Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500

This article conducts a systematic comparison of three methods for predicting the direction (+/−) of financial time series using over ten years of DAX 30 and the S&P 500 datasets at daily and hourly frames. We choose the methods from representative machine learning families, particularly supervi...

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Bibliographic Details
Published in:Journal of Computational Social Science 2020-04, Vol.3 (1), p.103-133
Main Authors: Ersan, Deniz, Nishioka, Chifumi, Scherp, Ansgar
Format: Article
Language:English
Subjects:
Big Data/Analytics
Complex Systems
Computational Linguistics
Data
Financial time series forecasting
Forecasting
Machine learning
Machinery
Neural networks
Prediction
Predictions
Principal components analysis
Research Article
Simulation and Modeling
Social Media
Social networks
Social Sciences
Support vector machines
Temporal analysis
Time series
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