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)

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TotalReturn:

'The total return on a portfolio of investments takes into account not only the capital appreciation on the portfolio, but also the income received on the portfolio. The income typically consists of interest, dividends, and securities lending fees. This contrasts with the price return, which takes into account only the capital gain on an investment.'

Which means for our asset as example:
  • The total return over 5 years of Automatic Data Processing is 170.8%, which is larger, thus better compared to the benchmark SPY (129.1%) in the same period.
  • During the last 3 years, the total return, or performance is 62%, which is lower, thus worse than the value of 71.3% from the benchmark.

CAGR:

'The compound annual growth rate (CAGR) is a useful measure of growth over multiple time periods. It can be thought of as the growth rate that gets you from the initial investment value to the ending investment value if you assume that the investment has been compounding over the time period.'

Using this definition on our asset we see for example:
  • Compared with the benchmark SPY (18.1%) in the period of the last 5 years, the annual performance (CAGR) of 22.1% of Automatic Data Processing is higher, thus better.
  • Compared with SPY (19.7%) in the period of the last 3 years, the annual performance (CAGR) of 17.4% is lower, thus worse.

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.'

Which means for our asset as example:
  • The historical 30 days volatility over 5 years of Automatic Data Processing is 26.6%, which is larger, thus worse compared to the benchmark SPY (18.7%) in the same period.
  • During the last 3 years, the historical 30 days volatility is 30.5%, which is higher, thus worse than the value of 22.5% from the benchmark.

DownVol:

'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.'

Applying this definition to our asset in some examples:
  • The downside risk over 5 years of Automatic Data Processing is 18.9%, which is higher, thus worse compared to the benchmark SPY (13.6%) in the same period.
  • Looking at downside volatility in of 21.7% in the period of the last 3 years, we see it is relatively higher, thus worse in comparison to SPY (16.3%).

Sharpe:

'The Sharpe ratio is the measure of risk-adjusted return of a financial portfolio. Sharpe ratio is a measure of excess portfolio return over the risk-free rate relative to its standard deviation. Normally, the 90-day Treasury bill rate is taken as the proxy for risk-free rate. A portfolio with a higher Sharpe ratio is considered superior relative to its peers. The measure was named after William F Sharpe, a Nobel laureate and professor of finance, emeritus at Stanford University.'

Using this definition on our asset we see for example:
  • The risk / return profile (Sharpe) over 5 years of Automatic Data Processing is 0.74, which is lower, thus worse compared to the benchmark SPY (0.83) in the same period.
  • Compared with SPY (0.76) in the period of the last 3 years, the Sharpe Ratio of 0.49 is lower, thus worse.

Sortino:

'The Sortino ratio improves upon the Sharpe ratio by isolating downside volatility from total volatility by dividing excess return by the downside deviation. The Sortino ratio is a variation of the Sharpe ratio that differentiates harmful volatility from total overall volatility by using the asset's standard deviation of negative asset returns, called downside deviation. The Sortino ratio takes the asset's return and subtracts the risk-free rate, and then divides that amount by the asset's downside deviation. The ratio was named after Frank A. Sortino.'

Applying this definition to our asset in some examples:
  • Looking at the ratio of annual return and downside deviation of 1.04 in the last 5 years of Automatic Data Processing, we see it is relatively lower, thus worse in comparison to the benchmark SPY (1.15)
  • Looking at downside risk / excess return profile in of 0.69 in the period of the last 3 years, we see it is relatively smaller, thus worse in comparison to SPY (1.05).

Ulcer:

'Ulcer Index is a method for measuring investment risk that addresses the real concerns of investors, unlike the widely used standard deviation of return. UI is a measure of the depth and duration of drawdowns in prices from earlier highs. Using Ulcer Index instead of standard deviation can lead to very different conclusions about investment risk and risk-adjusted return, especially when evaluating strategies that seek to avoid major declines in portfolio value (market timing, dynamic asset allocation, hedge funds, etc.). The Ulcer Index was originally developed in 1987. Since then, it has been widely recognized and adopted by the investment community. According to Nelson Freeburg, editor of Formula Research, Ulcer Index is “perhaps the most fully realized statistical portrait of risk there is.'

Using this definition on our asset we see for example:
  • Looking at the Downside risk index of 9.05 in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus worse in comparison to the benchmark SPY (5.59 )
  • Compared with SPY (6.38 ) in the period of the last 3 years, the Ulcer Ratio of 11 is larger, thus worse.

MaxDD:

'Maximum drawdown is defined as the peak-to-trough decline of an investment during a specific period. It is usually quoted as a percentage of the peak value. The maximum drawdown can be calculated based on absolute returns, in order to identify strategies that suffer less during market downturns, such as low-volatility strategies. However, the maximum drawdown can also be calculated based on returns relative to a benchmark index, for identifying strategies that show steady outperformance over time.'

Which means for our asset as example:
  • Looking at the maximum drop from peak to valley of -39.5 days in the last 5 years of Automatic Data Processing, we see it is relatively lower, thus worse in comparison to the benchmark SPY (-33.7 days)
  • Looking at maximum DrawDown in of -39.5 days in the period of the last 3 years, we see it is relatively lower, thus worse in comparison to SPY (-33.7 days).

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) in days.'

Which means for our asset as example:
  • The maximum time in days below previous high water mark over 5 years of Automatic Data Processing is 212 days, which is larger, thus worse compared to the benchmark SPY (139 days) in the same period.
  • Compared with SPY (119 days) in the period of the last 3 years, the maximum days under water of 212 days is higher, thus worse.

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.'

Applying this definition to our asset in some examples:
  • The average days under water over 5 years of Automatic Data Processing is 40 days, which is greater, thus worse compared to the benchmark SPY (32 days) in the same period.
  • During the last 3 years, the average days below previous high is 47 days, which is greater, thus worse than the value of 25 days from the benchmark.

Performance (YTD)

Historical returns have been extended using synthetic data.

Allocations
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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, do not account for slippage, fees or taxes, and are based on backtesting, which has many inherent limitations, some of which are described in our Terms of Use.