Error Objective Functions

In addition to using as the prediction objective you may also use Trading Objective Functions.
Minimize Error – This objective function minimizes the average of the absolute value of the differences between the prediction and the actual value.
Maximize Correlation – This objective function maximizes the correlation between the prediction and the actual value. The correlation is a measure of linear correlation between the prediction and the actual. The closer the correlation is to one, the stronger the positive correlation. The closer the correlation is to negative one the stronger the negative correlation. A value of zero represents no correlation between the prediction and the actual. When optimizing, NeuroShell Trader maximizes the absolute value of the correlation. During optimization, the maximum correlation value may flip between positive and negative values as the maximum absolute value is improved.
Maximize R-Squared – This objective function maximizes the R-Squared value between the prediction and the actual value. The R-Squared value is a statistical measure usually applied to multiple regression analysis. It compares the accuracy of the prediction to the accuracy of the mean of all of the samples (a trivial benchmark model). A perfect fit would result in an R squared value of 1, a very good fit near 1, and a very poor fit near 0. If you neural model predictions are worse than you could predict by just using the mean of your sample case outputs, the R squared value will be negative.
Do not confuse R squared with r squared, where r is the correlation coefficient. They are two different measures with different formulas. Although the result is the same value with linear regression analysis, they are not the same in non-linear neural networks.
Minimize Mean Squared Error – This objective function minimizes the Mean Squared Error between the prediction and the actual value. The Mean Squared Error is the average of the squared value of the error between the prediction and the actual. This more heavily penalizes any error that is larger than the average.
Maximize % Correct Sign – This objective function maximizes the percentage of times the prediction correctly predicts the sign of the actual value. This is useful when predicting values that oscillate around zero (i.e. Percent Change or Change). This objective function presumes that if you can predict the direction of the actual value you will be able to make money because the trade is going to be profitable, even though you might not predict the size of the movement accurately.
 

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