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Deep learning with long short-term memory networks for financial market predictions

Citations

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Cited by:

  1. Liyun Su & Dan Li & Dongyang Qiu, 2025. "BLS-QLSTM: a novel hybrid quantum neural network for stock index forecasting," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
  2. Zihan Chen & Lei Nico Zheng & Cheng Lu & Jialu Yuan & Di Zhu, 2023. "ChatGPT Informed Graph Neural Network for Stock Movement Prediction," Papers 2306.03763, arXiv.org, revised Sep 2023.
  3. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
  4. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
  5. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
  6. Burak Korkusuz & Mehmet Sahiner, 2025. "Coin impact on cross-crypto realized volatility and dynamic cryptocurrency volatility connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-32, December.
  7. repec:bjc:journl:v:12:y:2025:i:10:p:2945-2962 is not listed on IDEAS
  8. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
  9. Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
  10. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
  11. Sini Guo & Yu Qin & Yuan Gao, 2025. "Uncertain online portfolio selection with LSTM predictors," Fuzzy Optimization and Decision Making, Springer, vol. 24(4), pages 615-641, December.
  12. Waleed Soliman & Zhiyuan Chen & Colin Johnson & Sabrina Wong, 2025. "Improving ETF Prediction Through Sentiment Analysis: A DeepAR and FinBERT Approach With Controlled Seed Sampling," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 32(1), March.
  13. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  14. 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.
  15. Yang Dexiang & Mu Shengdong & Yunjie Liu & Gu Jijian & Lien Chaolung, 2023. "An Improved Deep-Learning-Based Financial Market Forecasting Model in the Digital Economy," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
  16. Xiaohua Zeng & Changzhou Liang & Qian Yang & Fei Wang & Jieping Cai, 2025. "Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-31, January.
  17. Haithem Awijen & Hachmi Ben Ameur & Zied Ftiti & Waël Louhichi, 2025. "Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models," Annals of Operations Research, Springer, vol. 345(2), pages 979-1002, February.
  18. Liang, Qian & Lin, Qingyuan & Guo, Mengzhuo & Lu, Quanying & Zhang, Dayong, 2025. "Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model," International Review of Financial Analysis, Elsevier, vol. 102(C).
  19. Abraham Itzhak Weinberg, 2025. "Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction," Papers 2512.15738, arXiv.org.
  20. Sarbjit Singh Oberoi & Sayan Banerjee, 2023. "Bankruptcy Prediction of Indian Banks Using Advanced Analytics," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 22-41.
  21. Gil Cohen & Mahmoud Qadan, 2022. "The Complexity of Cryptocurrencies Algorithmic Trading," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
  22. Bhattacharjee, Biplab & Kumar, Rajiv & Senthilkumar, Arunachalam, 2022. "Unidirectional and bidirectional LSTM models for edge weight predictions in dynamic cross-market equity networks," International Review of Financial Analysis, Elsevier, vol. 84(C).
  23. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
  24. Zhenya Liu & Nawazish Mirza & Rongyu You & Yaosong Zhan, 2026. "Understanding the complexity of futures markets investing in China: evidence from deep learning techniques," Annals of Operations Research, Springer, vol. 357(1), pages 409-440, February.
  25. Faisal Khalil & Gordon Pipa, 2022. "Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 147-171, June.
  26. Elizabeth Fons & Paula Dawson & Xiao-jun Zeng & John Keane & Alexandros Iosifidis, 2020. "Augmenting transferred representations for stock classification," Papers 2011.04545, arXiv.org.
  27. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
  28. Gagan Deep & Akash Deep & William Lamptey, 2025. "Interpretable Hypothesis-Driven Trading:A Rigorous Walk-Forward Validation Framework for Market Microstructure Signals," Papers 2512.12924, arXiv.org.
  29. Jan Kwiatkowski & Jaros{l}aw A. Chudziak, 2025. "On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model," Papers 2510.14156, arXiv.org.
  30. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
  31. Yoontae Hwang & Stefan Zohren, 2025. "Signature-Informed Transformer for Asset Allocation," Papers 2510.03129, arXiv.org, revised Jan 2026.
  32. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
  33. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
  34. Firat Melih Yilmaz & Engin Yildiztepe, 2024. "Statistical Evaluation of Deep Learning Models for Stock Return Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 221-244, January.
  35. Koya Ishikawa & Kazuhide Nakata, 2021. "Online Trading Models with Deep Reinforcement Learning in the Forex Market Considering Transaction Costs," Papers 2106.03035, arXiv.org, revised Dec 2021.
