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Compare · GOOG vs MSFT · 2026

Google vs Microsoft

A year of returns, risk, and volatility, compared.

Google (GOOG) and Microsoft (MSFT) are compared across trailing return, volatility, drawdown, and risk-adjusted metrics.

Gale Finance Team
Written by Gale Finance Team
Sid Kalla
Reviewed by Sid Kalla CFA Charterholder
Quick answer

Which is a better investment: GOOG or MSFT?

Over the past year, GOOG outperformed MSFT. GOOG returned +106.7% compared with MSFT’s +6.7%. GOOG had the better risk-adjusted return, with a Sharpe ratio of 2.63 versus MSFT’s 0.22. MSFT was less volatile than GOOG, but GOOG had a smaller max drawdown than MSFT.

Total Return
GOOG +106.7%
MSFT +6.7%
Sharpe Ratio
GOOG 2.63
MSFT 0.22
Annualized Volatility
GOOG 27.8%
MSFT 24.5%
Max Drawdown
GOOG -20.8%
MSFT -34.1%

Metric winners: Total Return: GOOG; Sharpe Ratio: GOOG; Annualized Volatility: MSFT (less volatile); Max Drawdown: GOOG (smaller drawdown).

GOOG Total Return
+106.7%
MSFT Total Return
+6.7%

Relative Performance of GOOG vs MSFT (Normalized to 100)

GOOG MSFT

Normalized to 100 at start date for comparison

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Key Takeaways

  • Total Return: GOOG delivered a +106.7% total return, while MSFT returned +6.7% over the same period. GOOG outperformed on total returns.
  • Risk-Adjusted Return (Sharpe Ratio): GOOG had a higher Sharpe (2.63 vs 0.22), indicating better risk-adjusted performance.
  • Volatility (Annualized): GOOG was more volatile, with 27.8% annualized volatility, versus 24.5% for MSFT.
  • Maximum Drawdown: GOOG's maximum drawdown was -20.8%, while MSFT experienced a deeper drawdown of -34.1%.
  • Tail Risk (VaR & Expected Shortfall): At the 5% level (daily log returns), GOOG's VaR was -2.30% and its Expected Shortfall (CVaR) was -3.14%; MSFT's were -2.43% and -3.62%. VaR is the cutoff; Expected Shortfall is the average move on the worst days.
  • Skew & Kurtosis: Skew: GOOG 0.30 vs MSFT -0.92. Excess kurtosis: GOOG 3.29 vs MSFT 10.01. Negative skew leans downside; higher excess kurtosis means fatter tails.
  • Tail Days & Extremes: 2σ tail days (down/up): GOOG 4/5, MSFT 4/5. Worst day: GOOG -7.51% (2025-05-07) vs MSFT -9.99% (2026-01-29). Best day: GOOG +9.01% (2025-09-03) vs MSFT +7.63% (2025-05-01).
  • Risk ratios: Sortino - GOOG: 4.54 vs. MSFT: 0.30 , Calmar - GOOG: 5.17 vs. MSFT: 0.20 , Sterling - GOOG: 4.97 vs. MSFT: 0.07 , Treynor - GOOG: 0.63 vs. MSFT: 0.06 , Ulcer Index - GOOG: 5.66% vs. MSFT: 14.27%

Investment Comparison

If you invested $10,000 in each asset on April 25, 2025:

GOOG $20,670.04 +106.7%
MSFT $10,667.11 +6.7%

Difference: $10,002.93 (GOOG ahead)

Google vs Microsoft Performance Over Time

Metric GOOG MSFT
30 Days 16.8% 11.5%
90 Days 2.8% -10.8%
180 Days 29.7% -20.4%
1 Year 106.7% 6.7%

Shorter time frames can show different leaders as market conditions change. Consider your investment horizon when comparing performance.

Google vs Microsoft Correlation

Average Correlation
weakly correlated
0.15
Current (30-day) 0.58
30-day rolling range -0.17 to +0.65

Google and Microsoft are weakly correlated over the past year. With a correlation of 0.15, these assets show meaningful independence, offering diversification benefits when held together.

For portfolio construction, this weak correlation suggests that combining GOOG and MSFT could reduce overall portfolio variance. However, correlations can increase during market stress.

Metric Value
Current (30-day) 0.58
Average (full period) 0.15
Minimum (30-day rolling) -0.17
Maximum (30-day rolling) 0.65

Correlation measures how closely two assets move together. Values near +1 indicate strong co-movement, near 0 indicates independence, and negative values indicate inverse movement. Current, minimum, and maximum figures are 30-day rolling correlations on shared daily returns.

Drawdown

Maximum Drawdown
GOOG
-20.8%
MSFT
-34.1%

Google experienced its maximum drawdown of -20.8% from 2026-02-02 to 2026-03-30. It has not yet recovered to its previous peak.

