Nghiên cứu khả năng ứng dụng trí tuệ nhân tạo trong dự báo giá cổ phiếu
Từ khóa
Tóm tắt
Trí tuệ nhân tạo (AI) đang ngày càng được nghiên cứu và ứng dụng mạnh mẽ trong ngành Tài chính. Các nhà đầu tư chứng khoán cũng đang không ngừng ứng dụng AI...##plugins.themes.default.displayStats.downloads##
Tài liệu tham khảo
1. Mammeri, Z., Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches. IEEE Access, 2019. 7: p. 55916-55950.
2. Ahmed, S., et al., Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 2022. 6: p. 101646.
3. Clifford, C., Google CEO: A.I. is more important than fire or electricity. 2018, CNBC.
4. Königstorfer, F. and S. Thalmann, Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance. Journal of Behavioral and Experimental Finance, 2020. 27: p. 100352.
5. Ghosh, B. and E. Kozarevic, Identifying explosive behavioral trace in the CNX Nifty Index: a quantum finance approach. Investment Management and Financial Innovations, 2018. 15(1): p. 208-223
6. Mamoshina, P., et al., Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 2017. 9(5): p. 5665-5690.
7. Petropoulos, F., et al., Forecasting: theory and practice. International Journal of Forecasting, 2022. 38(3): p. 705-871.
8. Shmueli, G. and A. Tafti, How to “improve” prediction using behavior modification. International Journal of Forecasting, 2023. 39(2): p. 541-555.
9. Kumar, D., P.K. Sarangi, and R. Verma, A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 2022. 49: p. 3187-3191.
10. Smyl, S., A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 2020. 36(1): p. 75-85.
11. Hewamalage, H., C. Bergmeir, and K. Bandara, Recurrent Neural Networks for Time Series Forecasting: Current status and future directions. International Journal of Forecasting, 2021. 37(1): p. 388-427.
12. Su, P.-H., et al. On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems. 2016. Berlin, Germany: Association for Computational Linguistics.
13. Jaggi, M., et al. Text Mining of Stocktwits Data for Predicting Stock Prices. Applied System Innovation, 2021. 4, DOI: 10.3390/asi4010013.
14. Fama, E.F., The Behavior of Stock-Market Prices. The Journal of Business, 1965. 38(1): p. 34-105.
15. Fama, E.F., et al., The Adjustment of Stock Prices to New Information. International Economic Review, 1969. 10(1): p. 1-21.
16. Fama, E.F., Random Walks in Stock Market Prices. Financial Analysts Journal, 1995. 51(1): p. 75-80.
17. Chong, E., C. Han, and F.C. Park, Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 2017. 83: p. 187-205.
18. Oliveira, N., P. Cortez, and N. Areal, The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 2017. 73: p. 125-144.
19. Weng, B., M.A. Ahmed, and F.M. Megahed, Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 2017. 79: p. 153-163.
20. Januschowski, T., et al., Criteria for classifying forecasting methods. International Journal of Forecasting, 2020. 36(1): p. 167-177.
21. Dudek, G., STD: A Seasonal-Trend-Dispersion Decomposition of Time Series. IEEE Transactions on Knowledge & Data Engineering, 2023. 35(10): p. 10339-10350.
22. Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003. 50: p. 159-175.
23. Babu, C.N. and B.E. Reddy, A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing, 2014. 23: p. 27-38.
24. Vijh, M., et al., Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science, 2020. 167: p. 599-606.
25. Hoque, K.E. and H. Aljamaan, Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting. IEEE Access, 2021. 9: p. 163815-163830.
26. Mintarya, L.N., et al., Machine learning approaches in stock market prediction: A systematic literature review. Procedia Computer Science, 2023. 216: p. 96-102.
27. Li, A.W. and G.S. Bastos, Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review. IEEE Access, 2020. 8: p. 185232-185242.
28. Hu, Z., Y. Zhao, and M. Khushi A Survey of Forex and Stock Price Prediction Using Deep Learning. Applied System Innovation, 2021. 4, DOI: 10.3390/asi4010009.
29. Zuo, Y. and E. Kita, Stock price forecast using Bayesian network. Expert Systems with Applications, 2012. 39(8): p. 6729-6737.
30. Adebiyi, A.A., A.O. Adewumi, and C.K. Ayo, Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014. 2014(1): p. 614342.
31. Li, X., P. Wu, and W. Wang, Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong. Information Processing & Management, 2020. 57(5): p. 102212.
32. Khan, W., et al., Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 2022. 13(7): p. 3433-3456.
33. Schumaker, R.P. and H. Chen, A quantitative stock prediction system based on financial news. Information Processing & Management, 2009. 45(5): p. 571-583.
34. Fürnkranz, J., et al. Mean Absolute Error. in Encyclopedia of Machine Learning and Data Mining. 2010.
35. Thanh, H.T.P. and P. Meesad, Stock Market Trend Prediction Based on Text Mining of Corporate Web and Time Series Data. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2014. 18(1): p. 22-31.
36. Duong, D., T. Nguyen, and M. Dang, Stock Market Prediction using Financial News Articles on Ho Chi Minh Stock Exchange, in Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication. 2016, Association for Computing Machinery: Danang, Viet Nam. p. Article 71.
37. Trang Tran, N.N.L., Nguyen Son Tung, Nguyen Thu Thao, PTH Tham, Tuan Nguyen. NEU-Stock: Stock Market Prediction Based on Financial News. in The 2nd International Conference on Human-centered Artificial Intelligence. 2021. Da Nang, Vietnam: CEUR Workshop Proceedings.
38. Hanh, L.H., et al., Stock Market Prediction: The Application of Text-Mining in Vietnam. VNU JOURNAL OF ECONOMICS AND BUSINESS, 2022. 2(2).
39. Devlin, J., et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. in North American Chapter of the Association for Computational Linguistics. 2019.
40. Araci, D., FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. ArXiv, 2019. abs/1908.10063.
41. Graves, A., Generating Sequences With Recurrent Neural Networks. ArXiv, 2013. abs/1308.0850.
42. George Box, G.J., Time Series Analysis: Forecasting and Control. 1976.
43. Svetlozar T. Rachev, S.M., Frank J. Fabozzi, Sergio M. Focardi, Teo Jašic Financial Econometrics: From Basics to Advanced Modeling Techniques. 2007: Wiley.
44. Zizhe, Z. Comparison of LSTM and ARIMA in Price Forecasting: Evidence from Five Indexes. in Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023). 2023. Atlantis Press.
45. Ma, Q., Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences, 2020. 218: p. 01026.
46. Vuong, P.H., et al., A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach. 2024. 107(1): p. 368504241236557.
47. Chen, J., et al., Machine learning techniques for stock price prediction and graphic signal recognition. Engineering Applications of Artificial Intelligence, 2023. 121: p. 106038.