Year-to-date relative performance for four of the largest US tech stocks: Google (GOOG), Nvidia (NVDA), Facebook (META) and Microsoft (MSFT). Each line is indexed to 100 on the first trading day of the year, so the chart shows what 100 dollars invested on Jan 2 would be worth today - assuming you'd reinvested dividends, ignored taxes and could trade at the close.<\/p>

Why these four<\/h2>
These four companies sit at the center of the current AI capex story. NVDA sells the GPUs and networking gear (and the surrounding CUDA software stack) that the rest of the industry is building data centers around. GOOG, META and MSFT are three of NVDA's largest customers and three of the loudest voices in committing to AI infrastructure spend.<\/p>
This chart gives a quick read on how the market is pricing the spenders<\/em> relative to the seller<\/em>.<\/p>

Methodology<\/h2>

Source<\/strong> — daily close prices from Yahoo Finance, pulled via the yfinance<\/a> python library.<\/li>
Adjustments<\/strong> — `auto_adjust=True<\/code>, so the series are corrected for splits and dividends. Closer to a total return line than a raw price line.<\/li>`
`Window<\/strong> — first trading day of the current calendar year through the most recent close, with the axis stretched a few days past the next known earnings date so upcoming events are visible.<\/li>`
`Refresh<\/strong> — a small novem job runs weekdays at 21:30 UTC (≈30 min after US close), fetches the data and posts it back to the plot data endpoint. So the chart is always current within one trading day.<\/li>`
**`Normalisation<\/strong> — series \/ series.iloc[0] * 100<\/code>. Every line starts at exactly 100 on day one.<\/li>`**
`Missing days<\/strong> — forward-filled for US holidays and half-day closes so the dates line up across all four series.<\/li> <\/ul>`Have a look at the yfinance docs<\/a> if you want to reproduce the data pull yourself — it's a couple of lines of pandas.<\/p> Earnings markers<\/h2> The vertical stippled lines mark days when one of the four reported quarterly results. The ticker labels at the top of each line tell you who. Most of the visible kinks in the price series sit on, or right after, one of these dates.<\/p> YTD 2026 so far:<\/p> Jan 28<\/strong> - META and MSFT reported (Q4 2025 \/ fiscal Q2 2026) after the close<\/li> Feb 4<\/strong> - GOOG reported Q4 2025 after the close<\/li> Feb 25<\/strong> - NVDA reported fiscal Q4 2026 after the close<\/li> Apr 29<\/strong> - GOOG, META and MSFT all reported on the same evening (Q1 2026 \/ fiscal Q3 2026)<\/li> <\/ul> Coming up:<\/p> May 20<\/strong> - NVDA reports fiscal Q1 2027 after the close. The marker is already on the chart so you can see it approaching from the right. It's the next big print to watch given how much of the AI capex thesis still routes through NVDA's income statement.<\/li> <\/ul> NVDA runs on a fiscal year ending in late January, so its reporting cadence is offset from the other three — Q1 lands in May rather than April, Q2 in August, etc. The calendar-quarter trio (GOOG, META, MSFT) tend to bunch up on the same evenings, which is why Jan 28 and Apr 29 both have multi-ticker markers.<\/p> Sources<\/h2> Earnings dates verified against company investor relations and press coverage:<\/p> Alphabet — Q4 2025 (Feb 4)<\/a> · Q1 2026 (Apr 29)<\/a><\/li> Nvidia — Fiscal Q4 2026 (Feb 25)<\/a> · Fiscal Q1 2027 (May 20)<\/a><\/li> Meta — Q4 2025 (Jan 28)<\/a> · Q1 2026 (Apr 29)<\/a><\/li> Microsoft — Fiscal Q2 2026 (Jan 28)<\/a> · Fiscal Q3 2026 (Apr 29)<\/a><\/li> <\/ul>