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Lesson 9 of 19

Calculating Geometric Mean

One problem with arithmetic mean is that it assumes the returns on the investment made at the beginning of each period. So, for each period the beginning investment amount is assumed to be the same. It ignores the compounding effect of investment returns made in the previous years. Using arithmetic returns, our measure can be majorly flawed. Consider an investment of $100 at the beginning. Say in the first year the investment value rises to $200. The returns are 100%. In year 2, the investment falls back to $100, which will be a return of -50% in the year 2. If we take the average of two year returns, i.e., 100% in year 1 and -50% in year 2, it shows an average annual return of 25% on this investment, even though our investment value is back to $100 (from where we started). This problem can be solved by calculating geometric returns which incorporates the compounding effect.

Let’s taken an example to understand how geometric returns are calculated. Let’s say our portfolio generated the following returns in 5 years.

gm1
gm1

Geometric returns = [(1+100%)*(1-50%)*(1+35%)*(1-20%)*(1+50%)]^(1/5) – 1 = 10%

The arithmetic mean on this portfolio would have been 23%.

As you can see, geometric return is lower than the arithmetic return, and is a better method for aggregating returns over multiple holding periods.

We can say that geometric mean is a more suitable method for aggregating returns over a period of time.

Previous Lesson

Calculating Weighted Average Mean

Next Lesson

Calculating Harmonic Mean

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Statistical Concepts and Market Returns

19 lessons

Lessons

1
Descriptive Vs. Inferential Statistics
2
Types of Measurement Scales
3
Parameter, Sample Statistic, and Frequency Distribution
4
Relative Frequencies and Cumulative Relative Frequencies
5
Properties of a Data Set (Histogram / Frequency Polygon)
6
Measures of Central Tendency
7
Calculating Arithmetic Mean
8
Calculating Weighted Average Mean
9
Calculating Geometric Mean
10
Calculating Harmonic Mean
11
Calculating Median and Mode of a Data Set
12
Quartiles, Quintiles, Deciles, and Percentiles
13
Range and Mean Absolute Deviation
14
Variance and Standard Deviation
15
Chebyshev’s Inequality
16
Coefficient of Variation
17
Sharpe Ratio
18
Skewness and Kurtosis
19
Relative Locations of Mean, Median and Mode

Quizzes

Statistical Concepts and Market Returns
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