Automatic Data Processing (ADP)

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.'

• The total return, or increase in value over 5 years of Automatic Data Processing is 89.4%, which is greater, thus better compared to the benchmark SPY (59.2%) in the same period.
• Looking at total return in of 52.2% in the period of the last 3 years, we see it is relatively greater, thus better in comparison to SPY (33.1%).

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.'

• Compared with the benchmark SPY (9.7%) in the period of the last 5 years, the annual return (CAGR) of 13.6% of Automatic Data Processing is larger, thus better.
• Looking at annual performance (CAGR) in of 15.1% in the period of the last 3 years, we see it is relatively higher, thus better in comparison to SPY (10%).

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.'

• The historical 30 days volatility over 5 years of Automatic Data Processing is 25.6%, which is greater, thus worse compared to the benchmark SPY (18.7%) in the same period.
• Looking at historical 30 days volatility in of 29.4% in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (21.5%).

DownVol:

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

• Compared with the benchmark SPY (13.6%) in the period of the last 5 years, the downside volatility of 18.3% of Automatic Data Processing is higher, thus worse.
• Compared with SPY (15.7%) in the period of the last 3 years, the downside risk of 20.9% is greater, 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.'

• Looking at the Sharpe Ratio of 0.44 in the last 5 years of Automatic Data Processing, we see it is relatively higher, thus better in comparison to the benchmark SPY (0.39)
• Looking at Sharpe Ratio in of 0.43 in the period of the last 3 years, we see it is relatively greater, thus better in comparison to SPY (0.35).

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.'

• The excess return divided by the downside deviation over 5 years of Automatic Data Processing is 0.61, which is greater, thus better compared to the benchmark SPY (0.53) in the same period.
• During the last 3 years, the ratio of annual return and downside deviation is 0.6, which is larger, thus better than the value of 0.48 from the benchmark.

Ulcer:

'The ulcer index is a stock market risk measure or technical analysis indicator devised by Peter Martin in 1987, and published by him and Byron McCann in their 1989 book The Investors Guide to Fidelity Funds. It's designed as a measure of volatility, but only volatility in the downward direction, i.e. the amount of drawdown or retracement occurring over a period. Other volatility measures like standard deviation treat up and down movement equally, but a trader doesn't mind upward movement, it's the downside that causes stress and stomach ulcers that the index's name suggests.'

• Compared with the benchmark SPY (5.79 ) in the period of the last 5 years, the Downside risk index of 7.32 of Automatic Data Processing is larger, thus worse.
• Compared with SPY (6.83 ) in the period of the last 3 years, the Ulcer Index of 8.43 is larger, thus worse.

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.'

• 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.
• During the last 3 years, the maximum reduction from previous high is -39.5 days, which is lower, thus worse than the value of -33.7 days from the benchmark.

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). Many assume Max DD Duration is the length of time between new highs during which the Max DD (magnitude) occurred. But that isn’t always the case. The Max DD duration is the longest time between peaks, period. So it could be the time when the program also had its biggest peak to valley loss (and usually is, because the program needs a long time to recover from the largest loss), but it doesn’t have to be'

• Compared with the benchmark SPY (187 days) in the period of the last 5 years, the maximum time in days below previous high water mark of 105 days of Automatic Data Processing is smaller, thus better.
• Compared with SPY (139 days) in the period of the last 3 years, the maximum time in days below previous high water mark 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.'

• Looking at the average days below previous high of 33 days in the last 5 years of Automatic Data Processing, we see it is relatively smaller, thus better in comparison to the benchmark SPY (42 days)
• Looking at average time in days below previous high water mark in of 30 days in the period of the last 3 years, we see it is relatively lower, thus better in comparison to SPY (38 days).