The metrics we use
Our collection of metrics fall into different categories and are designed to highlight the various aspects of an asset's performance.
To learn more about the time series data used as a basis for the metric calculation, read this article.
In the analysis and comparison advanced settings, the user has the option to choose whether the asset's close price or adjusted close price time series is used for the metrics calculations. Read this article for more information.
Metrics measuring different aspects of the return of an asset.
Returns time series
Time series of returns between each day and the first day.
The return on the first day of the time series is always zero, as the anchor to compare all other days to.
Mean return
Mean of (daily) returns, scaled to a given period, e.g. annualised.
Cumulative return
Return between two dates.
Period return
Cumulative return but scaled to a given period, e.g. annualised.
For example, the annualised return or CARG of an investment between two dates 10 years apart.
Period yield
Difference between the return calculated with close prices vs. the return calculated with adjusted close prices, scaled to a given period, e.g. annualised.
Metrics measuring different aspects of the risk of an asset, specifically the mean risk and the tail risk.
Drawdowns time series
Time series of returns between each day and the preceding peak.
If a particular day is a new peak, the drawdown is zero. Otherwise, the drawdown is always negative.
Volatility
Standard deviation of (daily) returns, scaled to a given period, e.g. annualised.
Down volatility
Standard deviation of (daily) negative returns of a returns time series, scaled to a given period, e.g. annualised.
Maximum drawdown
Standard measure of tail risk. The maximal percentage difference between a peak and a following trough in a given time series.
Value at risk
Hypothetical loss at a given confidence level assuming a normal distribution of (daily) returns, scaled to a given period, e.g. annualised.
For example, an annual value at risk of 10% at a 95% confidence level means a 95% probability of a loss of 10% of less in one year.
Expected shortfall
Value at risk's sister metric, measuring the expected loss in the part of the distribution that was excluded in the value at risk calculation.
- Value at risk = 10%: 95% probability of a loss of 10% or less in one year
- Expected shortfall = 15%: 5% probability of a loss of 15% in one year
Metrics measuring the return of an asset in relation to the risk of an asset.
Sharpe ratio
Standard measure of risk-adjusted returns, developed by Nobel Prize winner William F. Sharpe.
An investment with a higher return than risk has a Sharpe ratio greater than one. A loss-making investment has a Sharpe ratio below zero.
Sortino ratio
Sharpe ratio but using down volatility instead of volatility as the risk measure.
Calmar ratio
Return per unit of maximum drawdown.
While the Sharpe and Sortino ratio use measures of mean risk in the denominator, the Calmar ratio uses a measure of tail risk.
Metrics measuring an asset's performance vs. a benchmark.
Correlation
Pearson correlation coefficient between the daily returns of an asset and a benchmark.
Beta
- Beta = 1: Asset is perfectly correlated with the benchmark and has the same volatility as the benchmark
- Beta = 0: Asset is perfectly uncorrelated with the benchmark
- Beta > 1: Asset is correlated with the benchmark and has a higher volatility than the benchmark
- Beta between 1 and 0: Asset is uncorrelated with the benchmark and/or has a lower volatility than the benchmark
- Beta < 0: Asset is negatively correlated with the benchmark
Alpha
- Annual alpha = 0%: The asset and the benchmark generate the same return when adjusting for risk
- Annual alpha = +1%: The asset generates 1 percentage point more return than the benchmark when adjusting for risk
- Annual alpha = -1%: The asset generates 1 percentage point less return than the benchmark when adjusting for risk
Metrics measuring the characteristics of an asset's distribution of (daily) returns.
Skewness
The skewness of the distribution of (daily) returns is a measure of the symmetry of the distribution.
A normal distribution is symmetrical and has a skewness of zero. A distribution with positive skew is asymmetrical and shifted to the left with its tail on the right side.
Kurtosis
The kurtosis of the distribution of(daily) returns is a measure of the tailedness of the distribution, i.e. how much "weight" is in the tails vs. the centre.
A normal distribution has a kurtosis of 3. A distribution with a kurtosis of greater than 3 has excess kurtosis and more "weight" in the tails of the distribution.