Skip to content
All simulations

Log returns

See why +10% then −10% doesn't get you back, and why log returns simply add up.

Up and back down is not a round trip

10%

To get from $110.00 back to $100, the price only needs to fall 9.09%.

Start
$100.00
Up 10%
$110.00
Down 10%
$99.00
StepSimple returnLog return
$100 → $110.00+10.00%+9.53%
$110.00 → $100-9.09%-9.53%
Round trip adds to+0.91%0.00%

A 10% gain followed by a 10% loss leaves you at $99.00, not $100. Simple returns are lopsided: up 10.00% but back down only 9.09%. Log returns are symmetric: +9.53% up and -9.53% back, adding to exactly zero.

Log returns add up, simple returns don't

8%
$100 became$128.30
Real total return+28.30%
Simple return each month Log return each month
Sum of 12 simple returns
+28.58%
≠ real total return +28.30%
Sum of 12 log returns
+24.92%
= ln($128.30 ÷ $100), exactly

Adding up this year's monthly simple returns gives +28.58%, but $100 actually became $128.30, a +28.30% return. The gap of 0.28% comes from compounding. The log returns add to +24.92%, and e raised to that gives the real total back.

Model log returns and prices stay positive

50% a year
Dollar-step paths that went below $022%
Geometric Brownian motion paths below $00%
Random dollar steps
Geometric Brownian motion

Both panels use the same random shocks over three years. Added as fixed dollar amounts, 22% of prices fall below zero, which no real price can do. Treated as normally distributed log returns, the price is the exponential of the process, so it never reaches zero.

Try this

  • Set the move to 10%: $100 goes to $110, then back down to $99. Recovering only takes a 9.09% fall.
  • Try a 50% move. How far from $100 do you end up?
  • Draw a new year of returns a few times. The log sum always matches ln(end ÷ start); the simple sum never quite does.
  • Raise the volatility in the last part and count how many dollar-step prices go negative.

The math

rt=ln⁡PtPt−1∑t=1nrt=ln⁡PnP0r_t = \ln\frac{P_t}{P_{t-1}} \qquad \sum_{t=1}^{n} r_t = \ln\frac{P_n}{P_0}
rtr_t
log return in period t
PtP_t
price at the end of period t
ln⁡\ln
natural logarithm
Pn=P0e∑rtP_n = P_0 e^{\sum r_t}
prices are the exponential, so always positive