Deep Learning for Financial Applications : A Survey

Citations

Cited by:

1. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
2. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
3. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
4. Yunus Santur, 2023. "A Novel Financial Forecasting Approach Using Deep Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1341-1392, October.
5. Kai Ren, 2023. "Study on Intelligent Forecasting of Credit Bond Default Risk," Papers 2305.12142, arXiv.org, revised Jun 2023.
6. Xiaohang Ren & Wenting Jiang & Qiang Ji & Pengxiang Zhai, 2024. "Seeing is believing: Forecasting crude oil price trend from the perspective of images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2809-2821, November.
7. Joel Ong & Dorien Herremans, 2024. "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers 2406.08742, arXiv.org.
8. 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.
9. David Alaminos & Ignacio Esteban & M. Belén Salas, 2023. "Neural networks for estimating Macro Asset Pricing model in football clubs," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 57-75, April.
10. Rian Dolphin & Barry Smyth & Yang Xu & Ruihai Dong, 2021. "Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities," Papers 2107.03926, arXiv.org.
11. Ruyi Tao & Kaiwei Liu & Xu Jing & Jiang Zhang, 2024. "Predicting Company Growth by Econophysics informed Machine Learning," Papers 2410.17587, arXiv.org.
12. Jingyi Gu & Sarvesh Shukla & Junyi Ye & Ajim Uddin & Guiling Wang, 2023. "Deep learning model with sentiment score and weekend effect in stock price prediction," SN Business & Economics, Springer, vol. 3(7), pages 1-20, July.
13. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
14. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
15. Longbing Cao & Qiang Yang & Philip S. Yu, 2020. "Data science and AI in FinTech: An overview," Papers 2007.12681, arXiv.org, revised Jul 2021.
16. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
17. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
18. Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
19. Bartosz Bieganowski & Robert Slepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers 2404.01866, arXiv.org, revised Jun 2024.
    • Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
20. Shi, Yong & Li, Bo & Du, Guangle & Dai, Wei, 2021. "Clustering framework based on multi-scale analysis of intraday financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
21. 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.
22. Soobin Kwak & Youngjin Hwang & Yongho Choi & Jian Wang & Sangkwon Kim & Junseok Kim, 2022. "Reconstructing the Local Volatility Surface from Market Option Prices," Mathematics, MDPI, vol. 10(14), pages 1-12, July.
23. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
24. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
25. Saadet Eskiizmirliler & Korhan Günel & Refet Polat, 2021. "On the Solution of the Black–Scholes Equation Using Feed-Forward Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 915-941, October.
26. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Arup Kumar Mohanty & Mazin Abed Mohammed & Alaa S. Al-Waisy & Seifedine Kadry & Jungeun Kim, 2022. "A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market," Mathematics, MDPI, vol. 10(19), pages 1-23, October.
27. 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.
28. Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.
29. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
30. J. C. Garza Sepúlveda & F. Lopez-Irarragorri & S. E. Schaeffer, 2023. "Forecasting Forex Trend Indicators with Fuzzy Rough Sets," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 229-287, June.
31. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
32. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
33. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
34. Philippe Jardin, 2023. "Designing topological data to forecast bankruptcy using convolutional neural networks," Annals of Operations Research, Springer, vol. 325(2), pages 1291-1332, June.
35. Efe Arin & A. Murat Ozbayoglu, 2022. "Deep Learning Based Hybrid Computational Intelligence Models for Options Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 39-58, January.
36. Jaeyoung Cheong & Heejoon Lee & Minjung Kang, 2021. "Stock Index Prediction using Cointegration test and Quantile Loss," Papers 2109.15045, arXiv.org.
37. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
38. Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
39. Abdellilah Nafia & Abdellah Yousfi & Abdellah Echaoui, 2023. "Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks," IJFS, MDPI, vol. 11(2), pages 1-48, March.
40. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
41. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
42. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
43. 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.