Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500-Reference-Cited by-同舟云学术

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Published:2017-06 Issue:2 Volume:259 Page:689-702
ISSN:0377-2217
Container-title:European Journal of Operational Research
language:en
Short-container-title:European Journal of Operational Research

Author:

Krauss Christopher,Do Xuan Anh,Huck Nicolas

Publisher

Elsevier BV

Subject

Information Systems and Management,Management Science and Operations Research,Modeling and Simulation,General Computer Science,Industrial and Manufacturing Engineering

Reference69 articles.

1. Persistence in forecasting performance and conditional combination strategies;Aiolfi;Journal of Econometrics,2006

2. Searching for solutions in games and artificial intelligence;Allis,1994

3. Surveying stock market forecasting techniques – Part II: Soft computing methods;Atsalakis;Expert Systems with Applications,2009

4. Statistical arbitrage in the US equities market;Avellaneda;Quantitative Finance,2010

5. Practical portfolio performance: Measurement and attribution;Bacon,2008

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