A Dynamic Modeling and Validation Framework for the Market Direction Prediction

Xugang Ye and Jingjing Li
Additional contact information
Xugang Ye: Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
Jingjing Li: Leeds School of Business, University of Colorado, Boulder, CO, USA

International Journal of Business Analytics (IJBAN), 2015, vol. 2, issue 2, 1-13

Abstract: There are many research papers talking about building various machine learning models to predict the market index. However, very few attention has been paid to effectively validating or calibrating the prediction results. The focus of this paper is to present a dynamic modeling and validation framework for the market direction prediction. The central idea is to calibrate the probabilistic prediction by estimating two conditional probabilities of correct forecast from the dynamic validation data set. The calibration method can be combined with any predictive model that generates probabilistic prediction of the market direction.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJBAN.2015040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:2:y:2015:i:2:p:1-13

Access Statistics for this article

International Journal of Business Analytics (IJBAN) is currently edited by John Wang

More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().