Microsoft experienced its maximum drawdown of -34.1% from 2025-10-28 to 2026-03-27. It has not yet recovered to its previous peak.

Smaller drawdowns and faster recoveries indicate lower downside risk and greater resilience during market stress.

Google vs Microsoft Volatility (GOOG vs MSFT)

GOOG Volatility
27.8%
±1.75% 1-day vol
MSFT Volatility
24.5%
±1.54% 1-day vol
1-day volatility (1σ)
GOOG
±1.75%
MSFT
±1.54%

Google's 27.8% annualized volatility translates to about ±1.75% one-standard-deviation daily volatility.

Microsoft's 24.5% annualized volatility translates to about ±1.54% one-standard-deviation daily volatility.

GOOG had the wider volatility profile over this window. That means its day-to-day return distribution was broader; MSFT was calmer, but lower volatility does not by itself mean better returns.

Treat the ± daily figure as a one-standard-deviation estimate from historical returns, not a forecast or expected absolute daily move. For context, 15-18% annualized volatility is roughly ±1% one-standard-deviation daily volatility.

Risk-adjusted ratios

Sharpe Ratio of GOOG and MSFT

Sharpe Ratio: GOOG vs. MSFT

Return per total volatility

Sharpe gives us excess return per unit of risk. Upside and downside volatility both count as risk.

Higher is better
Excess return Annualized volatility 0 50% vol 27.9% · excess +73.3% vol 24.5% · excess +5.4%
excess return / total volatility
Formula Sharpe=E[R]RfσR\displaystyle \mathrm{Sharpe} = \frac{\mathbb{E}[R] - R_f}{\sigma_R}

Sharpe ratio measures return per unit of risk (volatility). A higher Sharpe indicates better risk-adjusted performance. GOOG had a higher Sharpe (2.63 vs 0.22), indicating better risk-adjusted performance.

A Sharpe above 1.0 is generally considered good, above 2.0 is excellent. Negative Sharpe means the asset underperformed the risk-free rate. Calculated on each asset's full 365-day lookback of available prices and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Sortino Ratio of GOOG and MSFT

Sortino Ratio: GOOG vs. MSFT

Return per downside volatility

Sortino keeps the return-over-risk idea, but only returns below the target rate count as volatility.

Higher is better
Frequency (days) Daily return (%) target -10.8% +9.8% 63 0
excess return / downside volatility
Formula Sortino=E[R]Rfσdown\displaystyle \mathrm{Sortino} = \frac{\mathbb{E}[R] - R_f}{\sigma_{\mathrm{down}}}

Sortino ratio measures return per unit of downside risk. Unlike Sharpe, it only counts downside deviation (returns below the target return). GOOG had better downside-adjusted returns.

A higher Sortino is better. It's useful when upside volatility is common (crypto is the obvious example). Downside deviation: GOOG 16.1% vs MSFT 17.8%. Calculated on each asset's full 365-day lookback of available prices, using the daily risk-free rate as the target return, and annualized using the asset calendar (365 for crypto, 252 trading days for equities/ETFs/metals).

Calmar Ratio of GOOG and MSFT

Calmar Ratio: GOOG vs. MSFT

CAGR per worst drawdown

Calmar compares CAGR against the single deepest peak-to-trough loss over the period.

Higher is better
0% GOOG +107.6% -20.8% MSFT +6.7% -34.1%
CAGR / max drawdown
Formula Calmar=CAGRMaxDD\displaystyle \mathrm{Calmar} = \frac{\mathrm{CAGR}}{|\mathrm{MaxDD}|}

Calmar ratio compares CAGR to maximum drawdown. Higher Calmar means more return per unit of worst drawdown. GOOG posted the higher Calmar ratio.

Calmar is computed on each asset's full 365-day lookback and uses the max drawdown over that same window.

Sterling Ratio of GOOG and MSFT

Sterling Ratio: GOOG vs. MSFT

Return per average drawdown

Sterling smooths the drawdown penalty by using average drawdown events instead of only the worst one.

Higher is better
0% -9% -18% -27% -36% 10% drawdown threshold
excess annual return / average deep drawdown
Formula Sterling=CAGRRfD>10%\displaystyle \mathrm{Sterling} = \frac{\mathrm{CAGR} - R_f}{\overline{D}_{>10\%}}

Sterling ratio measures excess return per unit of average drawdown (typically drawdowns worse than 10%). GOOG posted the higher Sterling ratio.

Sterling uses average drawdown events deeper than 10% and subtracts the risk-free rate to report excess return.

Treynor Ratio of GOOG and MSFT

Treynor Ratio: GOOG vs. MSFT

Excess return per market beta

Treynor divides excess annualized return by beta — the sensitivity of the asset to broad-market moves. The slope shown is each asset’s beta vs SPY.

