Description

Automatic Data Processing, Inc. provides cloud-based human capital management solutions worldwide. It operates through two segments, Employer Services and Professional Employer Organization (PEO). The Employer Services segment offers strategic, cloud-based platforms, and human resources (HR) outsourcing solutions. Its offerings include payroll, benefits administration, talent management, HR management, workforce management, insurance, retirement, and compliance services. The PEO Services segment provides HR outsourcing solutions to small and mid-sized businesses through a co-employment model. This segment offers benefits package, protection and compliance, talent engagement, comprehensive outsourcing, and recruitment process outsourcing services. The company was founded in 1949 and is headquartered in Roseland, New Jersey.

Statistics (YTD)

What do these metrics mean? [Read More] [Hide]

TotalReturn:

'Total return is the amount of value an investor earns from a security over a specific period, typically one year, when all distributions are reinvested. Total return is expressed as a percentage of the amount invested. For example, a total return of 20% means the security increased by 20% of its original value due to a price increase, distribution of dividends (if a stock), coupons (if a bond) or capital gains (if a fund). Total return is a strong measure of an investment’s overall performance.'

Using this definition on our asset we see for example:
  • The total return, or increase in value over 5 years of Automatic Data Processing is 63%, which is lower, thus worse compared to the benchmark SPY (103.4%) in the same period.
  • During the last 3 years, the total return, or increase in value is 29.6%, which is lower, thus worse than the value of 33.4% from the benchmark.

CAGR:

'Compound annual growth rate (CAGR) is a business and investing specific term for the geometric progression ratio that provides a constant rate of return over the time period. CAGR is not an accounting term, but it is often used to describe some element of the business, for example revenue, units delivered, registered users, etc. CAGR dampens the effect of volatility of periodic returns that can render arithmetic means irrelevant. It is particularly useful to compare growth rates from various data sets of common domain such as revenue growth of companies in the same industry.'

Using this definition on our asset we see for example:
  • The annual return (CAGR) over 5 years of Automatic Data Processing is 10.3%, which is smaller, thus worse compared to the benchmark SPY (15.3%) in the same period.
  • During the last 3 years, the annual return (CAGR) is 9%, which is lower, thus worse than the value of 10.1% from the benchmark.

Volatility:

'In finance, volatility (symbol σ) is the degree of variation of a trading price series over time as measured by the standard deviation of logarithmic returns. Historic volatility measures a time series of past market prices. Implied volatility looks forward in time, being derived from the market price of a market-traded derivative (in particular, an option). Commonly, the higher the volatility, the riskier the security.'

Applying this definition to our asset in some examples:
  • The historical 30 days volatility over 5 years of Automatic Data Processing is 27.8%, which is larger, thus worse compared to the benchmark SPY (20.9%) in the same period.
  • Compared with SPY (17.3%) in the period of the last 3 years, the 30 days standard deviation of 22.2% is higher, thus worse.

DownVol:

'Downside risk is the financial risk associated with losses. That is, it is the risk of the actual return being below the expected return, or the uncertainty about the magnitude of that difference. Risk measures typically quantify the downside risk, whereas the standard deviation (an example of a deviation risk measure) measures both the upside and downside risk. Specifically, downside risk in our definition is the semi-deviation, that is the standard deviation of all negative returns.'

Which means for our asset as example:
  • Looking at the downside deviation of 20% in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus worse in comparison to the benchmark SPY (14.9%)
  • Looking at downside deviation in of 16% in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (12.1%).

Sharpe:

'The Sharpe ratio was developed by Nobel laureate William F. Sharpe, and is used to help investors understand the return of an investment compared to its risk. The ratio is the average return earned in excess of the risk-free rate per unit of volatility or total risk. Subtracting the risk-free rate from the mean return allows an investor to better isolate the profits associated with risk-taking activities. One intuition of this calculation is that a portfolio engaging in 'zero risk' investments, such as the purchase of U.S. Treasury bills (for which the expected return is the risk-free rate), has a Sharpe ratio of exactly zero. Generally, the greater the value of the Sharpe ratio, the more attractive the risk-adjusted return.'

Using this definition on our asset we see for example:
  • The Sharpe Ratio over 5 years of Automatic Data Processing is 0.28, which is smaller, thus worse compared to the benchmark SPY (0.61) in the same period.
  • Compared with SPY (0.44) in the period of the last 3 years, the risk / return profile (Sharpe) of 0.29 is smaller, thus worse.

