Parameter Selection in Classification for Financial Market

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
Support Vector Machine: e.g., the optimal choice of Kernel Functions
Ensemble: e.g., the number of Learning Cycles for Bagging.
Discriminant Analysis: e.g., Linear/Quadratic; regularization choices for covariance matrix.
Naïve Bayes: e.g., Kernel choices; bandwidth selections.
K-nearest Neighbours: e.g., Distance metrics; k in kNN.
Decision Tree: e.g., Impurity measure choices; Tree Size Constraint.
Logistic Regression

In the following paper, we discuss in details how the parameterization choices are made in the context of Financial Market and the parameters are tuned in order to achieve optimal performance for each classifier mentioned above: the paper is available: https://ssrn.com/abstract=2967184 ; the presentation slide gives a summary: https://ssrn.com/abstract=2973065