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 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.'

Which means for our asset as example:
  • The total return, or increase in value over 5 years of Automatic Data Processing is 129.4%, which is larger, thus better compared to the benchmark SPY (109.8%) in the same period.
  • Compared with SPY (42.5%) in the period of the last 3 years, the total return, or increase in value of 49.9% 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.'

Using this definition on our asset we see for example:
  • Compared with the benchmark SPY (16%) in the period of the last 5 years, the annual performance (CAGR) of 18.1% of Automatic Data Processing is higher, thus better.
  • Looking at annual return (CAGR) in of 14.5% in the period of the last 3 years, we see it is relatively higher, thus better in comparison to SPY (12.6%).

Volatility:

'Volatility is a rate at which the price of a security increases or decreases for a given set of returns. Volatility is measured by calculating the standard deviation of the annualized returns over a given period of time. It shows the range to which the price of a security may increase or decrease. Volatility measures the risk of a security. It is used in option pricing formula to gauge the fluctuations in the returns of the underlying assets. Volatility indicates the pricing behavior of the security and helps estimate the fluctuations that may happen in a short period of time.'

Which means for our asset as example:
  • The historical 30 days volatility over 5 years of Automatic Data Processing is 22.7%, which is greater, thus worse compared to the benchmark SPY (17.9%) in the same period.
  • During the last 3 years, the volatility is 21.3%, which is larger, thus worse than the value of 18.4% from the benchmark.

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.'

Applying this definition to our asset in some examples:
  • Looking at the downside deviation of 16.1% in the last 5 years of Automatic Data Processing, we see it is relatively greater, thus worse in comparison to the benchmark SPY (12.5%)
  • Compared with SPY (12.6%) in the period of the last 3 years, the downside deviation of 14.8% is greater, thus worse.

Sharpe:

'The Sharpe ratio (also known as the Sharpe index, the Sharpe measure, and the reward-to-variability ratio) is a way to examine the performance of an investment by adjusting for its risk. The ratio measures the excess return (or risk premium) per unit of deviation in an investment asset or a trading strategy, typically referred to as risk, named after William F. Sharpe.'

Applying this definition to our asset in some examples:
  • Compared with the benchmark SPY (0.75) in the period of the last 5 years, the risk / return profile (Sharpe) of 0.69 of Automatic Data Processing is smaller, thus worse.
  • During the last 3 years, the Sharpe Ratio is 0.56, which is higher, thus better than the value of 0.55 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.'

Applying this definition to our asset in some examples:
  • The excess return divided by the downside deviation over 5 years of Automatic Data Processing is 0.97, which is smaller, thus worse compared to the benchmark SPY (1.08) in the same period.
  • During the last 3 years, the ratio of annual return and downside deviation is 0.81, which is higher, thus better than the value of 0.8 from the benchmark.

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.'

Using this definition on our asset we see for example:
  • Looking at the Ulcer Ratio of 9.37 in the last 5 years of Automatic Data Processing, we see it is relatively greater, thus worse in comparison to the benchmark SPY (8.48 )
  • Looking at Downside risk index in of 9.64 in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (5.54 ).

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 (-24.5 days) in the period of the last 5 years, the maximum DrawDown of -21.8 days of Automatic Data Processing is greater, thus better.
  • Looking at maximum DrawDown in of -21.8 days in the period of the last 3 years, we see it is relatively lower, thus worse in comparison to SPY (-18.8 days).

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 time in days below previous high water mark of 414 days in the last 5 years of Automatic Data Processing, we see it is relatively lower, thus better in comparison to the benchmark SPY (488 days)
  • During the last 3 years, the maximum days below previous high is 414 days, which is greater, thus worse than the value of 199 days from the benchmark.

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 time in days below previous high water mark over 5 years of Automatic Data Processing is 97 days, which is smaller, thus better compared to the benchmark SPY (119 days) in the same period.
  • Looking at average days under water in of 132 days in the period of the last 3 years, we see it is relatively larger, thus worse in comparison to SPY (44 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.