Feature selection for stock trend prediction via support vector machine

• Main Author: Ng, Ivan Wei Jun.
• Other Authors: Ong Yew Soon
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering
• Online Access: http://hdl.handle.net/10356/52072
• Institution: Nanyang Technological University

Stock market is a highly complex and non-linear dynamic system. Successful predictions in the stock market could bring in significant profits. However, prediction of the stock trend remains unresolved due to its complexity. Technical analysis is the analysis of securities by means of studying statistics generated by past market data, such as past prices and volume. These data generated were used as the input variables. Support Vector Machine is a supervised learning model, which will be used to analyze and classify data into the respective patterns identified. The aim of this project is to apply the linear Support Vector Machines strategy of feature selection to select the highest scoring feature. Once the feature set is determined, the model is used on the full training data. The resulting training model will then be used on the testing data to forecast the stock trend signal.