Table 14 Operators and functions in the formulaic expressions
From: Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies
Operator and function | Description | Type |
|---|---|---|
+, −, *, /, ^ | Add, subtract, multiply, divide, power | |
\({\rm Correlation}(x, y, n)\) | Correlation of the variables \(x\) and \(y\) for the past \(n\) days | Scalar |
\({\rm Covariance}(x, y, n)\) | Covariance of the variables \(x\) and \(y\) for the past \(n\) days | Scalar |
\({\rm Delay}(x, n)\) | x value of \(n\) days ago | Scalar |
\({\rm Delta}(x, n)\) | x value of current day minus its value of \(n\) days ago | Scalar |
\({\rm Rank}(x)\) | Rank value of the variable \(x\) of all the stocks and the achieved rank value is transformed into the range between 0.0 and 1.0. For example, \({\rm Rank}\left([20.2, 15.6, 10.0, 5.7, 50.2, 18.4]\right)\) is [0.8, 0.4, 0.2, 0.0, 1.0, 0.6] | Vector |
\({\rm Sign}(x)\) | 1 if x > 0, − 1 if x < 0, and 0 if x = 0 | Scalar |
\({\rm Std}(x, n)\) | Standard deviation of the variable x for the past n days | Scalar |
\({\rm Ts}\_{\rm Rank}(x, n)\) | Rank the values of the variable x over the past d days and then all the rank values are transformed into the range between 0.0 and 1.0. Finally, the rank value of the variable x in current day is returned | Scalar |
\({\rm Max}(x, n)\) | The maximum value of the variable x over the past d days | Scalar |
\({\rm Min}(x, n)\) | The minimum value of the variable x over the past d days | Scalar |