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Downside risk was first modeled by Roy (1952), who assumed that an investor's goal was to minimize his/her risk. This mean-semivariance, or downside risk, model is also known as “safety-first” technique, and only looks at the lower standard deviations of expected returns which are the potential losses.
Downside risk (DR) is measured by target semi-deviation (the square root of target semivariance) and is termed downside deviation. It is expressed in percentages and therefore allows for rankings in the same way as standard deviation. An intuitive way to view downside risk is the annualized standard deviation of returns below the target.
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The sample information for example could be concentration of iron in soil samples, or pixel intensity on a camera. Each piece of sample information has coordinates s = ( x , y ) {\displaystyle \mathbf {s} =(x,y)} for a 2D sample space where x {\displaystyle x} and y {\displaystyle y} are geographical coordinates.
In cybernetics and control theory, a setpoint (SP; [1] also set point) is the desired or target value for an essential variable, or process value (PV) of a control system, [2] which may differ from the actual measured value of the variable.
The Sortino ratio measures the risk-adjusted return of an investment asset, portfolio, or strategy. [1] 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.
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The semiorder defined from a utility function may equivalently be defined as the interval order defined by the intervals [(), +], [8] so every semiorder is an example of an interval order. A relation is a semiorder if, and only if, it can be obtained as an interval order of unit length intervals ( ℓ i , ℓ i + 1 ) {\displaystyle (\ell _{i ...