Author:
Rodriguez F. S.,Norouzzadeh P.,Anwar Z.,Snir E.,Rahmani B.
Abstract
AbstractModels of the stock market often focus on predicting the direction of the stock market. Instead of following this approach, we created a model to predict the daily absolute percent change of the S&P 500. An accurate model of this metric would greatly increase profitability of option trading strategies such as straddles and iron condors. In this project, novel features were created based on historical data and fed to machine learning algorithms such as Decision Trees, Rule Based Classifiers, K-mean Clusters, and Kernels. Based on our findings, Decision Trees and Kernels showed an accuracy of 80% when predicting absolute percent change, while Rule Based Classifiers had an accuracy of 88%.
Publisher
Springer Science and Business Media LLC
Reference13 articles.
1. S&P Global. https://www.spglobal.com/spdji/en/indices/equity/sp-500/#overview.
2. Gidofalvi G, Elkan C. Using news articles to predict stock price movements. San Diego: University of California; 2001.
3. Bohl L, Frederick R. How to pick stocks using fundamental and technical analysis. Westlake: Schwab Brokerage; 2022.
4. Hayes A, Battle A, Jackson A. Technical Analysis: What it is and how to use it in investing Investopedia. Accessed 14 Mar 2022.
5. Jiao Y, Jakubowicz J. Predicting stock movement direction with machine learning: an extensive study on S&P 500 stocks. IEEE Int Conf Big Data. 2017. https://doi.org/10.1109/BigData.2017.8258518.