Method for Improving the Performance of Technical Analysis Indicators By Neural Network Models
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DOI: 10.1007/s10614-021-10116-7
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References listed on IDEAS
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014.
"Forecasting the Equity Risk Premium: The Role of Technical Indicators,"
Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
- Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2011. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Working Papers CoFie-02-2011, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
- O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
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- Yanbo Zhang & Mengkun Liang & Haiying Ou, 2024. "Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3243-3278, December.
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Keywords
Technical analysis indicators; Neural network models; Quantitative investment;All these keywords.
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