Analysis of Frequent Trading Effects of Various Machine Learning Models
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DOI: 10.1007/s10614-024-10611-7
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- Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017.
"Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,"
European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
- Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2016. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," FAU Discussion Papers in Economics 03/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
- Christopher Krauss & Xuan Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01515120, HAL.
- Yaohu Lin & Shancun Liu & Haijun Yang & Harris Wu & Bingbing Jiang, 2021. "Improving stock trading decisions based on pattern recognition using machine learning technology," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.
- Hakob GRIGORYAN, 2016. "A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 7(1), pages 12-21, August.
- Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
- B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
- Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
- Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
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Keywords
High-frequency trading; Mathematical model; Logistic regression algorithm; Fully Connected Neural Network model; Support vector machine; Stock data;All these keywords.
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