Implementing Machine Learning Techniques in Financial Modeling
Arts and Sciences
The data science competition forum Kaggle, in conjunction with Two Sigma, proposed a financial modeling competition open to the public. The challenge is to predict an anonymous time-varying financial instrument based on anonymous features given in the dataset. To accomplish this task, we will demonstrate several machine learning techniques and show how well they perform in the prediction of the class variable. These techniques include Ridge Regression, Extreme Gradient Boosting, and Extremely Randomized Trees. We will review each of the techniques, and then show the results of how they worked independently and together.
Arloff, William D., "Implementing Machine Learning Techniques in Financial Modeling" (2017). Symposium on Undergraduate Research and Creative Expression (SOURCE). 610.
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