Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine le...

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Veröffentlicht in:	Financial innovation (Heidelberg) Jg. 5; H. 1; S. 1 - 20
Hauptverfasser:	Zhong, Xiao, Enke, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Heidelberg Springer 15.06.2019 Springer Berlin Heidelberg Springer Nature B.V SpringerOpen
Schlagworte:	Algorithms Artificial intelligence Big Data Classification Daily stock return forecasting Data representation Datasets Deep neural networks (DNNs) Economics Economics and Finance Hybrid machine learning algorithms Hypothesis testing Machine learning Macroeconomics/Monetary Economics//Financial Economics Neural networks Political Economy/Economic Systems Principal components analysis Return direction classification Securities markets Stock market indexes Trading strategies Data representation Return direction classification Hybrid machine learning algorithms Trading strategies Daily stock return forecasting Deep neural networks (DNNs)
ISSN:	2199-4730, 2199-4730
Online-Zugang:	Volltext
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