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A Guide for Applying Machine Learning Techniques in Finance

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in K-nearest Neighbour, one supporting so-called square-root of sample size N method, another talking about selecting K based on how well the classifier performs according to its cross-validation samples. The parameterization choices have signficant impacts on the performances of classifiers; so it’s important to get them right. Parameterized differently, as shown in the paper below, the performances of each of the 8 most popular classification algorithms can be significantly different.

The following 51-page paper introduces 8 most popular classifiers in Machine Learning and illustrates each with an example based on financial data from real world. It can serve as a guide for how to apply Machine Learning Techniques to solve problems faced by finance industry: https://ssrn.com/abstract=2967184.

Please see the presentation slides that present a summary of classification techniques used in finance industry: https://ssrn.com/abstract=2973065.