest case. A strategy that rebalances in March using annual figures needs to know what was actually filed by March, and an annual series carries no filing date to check against. Reading a fiscal-year figure into a date before the company published it is look-ahead bias, and it inflates results in a way that survives every other sanity check. The related trap of dropping companies that disappeared is covered in our guide to survivorship bias in backtesting.

Quarterly data is also the only way to see the shape of a year. Apple’s fiscal 2025 is a single revenue number in annual form; in quarterly form the December quarter is 30% of it.

Apple, quarterly revenue ($m), fiscal year ends late September
  FY2024 Q2  ends 2024-03-30  revenue   90,753  net income  23,636
  FY2024 Q3  ends 2024-06-29  revenue   85,777  net income  21,448
  FY2024 Q4  ends 2024-09-28  revenue   94,930  net income  14,736
  FY2025 Q1  ends 2024-12-28  revenue  124,300  net income  36,330
  FY2025 Q2  ends 2025-03-29  revenue   95,359  net income  24,780
  FY2025 Q3  ends 2025-06-28  revenue   94,036  net income  23,434
  FY2025 Q4  ends 2025-09-27  revenue  102,466  net income  27,466
  FY2026 Q1  ends 2025-12-27  revenue  143,756  net income  42,097

The September 2024 quarter shows why the distinction matters. Revenue of 94,930 sits in line with the quarters around it while net income drops to 14,736, a third below the quarter before it, because of a one-off charge. An annual figure absorbs that into a twelve-month total and a reader would never see it.

Why do fiscal years not line up?

A fiscal year is whatever the company says it is, and comparing “2025” across companies is comparing different periods.

  AAPL  FY2025 ends 27 September 2025
  MSFT  FY2025 ends 30 June 2025
  WMT   FY2025 ends 31 January 2025
  NVDA  FY2025 ends 26 January 2025
  COST  FY2025 ends 31 August 2025

Walmart’s fiscal 2025 ended nearly eight months before Apple’s did. Any screen that groups on a fiscal-year label is silently mixing periods up to eight months apart, which matters a great deal in a year when conditions changed, such as 2020 or 2022.

Two habits fix this. Group on the period end date rather than the fiscal-year label when the comparison is macroeconomic, and check that fiscal year ends are exposed by whatever source you use before assuming a shared calendar. xfinlink returns fiscal_year, fiscal_period, period_end and filing_date on every fundamentals row, so the alignment question can be answered without a second lookup.

How do you build a trailing twelve month figure?

Trailing twelve months combines the two: current as of the last filing, and a full year long, so seasonality cancels. Sum the four most recent quarters.

q = xfl.fundamentals("AAPL", period_type="quarterly",
                     start="2023-01-01", end="2025-12-31",
                     fields=["revenue", "net_income"])
ttm = q.sort_values("period_end").tail(4)["revenue"].sum()
  trailing four quarters: 435,617
  most recent annual:     416,161

The gap between the two figures, 4.7%, is one quarter of growth that the annual number has not caught up to. Any multiple computed on revenue is therefore 4.7% higher on the annual figure than on the trailing twelve months, for the same company on the same day.

Annual Quarterly Trailing twelve months
Period covered Fiscal year About 13 weeks Last four quarters
Updates Once a year Four times a year Four times a year
Seasonality Cancelled Present Cancelled
Audited Yes Reviewed only Mixed
Best for Ratios, long-horizon studies Event studies, backtest timing Current valuation multiples

Frequently asked questions

Can annual figures be rebuilt from quarterly ones? For flow items such as revenue, net income and cash flow, yes: four quarters sum to the year. For balance-sheet items such as debt, cash and total assets, no, because each is a snapshot at a single date rather than an amount accumulated over the period. Take the year-end value instead of summing. The field reference marks which is which.

Should quarterly figures be annualised by multiplying by four? Only for a company with no seasonality, which is rare. Multiplying Apple’s December quarter by four overstates its annual revenue by about 20%. Use a trailing twelve month sum instead.

Why does a quarterly figure disagree with the same quarter shown in the annual report? Quarterly statements are reviewed rather than audited, and year-end adjustments are booked against the fourth quarter. A restated comparative in a later filing is the normal reason the two disagree, not an error in either.

Which should a screener use? Trailing twelve months for anything divided by price, and annual for balance-sheet quality measures, which change slowly and are cleanest at year end. Related reading on a common reporting trap: how shares outstanding are reported.

Both period types come from the same SEC filings and the same endpoint in xfinlink, switched with one parameter, and the pricing page sets out how much history each plan covers. The decision is about the question being asked, not about what a data source makes convenient.

Built with xfinlink — free financial data API for Python. pip install -U xfinlink

Built with xfinlink — free financial data API for Python. pip install -U xfinlink
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