Forecasting Stock Indexes Using Bayesian Additive Regression Tree

Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

• Darren Zou

• Bayesian
• Forecast
• Stock
• BART.

[1] Chipman et al., (2010), BART: Bayesian additive regression trees. Annuals of Applied Statistics. Volume 4, Number 1 (2010), 266-298.
[2] Tan et al., (2019), Bayesian additive regression trees and the General BART model. Statistics in Medicine. Volume38, Issue25, 10 November 2019, Pages 5048-5069
[3] Kapelner et al., (2016), bartMachine: Machine Learning with Bayesian Additive Regression Trees. Journal of Statistical Software. 10.18637/jss.v070.i04
[4] Geirsson et al., (2017), Parallel Bayesian Additive Regression Trees, using Apache Spark. Computer Science.
[5] Lakshminarayanan et al., (2016), Particle Gibbs for Bayesian Additive Regression Trees. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA. JMLR: WCP
[6] Hastie et al., (2000) Bayesian Backfitting, Statistical Science, Volume 15, Number 3, 196-223.
[7] Fred Economic Data, “Economic Research”, https://fred.stlouisfed.org, accessed on 17 Apr 2020. volume 38