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Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019

Citations

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Cited by:

  1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
  2. Jia Xu & Longbing Cao, 2023. "Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling," Papers 2305.08778, arXiv.org.
  3. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
  4. Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
  5. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  6. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
  7. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
  8. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
  9. Jean Jacques Ohana & Eric Benhamou & David Saltiel & Beatrice Guez, 2021. "Is the Covid equity bubble rational? A machine learning answer," Working Papers hal-03189799, HAL.
  10. 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.
  11. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
  12. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
  13. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
  14. Grilli, Luca & Santoro, Domenico, 2020. "Generative Adversarial Network for Market Hourly Discrimination," MPRA Paper 99846, University Library of Munich, Germany.
  15. Rian Dolphin & Barry Smyth & Ruihai Dong, 2022. "Stock Embeddings: Learning Distributed Representations for Financial Assets," Papers 2202.08968, arXiv.org.
  16. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
  17. Chao Deng & Liang Ma & Taishan Zeng, 2021. "Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
  18. Anika Kanwal & Siva Chandrasekaran, 2022. "2dCNN-BiCuDNNLSTM: Hybrid Deep-Learning-Based Approach for Classification of COVID-19 X-ray Images," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
  19. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
  20. Thibaut Théate & Sébastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Energies, MDPI, vol. 13(23), pages 1-17, December.
  21. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  22. Xianfei Hui & Baiqing Sun & Indranil SenGupta & Yan Zhou & Hui Jiang, 2022. "Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning," Papers 2204.02891, arXiv.org, revised Jan 2023.
  23. Berthine Nyunga Mpinda & Jules Sadefo-Kamdem & Salomey Osei & Jeremiah Fadugba, 2021. "Accuracies of Model Risks in Finance using Machine Learning," Working Papers hal-03191437, HAL.
  24. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).
  25. Katsuya Ito & Kentaro Minami & Kentaro Imajo & Kei Nakagawa, 2020. "Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction," Papers 2012.10215, arXiv.org.
  26. Manu Centeno-Telleria & Ekaitz Zulueta & Unai Fernandez-Gamiz & Daniel Teso-Fz-Betoño & Adrián Teso-Fz-Betoño, 2021. "Differential Evolution Optimal Parameters Tuning with Artificial Neural Network," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
  27. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
  28. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
  29. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
  30. Daehyeon PARK & Doojin RYU, 2021. "Forecasting Stock Market Dynamics using Bidirectional Long Short-Term Memory," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 22-34, June.
  31. Muhammad Aslam & Jae-Myeong Lee & Mustafa Raed Altaha & Seung-Jae Lee & Sugwon Hong, 2020. "AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System," Energies, MDPI, vol. 13(17), pages 1-14, August.
  32. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
  33. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
  34. Uribarri, Gonzalo & Mindlin, Gabriel B., 2022. "Dynamical time series embeddings in recurrent neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
  35. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
  36. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
  37. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
  38. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
  39. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
  40. Qingwen Li & Guangxi Yan & Chengming Yu, 2022. "A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
  41. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
  42. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
  43. Zheng, Ling & Zhou, Bin & Or, Siu Wing & Cao, Yijia & Wang, Huaizhi & Li, Yong & Chan, Ka Wing, 2021. "Spatio-temporal wind speed prediction of multiple wind farms using capsule network," Renewable Energy, Elsevier, vol. 175(C), pages 718-730.
  44. Yong Zhang & Hong Lin & Lina Zheng & Xingyu Yang, 2022. "Adaptive online portfolio strategy based on exponential gradient updates," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 672-696, April.
  45. Xiao Yang & Weiqing Liu & Dong Zhou & Jiang Bian & Tie-Yan Liu, 2020. "Qlib: An AI-oriented Quantitative Investment Platform," Papers 2009.11189, arXiv.org.
  46. Liu, Gang & Wang, Kun & Hao, Xiaochen & Zhang, Zhipeng & Zhao, Yantao & Xu, Qingquan, 2022. "SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system," Energy, Elsevier, vol. 241(C).
  47. Djoumbissie David Romain, 2020. "Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input," Papers 2011.13113, arXiv.org, revised Apr 2022.
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