Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Application Of Support Vector Machine In Predicting The Market's Monthly Trend Direction, Ali Alali
Application Of Support Vector Machine In Predicting The Market's Monthly Trend Direction, Ali Alali
Dissertations and Theses
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 …
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
A Survey Of Systems For Predicting Stock Market Movements, Combining Market Indicators And Machine Learning Classifiers, Jeffrey Allan Caley
Dissertations and Theses
In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification …