Money Math

Getting Comfortable With Log Charts

In Sunday’s Getting Paid To Flip Million Dollar Coins, I mentioned that exponential functions such as investment compounding are best displayed on a semi-log chart. Let’s do another example of that step-by-step for anyone that wants to learn or anyone who has struggled to teach it to someone else.

Suppose your wealth grows according to this compounding formula:

Wealth = a(1+r)ᵗ

where:

a = starting wealth

r= compounding rate (ie 10%)

t = time in years

For our examples we just use a = 1, so our charts are “growth of a dollar”.

For rule of 72 fans, you know that at a 10% growth rate wealth doubles every 7 years.

Wealth = (1.10)⁷ = 1.95

If you started with $10,000 after 30 years you’d have about $175k.

This chart is not necessarily hard on the eyes, but the fact that time is the exponential variable is a clue that over long stretches an exponential chart is going to become low resolution.

Here’s a 90 cumulative return history for the SP500

https://www.macrotrends.net/2324/sp-500-historical-chart-data …

2 observations:

The later years where you are compounding on a larger base of wealth stretch the chart so the earlier years’ changes are invisible.
The resolution of the chart and the ‘larger base effect’ obscure what you probably care about — how the rate of return is changing.

Here’s the log chart:

The log chart now shows the resolution of zigs and zags in the early years by making the Y-axis distance between wealth levels of 10 and100 the same as 100 to 1,000 or 1,000 to 10,000.

To create our own log chart, we transform the wealth function:

Wealth = a(1+r)ᵗ

Log(Wealth) = Log(a) + Log(1+r)ᵗ

Log(Wealth) = Log(a) + t * Log(1+r)

This fits the form of a line:

Y = b + mX

Set “starting wealth” to a = $1.

That reduces the equation to:

Log(Wealth) = t * Log(1+r)

t, time, is our independent variable and Log(1+r) is a constant slope that depends on the rate of return.

Rule of 72 enjoyyyers know compounding at 10% for 7 years doubles wealth:

Wealth = (1.10)⁷ = 2

We take the log of both sides:

Log(Wealth) = Log(2) = 7 * Log(1.10)

You can just use a calculator to see that log(2) rounds to .29 and slope of the log chart will be Log(1.10) = .041

To interpret the log chart we observe, if:

Log (Wealth) = .29 that represents a doubling of wealth
Log (Wealth) = 1 that represents a 10x increase in wealth aka an order of magnitude increase

Let’s now chart the wealth function as Log(Wealth):

Note: each of the 10 series corresponds to a rate of return of 10%, 9%, 8% and so on. The middle series (purple) is 5% per year and the flattest line corresponds to 1% per year.

If log (wealth) = .3, wealth has approximately doubled
If log (wealth) = .48, wealth has tripled
If log (wealth) = .7, wealth has 5x
If log (wealth) = 1, wealth has 10x

Also note that at 10% growth per year we computed the slope of the log chart earlier to be Log(1.10) = .041.

And voila, it takes about 25 years (1/.04) to 10x your wealth, aka Log(Wealth) = 1, a whole order of magnitude.

Moving your eyes to the right along the line where Y=.3, to the light blue numbers. Those numbers represent a rate of return of 4%. You can see that it takes 4 extra years to get to the same level of wealth if you compound at 4% instead of 5%.

What you can generally observe is that earning 2% instead of 1%, is vastly more important than going from 9% ror to 10% ror. This idea is captured in the fact that 2% is double the rate of return of 1% and 10% is only 11% bigger than 9% but in practical terms it is a reminder that:

a 1% difference in performance is a big deal
taxes are a big deal
fees are a big deal (“oh it’s just 1%”)
inflation rates (and real returns) are a big deal
but all these “big deals” matter more when the difference is a compounded rate of 2% vs 3% as opposed to 9% vs 10%

Here’s the chart zoomed in to holding periods of at least 10 years:

At 5% per year, you’ll double wealth in 14 years. At 3% it will take almost a decade longer.

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View Comments

Austin B says:

8 May 2025 at 10:06 pm

I’m a mechanical engineer and use log charts on the daily to review frequency domain information from vibration tests. I always tell people who are new to them: log charts let you see the “shape” of the plot, regardless of the scale. You can’t do this on a linearly-scaled plot because the few data points you have furthest from zero obscure the shape everywhere else. (Just try looking for selloffs in the linear scale plot of the S&P before 1990!)