Document Type
Dissertation
Rights
This item is available under a Creative Commons License for non-commercial use only
Abstract
Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market ”housing crash” of 2008 . The model utilized technical indicators as the respective parameters to assist in predicting the stock price movement when determining the performance of each model. Despite the No Free Lunch Theorem stating one model can not out perform another model, the study displayed higher accuracy for the RF model. Each model was evaluated using the confusion metrics to calculate the precision, recall, and F1 score.
DOI
https://doi.org/10.21427/D7HV40
Recommended Citation
Razy, Tiffany (2018). Stock direction within different sectors in a bull and bear market. Masters dissertation, DIT, 2018.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computing (Stream), January 2018.