MongoDB (MDB)

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.'

• Looking at the total return, or performance of 100.4% in the last 5 years of MongoDB, we see it is relatively larger, thus better in comparison to the benchmark SPY (96.2%)
• Compared with SPY (24.7%) in the period of the last 3 years, the total return, or increase in value of -44.4% is lower, thus worse.

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.'

• Looking at the annual return (CAGR) of 14.9% in the last 5 years of MongoDB, we see it is relatively greater, thus better in comparison to the benchmark SPY (14.4%)
• During the last 3 years, the compounded annual growth rate (CAGR) is -17.8%, which is smaller, thus worse than the value of 7.6% from the benchmark.

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 MongoDB is 67.3%, which is larger, thus worse compared to the benchmark SPY (20.9%) in the same period.
• Looking at 30 days standard deviation in of 71.4% in the period of the last 3 years, we see it is relatively higher, thus worse in comparison to SPY (17.6%).

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.'

• The downside risk over 5 years of MongoDB is 44.7%, which is greater, thus worse compared to the benchmark SPY (14.9%) in the same period.
• Looking at downside volatility in of 48.5% in the period of the last 3 years, we see it is relatively higher, thus worse in comparison to SPY (12.4%).

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.'

• Compared with the benchmark SPY (0.57) in the period of the last 5 years, the ratio of return and volatility (Sharpe) of 0.18 of MongoDB is lower, thus worse.
• During the last 3 years, the ratio of return and volatility (Sharpe) is -0.28, which is lower, thus worse than the value of 0.29 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.'

• Compared with the benchmark SPY (0.8) in the period of the last 5 years, the ratio of annual return and downside deviation of 0.28 of MongoDB is smaller, thus worse.
• Compared with SPY (0.41) in the period of the last 3 years, the downside risk / excess return profile of -0.42 is lower, thus worse.

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.'

• Looking at the Ulcer Ratio of 37 in the last 5 years of MongoDB, we see it is relatively greater, thus worse in comparison to the benchmark SPY (9.32 )
• Looking at Downside risk index in of 46 in the period of the last 3 years, we see it is relatively higher, thus worse in comparison to SPY (10 ).

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.'

• Compared with the benchmark SPY (-33.7 days) in the period of the last 5 years, the maximum reduction from previous high of -76.5 days of MongoDB is lower, thus worse.
• During the last 3 years, the maximum drop from peak to valley is -76.5 days, which is lower, thus worse than the value of -24.5 days from the benchmark.

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.'

• The maximum time in days below previous high water mark over 5 years of MongoDB is 704 days, which is larger, thus worse compared to the benchmark SPY (488 days) in the same period.
• Looking at maximum time in days below previous high water mark in of 704 days in the period of the last 3 years, we see it is relatively greater, thus worse in comparison to SPY (488 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.'

• The average time in days below previous high water mark over 5 years of MongoDB is 220 days, which is larger, thus worse compared to the benchmark SPY (123 days) in the same period.
• Looking at average time in days below previous high water mark in of 333 days in the period of the last 3 years, we see it is relatively larger, thus worse in comparison to SPY (177 days).