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:

'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.'

Which means for our asset as example:
  • Looking at the total return of 135.6% in the last 5 years of Automatic Data Processing, we see it is relatively greater, thus better in comparison to the benchmark SPY (62.7%)
  • Compared with SPY (34.7%) in the period of the last 3 years, the total return, or performance of 58.2% is greater, thus better.

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.'

Applying this definition to our asset in some examples:
  • Compared with the benchmark SPY (10.2%) in the period of the last 5 years, the annual return (CAGR) of 18.7% of Automatic Data Processing is larger, thus better.
  • Looking at annual return (CAGR) in of 16.5% in the period of the last 3 years, we see it is relatively greater, thus better in comparison to SPY (10.5%).

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 volatility over 5 years of Automatic Data Processing is 27.7%, which is larger, thus worse compared to the benchmark SPY (20.9%) in the same period.
  • Looking at historical 30 days volatility in of 31.6% in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (24.1%).

DownVol:

'The downside volatility is similar to the volatility, or standard deviation, but only takes losing/negative periods into account.'

Which means for our asset as example:
  • Looking at the downside risk of 19.7% in the last 5 years of Automatic Data Processing, we see it is relatively higher, thus worse in comparison to the benchmark SPY (15.3%)
  • During the last 3 years, the downside volatility is 22.6%, which is larger, thus worse than the value of 17.6% from the benchmark.

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.'

Which means for our asset as example:
  • Looking at the risk / return profile (Sharpe) of 0.59 in the last 5 years of Automatic Data Processing, we see it is relatively higher, thus better in comparison to the benchmark SPY (0.37)
  • Looking at Sharpe Ratio in of 0.44 in the period of the last 3 years, we see it is relatively larger, thus better in comparison to SPY (0.33).

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.'

Applying this definition to our asset in some examples:
  • Looking at the excess return divided by the downside deviation of 0.82 in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus better in comparison to the benchmark SPY (0.51)
  • Looking at excess return divided by the downside deviation in of 0.62 in the period of the last 3 years, we see it is relatively larger, thus better in comparison to SPY (0.45).

Ulcer:

'The Ulcer Index is a technical indicator that measures downside risk, in terms of both the depth and duration of price declines. The index increases in value as the price moves farther away from a recent high and falls as the price rises to new highs. The indicator is usually calculated over a 14-day period, with the Ulcer Index showing the percentage drawdown a trader can expect from the high over that period. The greater the value of the Ulcer Index, the longer it takes for a stock to get back to the former high.'

Which means for our asset as example:
  • Compared with the benchmark SPY (7.71 ) in the period of the last 5 years, the Downside risk index of 9.93 of Automatic Data Processing is higher, thus worse.
  • Looking at Ulcer Index in of 12 in the period of the last 3 years, we see it is relatively higher, thus worse in comparison to SPY (9.08 ).

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 reduction from previous high of -39.5 days in the last 5 years of Automatic Data Processing, we see it is relatively smaller, thus worse in comparison to the benchmark SPY (-33.7 days)
  • Compared with SPY (-33.7 days) in the period of the last 3 years, the maximum reduction from previous high 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.'

Using this definition on our asset we see for example:
  • Compared with the benchmark SPY (189 days) in the period of the last 5 years, the maximum time in days below previous high water mark of 212 days of Automatic Data Processing is larger, thus worse.
  • Looking at maximum days below previous high in of 212 days in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (189 days).

AveDuration:

'The Drawdown Duration is the length of any peak to peak period, or the time between new equity highs. 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:
  • Looking at the average days below previous high of 46 days in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus worse in comparison to the benchmark SPY (46 days)
  • Compared with SPY (45 days) in the period of the last 3 years, the average days below previous high of 57 days is larger, thus worse.

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, 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.