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

Do Company Insiders Predict Their Own Stock's Returns? Form 4 Cross-Section in Python
How Many Independent Bets Are There in the S&P 500? Principal Component Analysis in Python
Can a Company's Revenue Be Forecast From Its Own History? Out-of-Sample Test in Python
What Is EBITDA and Why Do Sources Disagree?
Does the Turn-of-the-Month Effect Still Work? Calendar Anomaly Test in Python
Is the S&P 500 Getting More Capital Intensive? Capex Analysis in Python
Do High-Accrual Companies Underperform? Accruals Screening in Python
What Does a Financial Data API Cost?
Do Price Gaps Get Filled? Gap-Fill Rates Against a Random Walk in Python
What Expected Returns Does the S&P 500 Imply? Reverse Optimization in Python
Which Sectors Are Really Cyclical? Revenue Betas vs Stock Betas in Python
How to Build a Stock Dataset for Machine Learning
Comparing Companies With Different Fiscal Year Ends
How Much Has Corporate Debt Actually Repriced? Effective Interest Rates in Python
Does Deferred Revenue Predict Next Quarter's Sales? Leading Indicator Test in Python
How Much Debt Is Hidden in Operating Leases? Lease-Adjusted Leverage in Python
How Much Do Profits Move When Sales Move? Operating Leverage Regression in Python
How Concentrated Is the S&P 500? Index Weight Analysis in Python
How Long Is Cash Tied Up in a Business? Cash Conversion Cycle Analysis in Python
Broker API vs Data API for Historical Stock Data
Where to Get Free Cash Flow Data for Stocks in Python
Are Stock Returns Skewed? Return Skewness in Python
Do High-Margin Companies Trade at Higher Multiples? EV/Sales in Python
How Much Profit Becomes Cash? Free Cash Flow Conversion in Python
Which Sectors Lead Out of a Market Bottom? Sector Recovery Analysis in Python
Are Buybacks Funded by Cash Flow or by Debt? S&P 500 Payout Analysis in Python
What to Look for in Fundamentals Data
Does Cash on the Balance Sheet Cushion a Crash? Quintile Sorts in Python
Do Corporate Insiders Time the Market? S&P 500 Insider Buying Breadth in Python
Does Unstable Volatility Warn of Deeper Drawdowns? Vol-of-Vol Sorts in Python
Does Fast Asset Growth Predict Weak Stock Returns? Decile Sorts in Python
How to Replace yfinance in a Python Script
Does the Nasdaq-100 Index Effect Still Exist? Event Study in Python
Does the Piotroski F-Score Still Work? Quality Screening in Python
How Many S&P 500 Stocks Beat the Index? Return Breadth Analysis in Python
Why Beta Differs Between Data Sources
Where to Get Historical Dividend Data for Stocks
Does a High Dividend Yield Predict a Dividend Cut? Yield-Trap Screening in Python
Do Old Ticker Symbols Still Point to the Same Company? S&P 500 Ticker Recycling in Python
What Growth Is Priced Into the S&P 500? Reverse DCF in Python
Ticker vs CIK vs FIGI: Which Company ID to Use
Does Illiquidity Still Pay? Amihud Measure in the S&P 500 in Python
Where to Get R&D Spending Data for Public Companies
Does R&D Spending Predict Revenue Growth? Cross-Sectional Test in Python
What Actually Drives Return on Equity? DuPont Decomposition in Python
What Data Do You Need to Measure Portfolio Risk?
Does a 60/40 Portfolio Actually Cut Drawdowns? Stocks and Bonds in Python
Do Steady Margins Mean Calmer Stocks? Cross-Sectional Analysis in Python
Do Sectors Diversify When It Matters? Conditional Correlation in Python
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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What Is EBITDA and Why Do Sources Disagree?

EBITDA stands for earnings before interest, taxes, depreciation and amortisation, and no accounting standard defines it. US GAAP does not specify it, IFRS does not specify it, and the SEC treats it as a non-GAAP measure that a company may publish only alongside a reconciliation to the nearest GAAP line. Every source that reports an EBITDA figure has therefore made a private decision about which items to add back, and two sources that decided differently will print different numbers for the same company and the same year. Uber’s fiscal 2025 shows the range. Built from operating income, the figure is $6,284 million. Built from net income, it is $6,866 million. The Adjusted EBITDA Uber itself reported for that year is $8,730 million. All three trace to the same audited statements, so the useful question about any EBITDA number is not whether it is correct but which build produced it.

What is EBITDA supposed to measure?

The intent is a rough proxy for the cash an operating business throws off before the effects of how it is financed, where it is taxed, and how much of its asset base was paid for in earlier years. Two competitors in one industry can report very different net income because one carries debt and the other does not, or because one bought a rival and now amortises the intangibles it recognised in the deal. Stripping out interest, tax and the non-cash charges attached to past capital spending is an attempt to put the underlying operations on comparable footing.

Two standard routes lead to it, and they begin at opposite ends of the income statement. The top-down route starts at operating income and adds back depreciation and amortisation. The bottom-up route starts at net income and adds back interest expense, income tax expense, and depreciation and amortisation. Those two routes agree only when nothing sits between operating income and pretax income except interest expense. For any company that holds investments, runs a pension, or accounts for a joint venture under the equity method, something almost always does.

Why do two sources report different EBITDA for the same company?

Four decisions sit behind every published figure, and none of them is usually disclosed.

