Market Statistics Guide

Systems Capital · Data & Methodology

Daily return statistics summarize how a stock has behaved — day over day — over the past year. Rather than looking at a single price chart, these statistics compress the entire year's trading days into a handful of numbers that statistics regarding volatility, consistency in returns, and directional trends at a glance.

This guide explains each metric in the Daily Market Return Statistics table and how to apply them in equity research.

The Metrics Explained

Average Daily Return

Also known as "Mean" This metric is the arithmetic average of the close-to-close return for each trading day over the trailing year. A positive value confirms the average daily performance of the stock has had net upward drift. However, average return is sensitive to outliers, and a large one day gain can make an otherwise flat stock have strong average. Always read it alongside other statistics.

Standard Deviation

Measures how widely daily returns are dispersed around the mean. A standard deviation of 1.2% means most days fall within roughly ±1.2% of the mean. Higher standard deviation means higher daily volatility and larger swings in both directions. This is the primary volatility signal in the table.

Median Return

The middle value when all daily returns are sorted. This is a more telling measure of the "typical" day than the mean. When the median is significantly lower than the mean, large positive outliers are skewing the average, which can imply that the stock's typical daily behavior is more muted than the mean suggests. A stock with a lower median than average suggests the average is brought up by outlier days, suggesting the stock has big one-day swings.

Mode Return

The mode shows the return value that occured on the most number of days the stock had over the year. Because exact return values rarely repeat across any two trading days, we calculate mode by limiting daily returns two only 2 decimal points. A mode clustered near zero indicates the stock often has small, quiet days. Comparing mode to mean and median reveals whether the stock's typical behavior suggests large outlier days with higher swings or consistent returns day-over-day.

Max / Min

The Max and the Min represent the single best and worst daily return over the trailing year. These include, or may not include, days that events occured — earnings surprises, macro shocks, or news-driven spikes. A wide gap between max and min relative to standard deviation suggests the stock is prone to large moves throughout the year. We consider these metrics primiarily for risk management with a preference toward small mins, and large maxes.

Kurtosis

Measures the "tailedness" of the distribution relative to a normal bell-shaped distribution. High kurtosis indicates fat tails which means the stock has experienced extreme moves (both up and down) more frequently than a normal distribution. Low kurtosis suggests the distribution is flatter and more concentrated around the mean. In the way that Max and Min tell us the most extreme values that have been historically achieved, Kurtosis tells us how frequent outlier values have happened. Combined with standard deviation, kurtosis helps distinguish between steady, consistent volatility and punctuated volatility with large surprise jumps. Normal Kurtosis has a value of 3.

Skewness

Skewness measures the asymmetry of the return distribution by measuring how the mean shifts. In a skewed distribution, the mean shifts toward the tail, the median stays between the median and the mode, and the mode stays at the peak of the distribution. Positive skewness implies the average is higher than the mode, and vice-versa for negative skewness. Negative skewness indicates a longer tail on the left, which indicates that the stock is prone to sharp downside moves. Skewness near zero suggests symmetric behavior.

How to Use These Together

No single metric tells the full story. And sometimes the importance of characteristic changes over time depending on the stock and the overall market. It's best to check often, understand fully, and remember that historic results aren't indicative of future performance.

As a rule of thumb, Systems Capital prefers investments with the following characteristics:

1. Average return that is greater than 0%.
2. Standard Deviation as close to 0% as possible.
3. Median as large as possible.
4. Mode also as large as possible but with all mean, median, and mode, ensure values are greater than daily inflation.
5. Max that is greater than 0% and as high as possible.
6. Min that is as small as possible and is ideally a positive value.
7. Kurtosis depends on investment goals. Often, we invest in both high and low, but not often normal kurtosis.
8. Skewness is market dependent, but often right around 0.

Live Data Preview

The table below shows a sample of the full dataset. Values represent trailing 1-year daily return statistics (close to close). The complete table — updated every weekday — is available to subscribers.

Data Notes

All statistics are calculated from close-to-close daily returns for trading days over the trailing year. Data is sourced programmatically and updated each weekday just after market open. Data is presented as-is and is not investment advice. For licensing inquiries or custom research, see the About page.