Sortino:

'The Sortino ratio, a variation of the Sharpe ratio only factors in the downside, or negative volatility, rather than the total volatility used in calculating the Sharpe ratio. The theory behind the Sortino variation is that upside volatility is a plus for the investment, and it, therefore, should not be included in the risk calculation. Therefore, the Sortino ratio takes upside volatility out of the equation and uses only the downside standard deviation in its calculation instead of the total standard deviation that is used in calculating the Sharpe ratio.'

Applying this definition to our asset in some examples:
  • Compared with the benchmark SPY (0.85) in the period of the last 5 years, the ratio of annual return and downside deviation of 0.39 of Automatic Data Processing is smaller, thus worse.
  • Compared with SPY (0.63) in the period of the last 3 years, the excess return divided by the downside deviation of 0.41 is smaller, thus worse.

Ulcer:

'The ulcer index is a stock market risk measure or technical analysis indicator devised by Peter Martin in 1987, and published by him and Byron McCann in their 1989 book The Investors Guide to Fidelity Funds. It's designed as a measure of volatility, but only volatility in the downward direction, i.e. the amount of drawdown or retracement occurring over a period. Other volatility measures like standard deviation treat up and down movement equally, but a trader doesn't mind upward movement, it's the downside that causes stress and stomach ulcers that the index's name suggests.'

Which means for our asset as example:
  • The Downside risk index over 5 years of Automatic Data Processing is 12 , which is higher, thus worse compared to the benchmark SPY (9.32 ) in the same period.
  • During the last 3 years, the Ulcer Ratio is 11 , which is larger, thus worse than the value of 10 from the benchmark.

MaxDD:

'A maximum drawdown is the maximum loss from a peak to a trough of a portfolio, before a new peak is attained. Maximum Drawdown is an indicator of downside risk over a specified time period. It can be used both as a stand-alone measure or as an input into other metrics such as 'Return over Maximum Drawdown' and the Calmar Ratio. Maximum Drawdown is expressed in percentage terms.'

Applying this definition to our asset in some examples:
  • Compared with the benchmark SPY (-33.7 days) in the period of the last 5 years, the maximum DrawDown of -39.5 days of Automatic Data Processing is lower, thus worse.
  • During the last 3 years, the maximum drop from peak to valley is -21.8 days, which is greater, thus better than the value of -24.5 days from the benchmark.

MaxDuration:

'The Drawdown Duration is the length of any peak to peak period, or the time between new equity highs. The Max Drawdown Duration is the worst (the maximum/longest) amount of time an investment has seen between peaks (equity highs). Many assume Max DD Duration is the length of time between new highs during which the Max DD (magnitude) occurred. But that isn’t always the case. The Max DD duration is the longest time between peaks, period. So it could be the time when the program also had its biggest peak to valley loss (and usually is, because the program needs a long time to recover from the largest loss), but it doesn’t have to be'

Using this definition on our asset we see for example:
  • The maximum days below previous high over 5 years of Automatic Data Processing is 384 days, which is smaller, thus better compared to the benchmark SPY (488 days) in the same period.
  • Looking at maximum days under water in of 384 days in the period of the last 3 years, we see it is relatively lower, thus better in comparison to SPY (488 days).

AveDuration:

'The Average Drawdown Duration is an extension of the Maximum Drawdown. However, this metric does not explain the drawdown in dollars or percentages, rather in days, weeks, or months. The Avg Drawdown Duration is the average amount of time an investment has seen between peaks (equity highs), or in other terms the average of time under water of all drawdowns. So in contrast to the Maximum duration it does not measure only one drawdown event but calculates the average of all.'

Using this definition on our asset we see for example:
  • The average days below previous high over 5 years of Automatic Data Processing is 100 days, which is lower, thus better compared to the benchmark SPY (123 days) in the same period.
  • Looking at average days below previous high in of 126 days in the period of the last 3 years, we see it is relatively lower, thus better in comparison to SPY (180 days).

Performance (YTD)

Historical returns have been extended using synthetic data.

Allocations ()

Allocations

Returns (%)

  • Note that yearly returns do not equal the sum of monthly returns due to compounding.
  • Performance results of Automatic Data Processing are hypothetical and do not account for slippage, fees or taxes.