Portland State University. Department of Electrical and Computer Engineering
Date of Publication
Master of Science (M.S.) in Electrical and Computer Engineering
Electrical and Computer Engineering
Support vector machines, Stock index futures -- Computer simulation, Machine learning -- Mathematical models
1 online resource (ix, 94 pages)
In this work, we investigate different techniques to predict the monthly trend direction of the S&P 500 market index. The techniques use a machine learning classifier with technical and macroeconomic indicators as input features. The Support Vector Machine (SVM) classifier was explored in-depth in order to optimize the performance using four different kernels; Linear, Radial Basis Function (RBF), Polynomial, and Quadratic. A result found was the performance of the classifier can be optimized by reducing the number of macroeconomic features needed by 30% using Sequential Feature Selection. Further performance enhancement was achieved by optimizing the RBF kernel and SVM parameters through gridsearch. This resulted in final classification accuracy rates of 62% using technical features alone with gridsearch and 60.4% using macroeconomic features alone using Rankfeatures
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Alali, Ali, "Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction" (2013). Dissertations and Theses. Paper 1496.