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
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DOI: 10.1007/s42001-019-00057-5
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- Gili Yen & Cheng-few Lee, 2008. "Efficient Market Hypothesis (EMH): Past, Present and Future," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 305-329.
- Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
- repec:adr:anecst:y:1995:i:40:p:04 is not listed on IDEAS
- Wei Huang & K. K. Lai & Y. Nakamori & Shouyang Wang, 2004. "Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 145-165.
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
- Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
- C. W. J. Granger & Zhuanxin Ding, 1995. "Some Properties of Absolute Return: An Alternative Measure of Risk," Annals of Economics and Statistics, GENES, issue 40, pages 67-91.
- Timmermann, Allan & Granger, Clive W. J., 2004.
"Efficient market hypothesis and forecasting,"
International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
- Timmermann, Allan & Granger, Clive, 2002. "Efficient Market Hypothesis and Forecasting," CEPR Discussion Papers 3593, C.E.P.R. Discussion Papers.
- Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
- repec:pri:cepsud:91malkiel is not listed on IDEAS
- Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
- Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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Cited by:
- Guangji Tong & Zhiwei Yin, 2022. "Adaptive Trading System of Assets for International Cooperation in Agricultural Finance Based on Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1557-1576, April.
- Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
- Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
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Keywords
Financial time series forecasting; Prediction; Machine learning; Temporal analysis;All these keywords.
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