AI-empowered financial stock: A survey
Source abstract
Abstract
In recent years, artificial intelligence (AI) technology has profoundly reshaped the financial stock sector, driving the intelligent transformation of quantitative trading, risk assessment, and investment decision-making. However, the deployment of AI continues to confront challenges such as data privacy concerns, algorithmic transparency issues, and market volatility. This paper provides a comprehensive survey of the transformative role of AI in the financial stock. AI technologies, including traditional machine learning, deep learning, natural language processing, and reinforcement learning, have revolutionized financial practices by enhancing prediction accuracy, enabling automated decision-making, and developing adaptive strategies for dynamic market environments. Then, this paper systematically reviews existing literature; research the key applications include stock market analysis, stock investment trading, and portfolio construction innovations; and identifies challenges such as data-centric challenges, model development and validation, and ethical and regulatory.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Stock Market Forecasting Methods · Advanced Technologies in Various Fields · Explainable Artificial Intelligence (XAI)
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Economics, Capital University of Economics and Business 1 , Beijing 100070,
College of Economics and Management, Shangqiu Normal University 2 , Shangqiu, Henan 47600, China
References (87)
Show source-provided references
1. Artificial intelligence (AI) in the financial sector—potential and public strategies10.3389/frai.2019.00016 ↗
2. Ai in the financial sector: The line between innovation, regulation and ethical responsibility10.3390/info15080432 ↗
3. C. Maple , L.Szpruch, G.Epiphaniou, K.Staykova, S.Singh, W.Penwarden, Y.Wen, Z.Wang, J.Hariharan, and P.Avramovic, “The AI revolution: Opportunities and challenges for the finance sector,” arXiv:2308.16538 (2023).
4. Towards an ‘internet of things’ framework for financial services sector
5. Text mining for big data analysis in financial sector: A literature review10.3390/su11051277 ↗
6. Application of cloud computing in financial services: An agent-oriented modelling approach10.1108/jm2-12-2017-0131 ↗
7. Contextualized design of iot (internet of things) finance for edge artificial intelligence computing10.1155/2022/6046957 ↗
8. A new CNN-based model for financial time series: TAIEX and FTSE stocks forecasting10.1007/s11063-022-10767-z ↗
9. A new combined CNN-RNN model for sector stock price analysis
10. Practices of natural language processing in the finance sector
11. T. G. Fischer , “Reinforcement learning in financial markets-a survey,” Technical Report, FAU Discussion Papers in Economics, 2018.
12. A review of natural language processing for financial technology10.1117/12.2604371 ↗
13. Stock market forecasting using hidden Markov model: A new approach
14. Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis10.1109/ACCESS.2020.3015966 ↗
15. A hybrid neurogenetic approach for stock forecasting10.1109/TNN.2007.891629 ↗
16. A prediction approach for stock market volatility based on time series data10.1109/ACCESS.2019.2895252 ↗
17. Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction10.1109/TFUZZ.2020.2972207 ↗
18. Tech for stronger financial market performance: The impact of AI on stock price crash risk in emerging market10.1108/IJOEM-10-2023-1717 ↗
19. Supervised machine learning techniques: An overview with applications to banking10.1111/insr.12448 ↗
20. Forecasting stock market movement direction with support vector machine10.1016/j.cor.2004.03.016 ↗
21. Identifying potential millennial customers for financial institutions using SVM10.1057/s41264-021-00128-7 ↗
22. Predicting failure in the U.S. banking sector: An extreme gradient boosting approach10.1016/j.iref.2018.03.008 ↗
23. XGBoost: A scalable tree boosting system
24. LightGBM: A highly efficient gradient boosting decision tree
25. Supervised learning: From theory to applications
26. News impact on stock price return via sentiment analysis10.1016/j.knosys.2014.04.022 ↗
27. A. Frigiola , “Supervised contrastive learning for classification of market stock series,” Ph.D. thesis, Politecnico di Torino, 2024.
28. F. Cai , N.-A.Le-Khac, and T.Kechadi, “Clustering approaches for financial data analysis: A survey,” arXiv:1609.08520 (2016).
29. Deep learning with long short-term memory networks for financial market predictions10.1016/j.ejor.2017.11.054 ↗
30. Deep learning for stock prediction using numerical and textual information10.1109/ICIS.2016.7550882 ↗
31. Semi-supervised clustering for financial risk analysis10.1007/s11063-021-10564-0 ↗
32. Stock market price prediction using LSTM RNN
33. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
34. Bidirectional LSTM networks for improved phoneme classification and recognition
35. Application of CNN-based financial risk identification and management convolutional neural networks in financial risk10.1016/j.sasc.2025.200215 ↗
36. Attention is all you need
37. Can transformers transform financial forecasting?10.1108/cfri-01-2024-0032 ↗
38. D. Araci , “FinBERT: Financial sentiment analysis with pre-trained language models,” arXiv:1908.10063 (2019).
39. Natural language based financial forecasting: A survey10.1007/s10462-017-9588-9 ↗
40. Y. Yang , M.Zhang, and S.Yue, “FinBERT: A pretrained language model for financial communications,” arXiv:2006.08097 (2020).