Higher is better
Asset return Market return 0 0 β 1.16 β 0.95
excess return / market beta
Formula Treynor=E[R]Rfβ\displaystyle \mathrm{Treynor} = \frac{\mathbb{E}[R] - R_f}{\beta}

Treynor ratio measures excess return per unit of market risk (beta) instead of total volatility. GOOG posted the higher Treynor ratio.

Treynor uses beta vs the S&P 500 (SPY) on shared dates and the average 3-month Treasury rate as the risk-free rate.

Ulcer Index of GOOG and MSFT

Ulcer Index: GOOG vs. MSFT

Drawdown pain

Ulcer Index is a risk index, not a return-over-risk ratio. Lower means smaller and shorter drawdowns.

Lower is better
0% -9% -18% -27% -36%
root-mean-square drawdown
Formula UI=E[Dt2]\displaystyle \mathrm{UI} = \sqrt{\mathbb{E}[D_t^2]}

Ulcer Index captures drawdown depth and duration. Lower Ulcer Index means less drawdown pain. GOOG had the lower Ulcer Index (less drawdown pain).

Ulcer Index is computed from each asset's drawdown series over the full lookback window.

Tail Risk & Distribution Shape (1-Year): Google vs. Microsoft

This section looks at the shape of daily returns, not just the average. Tail stats are computed per asset on its own daily series (crypto includes weekends). We use daily log returns ln(PtPt1)\ln\left(\frac{P_t}{P_{t-1}}\right) so multi-day moves add cleanly.

Definitions: Value at Risk (VaR), Expected Shortfall, skew, kurtosis, and fat tails.

Tail Risk & Distribution Shape: GOOG vs. MSFT (1-Year)

Actual daily return tails

The bars are real daily log-return observations from the article window. Darker bars are observations at or beyond each asset’s 5% VaR cutoff.

Observed returns
GOOG VaR 5% ES 5% MSFT VaR 5% ES 5% -12.2% 0% +12.2% Daily log return
VaR marks the 5th percentile loss cutoff; Expected Shortfall averages the observations beyond that cutoff.
Formula VaR5%=Q0.05(rt),ES5%=E[rtrtVaR5%]\displaystyle \mathrm{VaR}_{5\%}=Q_{0.05}(r_t),\quad \mathrm{ES}_{5\%}=\mathbb{E}[r_t\mid r_t\le \mathrm{VaR}_{5\%}]
Metric (1-Year) GOOG MSFT
5% VaR (daily log return) -2.30% -2.43%
5% Expected Shortfall (CVaR) -3.14% (worst 13 days) -3.62% (worst 13 days)
Skew 0.30 -0.92
Excess kurtosis 3.29 10.01
2σ tail days (down / up) 4 / 5 4 / 5
Worst day -7.51% (2025-05-07) -9.99% (2026-01-29)
Best day +9.01% (2025-09-03) +7.63% (2025-05-01)

Downside co-moves (2σ) — 1-Year

Computed on shared dates only (n=249). A “2σ downside move” means a shared-close log return more than 2 standard deviations below that asset’s own mean on this shared-date series. Dates below show simple returns (%) for readability.

Downside co-move map: GOOG vs. MSFT (2σ)

Shared-close daily returns

Dots mark actual downside days: asset-colored dots are one-sided downside moves, and red dots are joint downside days. Grey dots add sampled shared-return context when available. The shaded lower-left zone shows where both GOOG and MSFT crossed their own 2σ downside threshold.

-2σ MSFT -2σ GOOG Joint downside zone -12.0% 0% +12.0% +8.9% 0% -8.9% MSFT daily log return GOOG daily log return
Show downside tail dates

Dates below are shared-date observations. The “Date” is the period end (close). Tail thresholds are computed on log returns, but the table shows simple returns (%) for readability. Returns are computed from the previous shared close to this one (for example, Friday → Monday includes weekend moves).

Days when both GOOG and MSFT had a big down day (2σ)

None in this window.

Days when GOOG had a big down day

Date (interval) GOOG MSFT
2025-05-07 -7.51% +0.01%
2025-06-18 → 2025-06-20 -3.59% -0.59%
2025-12-17 -3.14% -0.06%
2026-03-24 -3.28% -2.68%

Days when MSFT had a big down day

Date (interval) GOOG MSFT
2026-01-29 +0.71% -9.99%
2026-02-05 -0.60% -4.95%
2026-02-20 → 2026-02-23 -1.02% -3.21%
2026-04-23 +0.01% -3.97%

Read this as “how ugly the ugly days get”, not as a precise forecast. One-year samples are small, so tail estimates are inherently noisy.

