دوره 22، شماره 2 - ( 6-1404 )
جلد 22 شماره 2 صفحات 126-109 |
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Anbaee Farimani S, Ghouchannezhad noor nia R, VAFAEI JAHAN M. Review on Large Language Models in Finance: Text and Time Series Analysis for Investor Behavior and Market Prediction. JSDP 2025; 22 (2) : 7
URL: http://jsdp.rcisp.ac.ir/article-1-1252-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1252-fa.html
انبائی فریمانی سعیده، قوچان نژادنورنیا راهله، وفایی جهان مجید. مروری بر کاربرد مدل‌های بزرگ زبانی در پردازش متن و سری‌های زمانی در تحلیل رفتار سرمایه‌گذاران و پیش‌بینی بازارهای مالی. پردازش علائم و داده‌ها. 1404; 22 (2) :109-126
دانشیار گروه مهندسی کامپیوتر، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
چکیده: (340 مشاهده)
استفاده گسترده از شبکه‌های اجتماعی و انتشار اخبار در رسانه‌ها، حجم عظیمی از داده‌های متنی و سری‌های زمانی را تولید کرده‌است که بر رفتار سرمایه‌گذاران در بازارهای مالی تأثیر مستقیم می‌گذارد؛ در این میان، مدل‌های بزرگ زبانی و فناوری‌های پیشرفته پردازش سری‌های زمانی و زبان طبیعی نقشی کلیدی در جمع‌آوری، تحلیل و استخراج الگوهای پنهان از این داده‌ها ایفا می‌کنند. این مقاله مروری، به بررسی بیش از دویست مرجع منتشرشده از سال ۲۰۰۶ تا ۲۰۲۴ می‌پردازد که به برهم‌کنش بازارهای مالی و وقایع خبری منتشرشده در وب با رویکرد متن‌کاوی متمرکزند. در این مطالعه، انواع منابع اطلاعاتی، روش‌های بازنمایی متن، تحلیل احساسات و مدل‌های پیش‌گو مورد بررسی قرار گرفته‌اند؛ همچنین، کاربرد مدل‌های بزرگ زبانی در پردازش سری‌های زمانی و تحلیل داده‌های بلادرنگ، به‌عنوان یکی از نوآوری‌های اخیر در این حوزه مورد توجه قرار گرفته است. هدف از این پژوهش، شناسایی مرز دانش در حوزه تحلیل کلان‌داده‌ها و ارائه مسیرهای آینده پژوهشی در زمینه روش‌های متن‌کاوی، هوش مصنوعی و یادگیری عمیق برای توسعه سامانه‌های پیش‌بینی، توصیه‌گر و تحلیل هم‌بستگی در بازارهای مالی نظیر بورس و فارکس است.
شماره‌ی مقاله: 7
واژه‌های کلیدی: مدل‌های بزرگ زبانی، متن‌کاوی، تحلیل احساس، پیش‌بینی بازارهای مالی، اخبار، شبکه‌های اجتماعی
نوع مطالعه: ترویجی |
موضوع مقاله:
مقالات پردازش متن
دریافت: 1400/4/27 | پذیرش: 1404/4/30 | انتشار: 1404/6/22 | انتشار الکترونیک: 1404/6/22
دریافت: 1400/4/27 | پذیرش: 1404/4/30 | انتشار: 1404/6/22 | انتشار الکترونیک: 1404/6/22
فهرست منابع
1. Li, Y., Wang, S., Ding, H. and Chen, H, "Large Language Models in Finance: A Survey", in Proceedings of the Fourth ACM International Conference on AI in Finance. Association for Computing Machinery: Brooklyn, NY, USA., 2023. pp. 374-382. [DOI:10.1145/3604237.3626869]
2. Nie, Y., Kong, Y., Dong, X., Mulvey, J.M., Poor, H.V., Wen, Q. and Zohren, S., "A survey of large language models for financial applications: Progress, prospects and challenges". arXiv preprint arXiv:2406.11903, 2024.
3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. and Agarwal, S., "Language models are few-shot learners" Advances in neural information processing systems, vol. 33, pp.1877-1901, 2020.
4. کاظمی تبار، سیدجواد، شهباززاده، مجید، «کشف تقلب در بازار بورس اوراق بهادار با استفاده از کاربرد نامساوی چبیشف»، فصلنامة پردازش علائم و داده‌ها، دورة 17، شمارة 1، صص 3-14، 1399.
