Which classifier has the best performance?

In practice, given a wide range of classifiers, we often have to choose the one based on performance comparison through validation. Research literature shows that there is no classifier that performs universally best in all contexts for all problems. The following paper applied 8 most popular classifiers (e.g., SVM, Neural Net, Ensemble, KNN, Decision Tree, Logistic Regression, Discriminant Analysis, Naive Bayes, etc.) in Machine Learning arena to solve a problem currently confronting finance institutions such as banks, insurers, asset managers in their derivative valuation and risk management. The paper shows that when properly parameterized (which the paper discusses in details), the performances are either consistent with or contrary to some classic studies in the area. The paper is available at SSRN.