Full Comparison of Google vs. Microsoft (1-Year)

Metric GOOG MSFT
Total Return +106.7% +6.7%
Annualized Volatility 27.8% 24.5%
Sharpe Ratio 2.63 0.22
Sortino Ratio 4.54 0.30
Calmar Ratio 5.17 0.20
Sterling Ratio 4.97 0.07
Treynor Ratio 0.63 0.06
Ulcer Index 5.66% 14.27%
Max Drawdown -20.8% -34.1%
Avg Correlation to S&P 500 0.51 0.47
5% VaR (daily log return) -2.30% -2.43%
5% Expected Shortfall (CVaR) -3.14% -3.62%
Skew 0.30 -0.92
Excess kurtosis 3.29 10.01
2σ tail days (down / up) 4 / 5 4 / 5
Audit this calculation

Formulas, inputs, and conventions used to compute the metrics on this page.

Inputs & conventions

Shared window for pair metrics
2025-04-25 → 2026-04-23 (last shared close).
Rolling correlation sample (shared closes)
220 rolling 30-day values (from 249 shared daily returns).
Annualization (days/year)
GOOG: 252 days/year; MSFT: 252 days/year.
Risk-free rate
Uses the 3-month U.S. Treasury yield (FRED: DGS3MO), averaged over each asset’s window:
  • GOOG: 4.17% over 2025-04-25 → 2026-04-23.
  • MSFT: 4.17% over 2025-04-25 → 2026-04-23.
Volatility drag (rule of thumb)
Estimated from annualized volatility (simple returns). For the log-return framing, see Log returns.
  • GOOG: ≈ -3.9%/yr
  • MSFT: ≈ -3.0%/yr
Data alignment
No forward fill. Correlation and tail co-moves are computed on shared closes only.
For cross-calendar pairs (e.g., crypto vs stocks), weekend/holiday moves roll into the next shared close.
Return conventions
Volatility/Sharpe/Sortino use simple daily returns. Tail-risk uses daily log returns for distribution stats (but tables show simple returns). Log returns.

Formulas

Daily simple return
rt=PtPt11r_t = \frac{P_t}{P_{t-1}} - 1
σann=σ(rt)A\sigma_{ann} = \sigma(r_t)\sqrt{A}
drag12σann2\text{drag} \approx \tfrac{1}{2}\sigma_{ann}^2
S=Arˉrfσ(rt)AS = \frac{A\,\bar{r} - r_f}{\sigma(r_t)\sqrt{A}}
So=ArˉrfE[min(0,rtrf/A)2]ASo = \frac{A\,\bar{r} - r_f}{\sqrt{\mathbb{E}[\min(0,\,r_t - r_f/A)^2]}\,\sqrt{A}}
MDD=mint(PtmaxstPs1)MDD = \min_t\left(\frac{P_t}{\max_{s \le t} P_s} - 1\right)
ρ=cov(rA,rB)σAσB\rho = \frac{\operatorname{cov}(r^A,\,r^B)}{\sigma_A\,\sigma_B}
t=ln(PtPt1)\ell_t = \ln\left(\frac{P_t}{P_{t-1}}\right)
Notation
PtP_t
Price on day t.
rtr_t
Simple daily return.
t\ell_t
Log daily return.
rˉ\bar{r}
Average daily return.
σ(rt)\sigma(r_t)
Standard deviation of daily returns.
AA
Annualization factor (days/year).
rfr_f
Annual risk-free rate.

Google vs Microsoft: Frequently Asked Questions

Which has higher volatility: GOOG or MSFT?

GOOG showed higher volatility at 27.8% annualized, compared to 24.5% for MSFT Over the past year. Higher volatility means larger price swings in both directions.

Does GOOG provide diversification when held with MSFT?

GOOG and MSFT are weakly correlated over the past year, with an average correlation of 0.15. This weak correlation suggests meaningful diversification benefits when held together.

How bad are the worst 5% days for GOOG vs MSFT?

Over the past year, GOOG's 5% VaR was -2.30% and its 5% Expected Shortfall was -3.14% (worst 13 days). MSFT's were -2.43% and -3.62% (worst 13 days).

Do GOOG and MSFT crash together on bad days?

On shared dates (n=249), when MSFT has a 2σ down day, GOOG also does 0.0% (0/4 days). In the other direction, when GOOG has one, MSFT also does 0.0% (0/4 days).

Which has better risk-adjusted returns: GOOG or MSFT?

GOOG showed better risk-adjusted performance with a Sharpe ratio of 2.63 versus MSFT's 0.22 Over the past year.

Can GOOG and MSFT be combined in a portfolio?

Yes, though allocation sizing matters. Their weak correlation could meaningfully reduce overall portfolio variance. GOOG's higher volatility (27.8%) means even small allocations can materially impact overall portfolio risk.

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