  36. Hyungjun Park & Min Kyu Sim & Dong Gu Choi, 2019. "An intelligent financial portfolio trading strategy using deep Q-learning," Papers 1907.03665, arXiv.org, revised Nov 2019.
  37. Yan Liu & Xiong Zhang, 2023. "Option Pricing Using LSTM: A Perspective of Realized Skewness," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
  38. Yachou Najlae & Abahman Omar & Hakimi Khalid, 2026. "Designing an LSTM-Based Model for Financial Asset Forecasting Using Machine Learning," Central European Economic Journal, Sciendo, vol. 13(60), pages 1-23.
  39. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
  40. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
  41. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
  42. Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
  43. 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.
  44. Hu, Xiao & Kang, Siqin & Ren, Long & Zhu, Shaokeng, 2024. "Interactive preference analysis: A reinforcement learning framework," European Journal of Operational Research, Elsevier, vol. 319(3), pages 983-998.
  45. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
  46. Byung-ki Jeon & Eui-Jong Kim, 2020. "Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data," Energies, MDPI, vol. 13(20), pages 1-16, October.
  47. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
  48. Igor L. R. Azevedo & Toyotaro Suzumura, 2024. "From Votes to Volatility Predicting the Stock Market on Election Day," Papers 2412.11192, arXiv.org.
  49. Avishay Aiche & Zvi Winer & Gil Cohen, 2024. "Constructing Cybersecurity Stocks Portfolio Using AI," Forecasting, MDPI, vol. 6(4), pages 1-13, November.
  50. Hasnain Iftikhar & Faridoon Khan & Elías A. Torres Armas & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2025. "A novel hybrid framework for forecasting stock indices based on the nonlinear time series models," Computational Statistics, Springer, vol. 40(8), pages 4163-4186, November.
  51. Njud S. Alharbi & Hadi Jahanshahi & Qijia Yao & Stelios Bekiros & Irene Moroz, 2023. "Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
  52. Chengyu Yang & Han Zhou & Ximing Chen & Jiejun Huang, 2024. "Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism," Energies, MDPI, vol. 17(9), pages 1-17, April.
  53. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
  54. Wang, Jianzhou & Lv, Mengzheng & Wang, Shuai & Gao, Jialu & Zhao, Yang & Wang, Qiangqiang, 2024. "Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
  55. Shiguo Huang & Linyu Cao & Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2024. "Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization," Mathematics, MDPI, vol. 12(21), pages 1-21, October.
  56. Arasteh, Abdollah, 2025. "A data-driven prediction method for multi-period portfolio optimization using the real options approach," Finance Research Letters, Elsevier, vol. 80(C).
  57. Ghosh, Indranil & Chaudhuri, Tamal Datta & Alfaro-Cortés, Esteban & Gámez, Matías & García, Noelia, 2022. "A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
  58. Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021. "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, vol. 78(C).
  59. Werner Kristjanpoller & Kevin Michell & Cristian Llanos & Marcel C. Minutolo, 2025. "Incorporating causal notions to forecasting time series: a case study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-22, December.
  60. Adamantios Ntakaris & Moncef Gabbouj & Juho Kanniainen, 2023. "Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting," Papers 2304.09840, arXiv.org, revised May 2023.
  61. Doss, Karan & Hanshew, Alissa S. & Mauro, John C., 2020. "Signatures of criticality in mining accidents and recurrent neural network forecasting model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
  62. Gergana Taneva-Angelova & Stefan Raychev & Galina Ilieva, 2025. "A Framework for Gold Price Prediction Combining Classical and Intelligent Methods with Financial, Economic, and Sentiment Data Fusion," IJFS, MDPI, vol. 13(2), pages 1-25, June.
  63. Green, Lawrence & Sung, Ming-Chien & Ma, Tiejun & Johnson, Johnnie E. V., 2019. "To what extent can new web-based technology improve forecasts? Assessing the economic value of information derived from Virtual Globes and its rate of diffusion in a financial market," European Journal of Operational Research, Elsevier, vol. 278(1), pages 226-239.
  64. Fabozzi, Frank J. & Recchioni, Maria Cristina & Renò, Roberto, 2025. "Fifty years at the interface between financial modeling and operations research," European Journal of Operational Research, Elsevier, vol. 327(1), pages 1-21.
  65. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
  66. Arthur Emanuel de Oliveira Carosia & Ana Estela Antunes Silva & Guilherme Palermo Coelho, 2025. "Predicting the Brazilian Stock Market with Sentiment Analysis, Technical Indicators and Stock Prices: A Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2351-2378, April.
  67. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
  68. Keonvin Park, 2026. "Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction," Papers 2603.19288, arXiv.org.
  69. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  70. Zeynep Cipiloglu Yildiz & Selim Baha Yildiz, 2022. "A portfolio construction framework using LSTM‐based stock markets forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2356-2366, April.