41. FinFormer: A multimodal transformer for financial market prediction with news and prices
42. How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–a review and research agenda10.1016/j.jjimei.2022.100094 ↗
43. Human-level control through deep reinforcement learning10.1038/nature14236 ↗
44. J. Schulman , F.Wolski, P.Dhariwal, A.Radford, and O.Klimov, “Proximal policy optimization algorithms,” arXiv:1707.06347 (2017).
45. Z. Jiang , D.Xu, and J.Liang, “A deep reinforcement learning framework for the financial portfolio management problem,” arXiv:1706.10059 (2017).
46. Reinforcement-learning based portfolio management with augmented asset movement prediction states10.1609/aaai.v34i01.5462 ↗
47. Empirical analysis: Stock market prediction via extreme learning machine10.1007/s00521-014-1550-z ↗
48. A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction10.1016/j.eswa.2017.02.044 ↗
49. Forecasting the volatility of stock price index10.1016/j.eswa.2006.08.001 ↗
50. An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network10.1016/s0165-0114(98)00399-6 ↗
51. Applying machine learning models in stock market prediction10.36713/epra2016 ↗
52. Deep learning for stock market prediction10.3390/e22080840 ↗
53. Forecasting intraday stock price trends with text mining techniques
54. Analysis of stock price return using textual data and numerical data through text mining
55. Analysis of the relation between stock price returns and headline news using text categorization
56. Twitter mood predicts the stock market10.1016/j.jocs.2010.12.007 ↗
57. Predictive sentiment analysis of tweets: A stock market application
58. Forecasting with economic news10.1080/07350015.2022.2060988 ↗
59. Integrating StockTwits with sentiment analysis for better prediction of stock price movement
60. Sentiment analysis of twitter data for predicting stock market movements
61. Intraday return forecasts and high-frequency trading of stock index futures: A hybrid wavelet-deep learning approach10.1080/1540496x.2023.2177507 ↗
62. Stock prices prediction based on social influence & historic data
63. A deep learning model for predicting buy and sell recommendations in stock exchange of Thailand using long short-term memory
64. How can we use artificial intelligence for stock recommendation and risk management? A proposed decision support system10.1016/j.jjimei.2022.100130 ↗
65. Stock price prediction based on artificial neural network
66. Improving quantitative stock trading prediction based on mad using q-learning technology
67. Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory10.1016/j.eswa.2023.119556 ↗
68. Artificial intelligence for ETF market prediction and portfolio optimization
69. Dynamic portfolio management based on pair trading and deep reinforcement learning
70. Impact of the development of FinTech by commercial banks on bank credit risk10.1016/j.frl.2023.103857 ↗
71. Applications of artificial intelligence in business management, e-commerce and finance10.1016/j.matpr.2021.06.419 ↗
72. Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing10.1016/j.techfore.2022.121562 ↗
73. Forecasting stock market crisis events using deep and statistical machine learning techniques10.1016/j.eswa.2018.06.032 ↗
74. AIRMS: A risk management tool using machine learning10.1016/j.eswa.2018.03.044 ↗
75. Research on early warning system of financial risk in China based on k-means clustering algorithm and bp neural network
76. Research on extreme risk warning for financial market based on RU-SMOTE-SVM
77. Firms’ ESG reputational risk and market longevity: A firm-level analysis for the United States10.1016/j.jbusres.2022.05.010 ↗
78. The role of artificial intelligence in developing a banking risk index: An application of adaptive neural network-based fuzzy inference system (ANFIS)10.1007/s10462-023-10473-9 ↗
79. Data empowerment and crash risk10.1080/13504851.2025.2526710 ↗
80. Digital twin: Financial technology’s next Frontier of robo-advisor10.3390/jrfm15040163 ↗
81. A survey of explainable artificial intelligence (XAI) in financial time series forecasting10.1145/3729531 ↗
82. A dual-stage attention-based recurrent neural network for time series prediction
83. Ai in finance: Challenges, techniques, and opportunities10.1145/3502289 ↗
84. Model-agnostic explainable artificial intelligence methods in finance: A systematic review, recent developments, limitations, challenges and future directions10.1007/s10462-025-11215-9 ↗
85. Federated learning empowered recommendation model for financial consumer services10.1109/tce.2023.3339702 ↗
86. Quantum computing for finance: Overview and prospects10.1016/j.revip.2019.100028 ↗
87. Can the European financial data space remove bias in financial AI development? Opportunities and regulatory challenges10.1093/ijlit/eaad020 ↗