An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms-Reference-Cited by-同舟云学术

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An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms

Published:2020-03 Issue: Volume:541 Page:122272
ISSN:0378-4371
Container-title:Physica A: Statistical Mechanics and its Applications
language:en
Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Jiang Minqi,Liu Jiapeng,Zhang Lu,Liu Chunyu

Funder

National Social Science Foundation of China

soft science project of Zhejiang Province

Natural Science Foundation of Zhejiang Province

key humanities and social science projects in Zhejiang Province university

Publisher

Elsevier BV

Subject

Condensed Matter Physics,Statistics and Probability

Reference47 articles.

1. Forecasting stock indices: a comparison of classification and level estimation models;Leung;Int. J. Forecast.,2000

2. Evaluating multiple classifiers for stock price direction prediction;Ballings;Expert Syst. Appl.,2015

3. Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest;Kumar,2006

4. Evaluating the employment of technical indicators in predicting stock price index variations using artificial neural networks (Case Study: Tehran Stock Exchange);Moein Aldin;Int. J. Bus. Manage.,2012

5. Using percentage accuracy to measure neural network predictions in Stock Market movements;Brownstone;Neurocomputing,1996

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