  71. Junzhe Jiang & Chang Yang & Xinrun Wang & Bo Li, 2025. "Why Regression? Binary Encoding Classification Brings Confidence to Stock Market Index Price Prediction," Papers 2506.03153, arXiv.org.
  72. Robert Jane & Tae Young Kim & Emily Glass & Emilee Mossman & Corey James, 2021. "Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)," Energies, MDPI, vol. 14(19), pages 1-39, September.
  73. Miao Su & Keun Sik Park & Sung Hoon Bae, 2024. "A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 21-43, March.
  74. Dhasmana, Samriddhi & Goel, Sandeep, 2023. "The insidious hyperreality in financial markets: An integrative review with evidence from the Indian financial market," International Review of Financial Analysis, Elsevier, vol. 90(C).
  75. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Xie, Chi, 2025. "Cross-market volatility forecasting with attention-based spatial–temporal graph convolutional networks," Journal of Empirical Finance, Elsevier, vol. 83(C).
  76. Karijadi, Irene & Chou, Shuo-Yan & Dewabharata, Anindhita, 2023. "Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method," Renewable Energy, Elsevier, vol. 218(C).
  77. Dominik Stempie'n & Janusz Gajda, 2025. "Comparative analysis of financial data differentiation techniques using LSTM neural network," Papers 2505.19243, arXiv.org.
  78. Muhammad Safiullah, Madiha Sher,MuhammadKashan,Adeel Rehman, Yasir Saleem Afridi, 2024. "Stock Market Analysis and Prediction Using Deep Learning," International Journal of Innovations in Science & Technology, 50sea, vol. 6(5), pages 329-337, June.
  79. Mitja Steinbacher & Matej Steinbacher & Matjaz Steinbacher, 2025. "Using CNN to Model Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 5299-5340, December.
  80. Jian Ni & Yue Xu, 2023. "Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 35-55, January.
  81. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
  82. Merve Mert Sarıtaş, 2025. "Forecasting the XBANK Index in Türkiye Using Macroeconomic Indicators: A Model Comparison with Ensemble Learning Methods," International Econometric Review (IER), Econometric Research Association, vol. 17(1), pages 44-58, June.
  83. Thi Thu Giang Nguyen & Robert Ślepaczuk, 2022. "The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index," Working Papers 2022-29, Faculty of Economic Sciences, University of Warsaw.
  84. Yi Cao & Jia Zhai, 2022. "Estimating price impact via deep reinforcement learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3954-3970, October.
  85. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
  86. Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
  87. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
  88. Asef Yelghi & Aref Yelghi & Shirmohammad Tavangari, 2024. "Artificial Intelligence in Financial Forecasting: Analyzing the Suitability of AI Models for Dollar/TL Exchange Rate Predictions," Papers 2411.04259, arXiv.org, revised Nov 2024.
  89. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," FrenXiv e75gc_v1, Center for Open Science.
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  91. Hairong Cui & Xurui Wang & Xiaojun Chu, 2025. "Stock Returns Prediction Based on Implied Volatility Spread Under Network Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2829-2852, May.
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  93. Xin, Ling, 2024. "Short-term contrarian in the carbon emission market," Energy Economics, Elsevier, vol. 139(C).
  94. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
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  96. Marcello Monga, 2024. "Automated Market Making and Decentralized Finance," Papers 2407.16885, arXiv.org.
  97. Chuheng Zhang & Yuanqi Li & Xi Chen & Yifei Jin & Pingzhong Tang & Jian Li, 2020. "DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis," Papers 2010.01265, arXiv.org, revised Jan 2021.
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  99. Krzysztof Piasecki & Michał Dominik Stasiak, 2020. "Optimization Parameters of Trading System with Constant Modulus of Unit Return," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
  100. Priya Singh & Manoj Jha, 2024. "Portfolio Optimization Using Novel EW-MV Method in Conjunction with Asset Preselection," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3683-3712, December.
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  106. Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
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  114. Andrés García-Medina & Ester Aguayo-Moreno, 2024. "LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1511-1542, April.
  115. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
  116. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
  117. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
  118. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
  119. Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
  120. Kevin Cedric Guyard & Michel Deriaz, 2024. "Predicting Foreign Exchange EUR/USD direction using machine learning," Papers 2409.04471, arXiv.org, revised Oct 2024.
  121. László Vancsura & Tibor Tatay & Tibor Bareith, 2023. "Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times," Risks, MDPI, vol. 11(2), pages 1-16, January.
  122. Qi Zhao, 2020. "A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data," Papers 2010.07404, arXiv.org.
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