Portland State University. Department of Electrical and Computer Engineering
Date of Award
Master of Science (M.S.) in Electrical and Computer Engineering
Electrical and Computer Engineering
1 online resource (ix, 94 pages)
Support vector machines, Stock index futures -- Computer simulation, Machine learning -- Mathematical models
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
Alali, Ali, "Application of Support Vector Machine in Predicting the Market's Monthly Trend Direction" (2013). Dissertations and Theses. Paper 1496.