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, when measuring performance, is the actual rate of return of an investment or a pool of investments over a given evaluation period. Total return includes interest, capital gains, dividends and distributions realized over a given period of time. Total return accounts for two categories of return: income including interest paid by fixed-income investments, distributions or dividends and capital appreciation, representing the change in the market price of an asset.'

Using this definition on our asset we see for example:
  • Compared with the benchmark SPY (114.2%) in the period of the last 5 years, the total return of 126.4% of Automatic Data Processing is greater, thus better.
  • Compared with SPY (46.4%) in the period of the last 3 years, the total return, or performance of 57.9% is higher, thus better.

CAGR:

'The compound annual growth rate isn't a true return rate, but rather a representational figure. It is essentially a number that describes the rate at which an investment would have grown if it had grown the same rate every year and the profits were reinvested at the end of each year. In reality, this sort of performance is unlikely. However, CAGR can be used to smooth returns so that they may be more easily understood when compared to alternative investments.'

Which means for our asset as example:
  • Compared with the benchmark SPY (16.5%) in the period of the last 5 years, the annual return (CAGR) of 17.8% of Automatic Data Processing is higher, thus better.
  • Compared with SPY (13.5%) in the period of the last 3 years, the annual return (CAGR) of 16.4% is higher, thus better.

Volatility:

'Volatility is a statistical measure of the dispersion of returns for a given security or market index. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. Commonly, the higher the volatility, the riskier the security. In the securities markets, volatility is often associated with big swings in either direction. For example, when the stock market rises and falls more than one percent over a sustained period of time, it is called a 'volatile' market.'

Applying this definition to our asset in some examples:
  • The 30 days standard deviation over 5 years of Automatic Data Processing is 26.4%, which is larger, thus worse compared to the benchmark SPY (18.7%) in the same period.
  • During the last 3 years, the volatility is 30.6%, which is greater, thus worse than the value of 22.6% 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.'

Which means for our asset as example:
  • The downside volatility over 5 years of Automatic Data Processing is 18.8%, which is greater, thus worse compared to the benchmark SPY (13.6%) in the same period.
  • During the last 3 years, the downside volatility is 21.8%, which is larger, thus worse than the value of 16.5% from the benchmark.

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.'

Applying this definition to our asset in some examples:
  • The Sharpe Ratio over 5 years of Automatic Data Processing is 0.58, which is lower, thus worse compared to the benchmark SPY (0.75) in the same period.
  • During the last 3 years, the Sharpe Ratio is 0.46, which is lower, thus worse than the value of 0.49 from the benchmark.

Sortino:

'The Sortino ratio measures the risk-adjusted return of an investment asset, portfolio, or strategy. It is a modification of the Sharpe ratio but penalizes only those returns falling below a user-specified target or required rate of return, while the Sharpe ratio penalizes both upside and downside volatility equally. Though both ratios measure an investment's risk-adjusted return, they do so in significantly different ways that will frequently lead to differing conclusions as to the true nature of the investment's return-generating efficiency. The Sortino ratio is used as a way to compare the risk-adjusted performance of programs with differing risk and return profiles. In general, risk-adjusted returns seek to normalize the risk across programs and then see which has the higher return unit per risk.'

Using this definition on our asset we see for example:
  • Looking at the ratio of annual return and downside deviation of 0.81 in the last 5 years of Automatic Data Processing, we see it is relatively lower, thus worse in comparison to the benchmark SPY (1.03)
  • During the last 3 years, the ratio of annual return and downside deviation is 0.64, which is smaller, thus worse than the value of 0.67 from the benchmark.

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.'

Which means for our asset as example:
  • Looking at the Ulcer Index of 9.17 in the last 5 years of Automatic Data Processing, we see it is relatively higher, thus worse in comparison to the benchmark SPY (5.58 )
  • Compared with SPY (6.91 ) in the period of the last 3 years, the Ulcer Index of 11 is higher, 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:
  • 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.
  • Compared with SPY (-33.7 days) in the period of the last 3 years, the maximum drop from peak to valley of -39.5 days is lower, thus worse.

MaxDuration:

'The Maximum 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. It is the length of time the account was in the Max Drawdown. A Max Drawdown measures a retrenchment from when an equity curve reaches a new high. It’s the maximum an account lost during that retrenchment. This method is applied because a valley can’t be measured until a new high occurs. Once the new high is reached, the percentage change from the old high to the bottom of the largest trough is recorded.'

Applying this definition to our asset in some examples:
  • Looking at the maximum days below previous high of 210 days in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus worse in comparison to the benchmark SPY (139 days)
  • Looking at maximum days under water in of 210 days in the period of the last 3 years, we see it is relatively larger, thus worse in comparison to SPY (139 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 42 days, which is greater, thus worse compared to the benchmark SPY (33 days) in the same period.
  • During the last 3 years, the average days below previous high is 51 days, which is larger, thus worse than the value of 36 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.