An ensemble of LSTM neural networks for high‐frequency stock market classification
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DOI: 10.1002/for.2585
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Cited by:
- Adam Korniejczuk & Robert Ślepaczuk, 2024.
"Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market,"
Working Papers
2024-09, Faculty of Economic Sciences, University of Warsaw.
- Adam Korniejczuk & Robert 'Slepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Papers 2406.10695, arXiv.org.
- Gil Cohen, 2022. "Artificial Intelligence in Trading the Financial Markets," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 101-110.
- Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
- Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
- Hussain, Syed Mujahid & Ahmad, Nisar & Ahmed, Sheraz, 2023. "Applications of high-frequency data in finance: A bibliometric literature review," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
- Vecchi, Edoardo & Berra, Gabriele & Albrecht, Steffen & Gagliardini, Patrick & Horenko, Illia, 2023. "Entropic approximate learning for financial decision-making in the small data regime," Research in International Business and Finance, Elsevier, vol. 65(C).
- Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
- Jianyuan Zhong & Zhijian Xu & Saizhuo Wang & Xiangyu Wen & Jian Guo & Qiang Xu, 2024. "DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction," Papers 2405.15833, arXiv.org.
- Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Cohen, Gil & Aiche, Avishay, 2023. "Forecasting gold price using machine learning methodologies," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
- Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
- Gil, Cohen, 2022. "Intraday Trading of Precious Metals Futures Using Algorithmic Systems," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
- Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
- Firuz Kamalov & Linda Smail & Ikhlaas Gurrib, 2021. "Stock price forecast with deep learning," Papers 2103.14081, arXiv.org.
- Firuz Kamalov, 2019. "Forecasting significant stock price changes using neural networks," Papers 1912.08791, arXiv.org.
- Chang Liu & Sandra Paterlini, 2023. "Stock Price Prediction Using Temporal Graph Model with Value Chain Data," Papers 2303.09406, arXiv.org.
- Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
- Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li, 2022. "Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1458-1482, November.
- Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
- Chu Myaet Thwal & Ye Lin Tun & Kitae Kim & Seong-Bae Park & Choong Seon Hong, 2024. "Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting," Papers 2402.06638, arXiv.org.
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