Description of Automatic Data Processing

Automatic Data Processing, Inc. - Common Stock

Statistics of Automatic Data Processing (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.'

Using this definition on our asset we see for example:
  • The total return over 5 years of Automatic Data Processing is 163.4%, which is greater, thus better compared to the benchmark SPY (66.2%) in the same period.
  • Compared with SPY (47.5%) in the period of the last 3 years, the total return of 102.4% is larger, thus better.

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.'

Which means for our asset as example:
  • The compounded annual growth rate (CAGR) over 5 years of Automatic Data Processing is 21.4%, which is larger, thus better compared to the benchmark SPY (10.7%) in the same period.
  • Looking at compounded annual growth rate (CAGR) in of 26.6% in the period of the last 3 years, we see it is relatively higher, thus better in comparison to SPY (13.9%).

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.'

Applying this definition to our asset in some examples:
  • Looking at the volatility of 18.9% in the last 5 years of Automatic Data Processing, we see it is relatively higher, thus worse in comparison to the benchmark SPY (13.3%)
  • Compared with SPY (12.5%) in the period of the last 3 years, the volatility of 19.7% is larger, 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:
  • Compared with the benchmark SPY (14.6%) in the period of the last 5 years, the downside volatility of 20.5% of Automatic Data Processing is greater, thus worse.
  • Compared with SPY (14.2%) in the period of the last 3 years, the downside risk of 22.1% is higher, thus worse.

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:
  • The risk / return profile (Sharpe) over 5 years of Automatic Data Processing is 1, which is greater, thus better compared to the benchmark SPY (0.62) in the same period.
  • Compared with SPY (0.91) in the period of the last 3 years, the ratio of return and volatility (Sharpe) of 1.22 is greater, thus better.

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.'

Which means for our asset as example:
  • Compared with the benchmark SPY (0.56) in the period of the last 5 years, the ratio of annual return and downside deviation of 0.92 of Automatic Data Processing is larger, thus better.
  • Looking at downside risk / excess return profile in of 1.09 in the period of the last 3 years, we see it is relatively larger, thus better in comparison to SPY (0.8).

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 Ratio of 5.34 in the last 5 years of Automatic Data Processing, we see it is relatively larger, thus better in comparison to the benchmark SPY (3.96 )
  • During the last 3 years, the Ulcer Ratio is 5.29 , which is higher, thus better than the value of 4.01 from the benchmark.

MaxDD:

'Maximum drawdown measures the loss in any losing period during a fund’s investment record. It is defined as the percent retrenchment from a fund’s peak value to the fund’s valley value. The drawdown is in effect from the time the fund’s retrenchment begins until a new fund high is reached. The maximum drawdown encompasses both the period from the fund’s peak to the fund’s valley (length), and the time from the fund’s valley to a new fund high (recovery). It measures the largest percentage drawdown that has occurred in any fund’s data record.'

Which means for our asset as example:
  • The maximum reduction from previous high over 5 years of Automatic Data Processing is -19.2 days, which is larger, thus better compared to the benchmark SPY (-19.3 days) in the same period.
  • Looking at maximum DrawDown in of -19.2 days in the period of the last 3 years, we see it is relatively greater, thus better in comparison to SPY (-19.3 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.'

Using this definition on our asset we see for example:
  • Looking at the maximum time in days below previous high water mark of 163 days in the last 5 years of Automatic Data Processing, we see it is relatively smaller, thus better in comparison to the benchmark SPY (187 days)
  • Compared with SPY (139 days) in the period of the last 3 years, the maximum days below previous high of 95 days is lower, thus better.

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.'

Which means for our asset as example:
  • Compared with the benchmark SPY (41 days) in the period of the last 5 years, the average days under water of 36 days of Automatic Data Processing is lower, thus better.
  • Compared with SPY (36 days) in the period of the last 3 years, the average days below previous high of 28 days is lower, thus better.

Performance of Automatic Data Processing (YTD)

Historical returns have been extended using synthetic data.

Allocations of Automatic Data Processing
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Allocations

Returns of Automatic Data Processing (%)

  • "Year" returns in the table above are not equal to 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.