A Dynamic Modeling and Validation Framework for the Market Direction Prediction

View Sample PDF

Author(s): Xugang Ye (Johns Hopkins University, USA)and Jingjing Li (University of Colorado, USA)
Copyright: 2017
Pages: 16
Source title: Artificial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1759-7.ch015

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

Purchase

View A Dynamic Modeling and Validation Framework for the Market Direction Prediction on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

A Dynamic Modeling and Validation Framework for the Market Direction Prediction

Purchase

Abstract

Related Content

IRMA Sponsors