Decision Common choices What it changes
Starting line operating income, or net income Starting at net income pulls every non-operating item into the figure: interest income, investment revaluations, equity-method results, gains on disposals.
Which depreciation number the income statement line, or the cash flow statement add-back The two are not always the same number. The cash flow add-back can sweep in amortisation of acquired intangibles, capitalised software and depletion that the income statement presents inside cost of revenue.
Add-backs past the acronym stock-based compensation, restructuring, impairments, legal and regulatory reserves, one-off gains This is where the spread comes from. Each add-back raises the figure, and for a software or platform company stock-based compensation alone can move it by a third.
Period fiscal year, trailing twelve months, calendar year A trailing figure and a fiscal-year figure for a growing company differ by roughly a quarter of growth, on the same definition.

The third row does most of the damage. Interest, taxes, depreciation and amortisation are named in the acronym and nobody argues about them; everything after that is editorial, and the editorial part is what separates a computed EBITDA from the number in a company’s earnings release. Our note on annual versus quarterly data covers the period question in more detail.

How much does the choice change the number?

Uber’s fiscal year ended 31 December 2025. Both textbook builds, from the filed statement lines:

import xfinlink as xfl

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

fields = ["operating_income", "depreciation_amortization", "net_income",
          "interest_expense", "income_tax_expense", "stock_based_compensation", "ebitda"]

df = xfl.fundamentals("UBER", start="2025-01-01", end="2025-12-31",
                      period_type="annual", fields=fields)
r = df.iloc[-1]

from_operating = r.operating_income + r.depreciation_amortization
from_net_income = (r.net_income + r.interest_expense
                   + r.income_tax_expense + r.depreciation_amortization)
period_end                     2025-12-31
operating income                   5,565
depreciation and amortisation        719
net income                        10,053
interest expense                     440
income tax expense                -4,346
stock-based compensation           1,826

EBITDA from operating income       6,284
EBITDA from net income             6,866
served ebitda field                6,284
difference between the builds        582

Figures in millions of US dollars, and each of them matches the company’s filed XBRL facts for the year.

Two features of that output deserve attention. The income tax line is negative, because Uber recorded a tax benefit rather than a tax charge in 2025, so the bottom-up build subtracts $4,346 million on its way up the statement instead of adding it. And the $582 million gap between the two builds is not an error in either one: it is the net of interest income, other non-operating items, equity-method losses and the share of profit belonging to non-controlling interests. Those items fall below operating income and above net income, so the bottom-up route carries them and the top-down route does not. A company with a large investment portfolio produces a much wider gap than this one.

What does "adjusted EBITDA" add?

Uber reported Adjusted EBITDA of $8,730 million for fiscal 2025, and its fourth-quarter earnings release sets out the bridge. Starting from income from operations of $5,565 million, it adds depreciation and amortisation of $719 million, stock-based compensation of $1,826 million, legal, non-income tax and regulatory reserves of $564 million, acquisition and financing expenses of $43 million, restructuring charges of $9 million, goodwill and asset impairments of $2 million, and a $2 million loss on a lease arrangement.

So the adjusted figure sits $2,446 million above the top-down build, and stock-based compensation is three quarters of that difference. Nothing here is hidden. The reconciliation is published, Regulation G requires it, and credit agreements commonly define their own EBITDA in similar terms because a lender wants a covenant test that ignores charges the borrower cannot pay cash on. The figure becomes unreadable only when a third party republishes the total on its own, stripped of the bridge that explains it.

Where does a stored EBITDA field come from?

Some APIs serve EBITDA as a stored field. A call to Alpha Vantage’s OVERVIEW endpoint for IBM on 29 August 2026 returned "EBITDA": "16473000000" and "EVToEBITDA": "15.58", two of 55 fields in the response, with no statement of the starting line, the add-backs, or the period the figure covers (alphavantage.co/documentation, as of August 2026). For sorting a watchlist into rough buckets, that is enough, and it saves a download.

It stops being enough as soon as the number is compared across companies. A stored field applies one vendor’s editorial choices to every filer, and those choices interact with how each company tags its statements, so a cross-sectional screen inherits a definition nobody stated and cannot audit. The same argument applies to any single published statistic whose construction is not disclosed, which is the pattern behind why beta differs between sources.

Which build should you use?

Pick one and hold it fixed across every company in the sample, because consistency matters more than which variant you chose. For most equity work the top-down build, operating income plus depreciation and amortisation, is the sensible default: it stays inside the operating business and does not import investment gains that have nothing to do with trading performance. Add stock-based compensation back only if the analysis is a credit or cash-coverage question, since shares issued to employees are a real cost to existing shareholders even though no cash leaves the company. For anything sensitive to cash timing, use free cash flow rather than an earnings proxy.

A definition you control needs the components served separately. xfinlink returns operating_income, depreciation_amortization, net_income, interest_expense, income_tax_expense, stock_based_compensation, restructuring_charges and impairment_charges as distinct columns on the same annual row, alongside an ebitda column that for Uber’s fiscal 2025 equals operating income plus depreciation and amortisation. Every field name and the parameters fundamentals() accepts are in the docs, and plans start at a free tier.

FAQ

Is EBITDA the same as operating cash flow?
No. Operating cash flow reflects movements in working capital, cash taxes actually paid, and cash interest, none of which EBITDA sees. A company can grow EBITDA while operating cash flow falls, which usually means receivables or inventory are absorbing the difference.

Should stock-based compensation be added back?
For equity valuation, no. Shares issued to employees dilute existing holders, so the cost is real even though it never appears as a cash outflow. For a credit analysis that asks whether a borrower can service debt out of cash, adding it back is defensible.

Why does EBITDA computed from net income exceed EBITDA computed from operating income?
Because non-operating income sits between the two lines. Interest income, gains on investments and equity-method results are inside the bottom-up figure and outside the top-down one, and for Uber in fiscal 2025 that is $582 million.

Does EBITDA appear in a 10-K?
Not as a GAAP line. Companies present it as a non-GAAP measure with a reconciliation to net income or income from operations, which is the table to read before using any figure a company publishes about itself.

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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