4. Kazemitabar S. J., Shahbazzadeh M., "Stock Market Fraud Detection, A Probabilistic Approach". Signal and Data Processing, vol. 17(1), pp. 3-14, 2020. [DOI:10.29252/jsdp.17.1.3]
5. Ashish, V., "Attention is all you need". Advances in neural information processing systems, vol. 30, pp. I, 2017.
6. وفایی جهان، مجید، «استخراج ویژگی‌های ساختاری فایل‌های کامپیوتری مبتنی بر تحلیل و ارزیابی آماری»، فصلنامة پردازش علائم و داده‌ها، دورة 13، شمارة 4، صص 43-62، 1395.
6. VAFAEI, J.M., "Feature Extraction of Computer Files Structure by Statistical Analysis". Signal and Data Processing, vol. 13(4), pp. 43-62, 2017. [DOI:10.18869/acadpub.jsdp.13.4.43]
7. Farimani, S.A., M.V. Jahan, and A.M. Fard, "An Adaptive Multimodal Learning Model for Financial Market Price Prediction". IEEE Access, vol. 12, pp. 121846-121863, 2024. [DOI:10.1109/ACCESS.2024.3441029]
8. Farimani, S.A., Jahan, M.V., Fard, A.M. and Tabbakh, S.R.K., "Investigating the informativeness of technical indicators and news sentiment in financial market price prediction". Knowledge-Based Systems, vol. 247, pp. 108742, 2022. [DOI:10.1016/j.knosys.2022.108742]
9. Farimani, S.A., Jahan, M.V., Fard, A.M. and Haffari, G., "Leveraging Latent Economic Concepts and Sentiments in the News for Market Prediction". In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics. Porto, Portugal., DSAA. IEEE,ACM. 2021. pp. 1-10. [DOI:10.1109/DSAA53316.2021.9564122]
10. Delgadillo, J., Kinyua, J. and Mutigwe, C., "FinSoSent: Advancing Financial Market Sentiment Analysis through Pretrained Large Language Models". Big Data and Cognitive Computing, vol. 8(8), pp. 87, 2024. [DOI:10.3390/bdcc8080087]
11. Du, K., Xing, F., Mao, R. and Cambria, E., "Financial Sentiment Analysis: Techniques and Applications". ACM Computing Surveys, vol. 56(9), pp. 1-42, 2024. [DOI:10.1145/3649451]
12. Shen, Y. and Zhang, P.K., "Financial sentiment analysis on news and reports using large language models and finbert". In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), 2024. pp. 717-721. [DOI:10.1109/ICPICS62053.2024.10796670]
13. Park, T., "Enhancing anomaly detection in financial markets with an llm-based multi-agent framework". arXiv preprint arXiv:2403.19735, 2024.
14. Cao, Y., Chen, Z., Pei, Q., Dimino, F., Ausiello, L., Kumar, P., Subbalakshmi, K.P. and Ndiaye, P.M., "Risklabs: Predicting financial risk using large language model based on multi-sources data". 2024.
15. Xu, Y., Hao, J., Tang, K., Chen, J., Liu, A., Liu, P. and Zhang, G., "FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness". arXiv preprint arXiv:2505.23826, 2025. [DOI:10.18653/v1/2025.findings-acl.489]
16. Farimani, S.A., Jahan, M.V. and Milani Fard, A., "From Text Representation to Financial Market Prediction: A Literature Review". Information, vol. 13, 2022. [DOI:10.3390/info13100466]
17. Zhao, F., Li, X., Gao, Y., Li, Y., Feng, Z. and Zhang, C., "Multi-layer features ablation of BERT model and its application in stock trend prediction". Expert Systems with Applications, vol. 207, pp. 117958, 2022. [DOI:10.1016/j.eswa.2022.117958]
18. Avramelou, L., Nousi, P., Passalis, N. and Tefas, A., "Deep reinforcement learning for financial trading using multi-modal features". Expert Systems with Applications, vol. 238, pp. 121849, 2024. [DOI:10.1016/j.eswa.2023.121849]
19. Sanh, V., Debut, L., Chaumond, J. and Wolf, T., "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter". arXiv preprint arXiv:1910.01108, 2019.
20. Mo, K., Liu, W., Xu, X., Yu, C., Zou, Y. and Xia, F., "Fine-tuning gemma-7b for enhanced sentiment analysis of financial news headlines". In 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI), IEEE, 2024, pp. 130-135. [DOI:10.1109/ICETCI61221.2024.10594605] [PMID] []
21. Araci, D., "Finbert: Financial sentiment analysis with pre-trained language models". arXiv preprint arXiv:1908.10063, 2019.
22. Jeong, C., "Domain-specialized LLM: Financial fine-tuning and utilization method using Mistral 7B". Journal of Intelligence and Information Systems, vol. 30(1), pp. 93-120, 2024. [DOI:10.13088/jiis.2024.30.1.093]
23. Cutler, D.M., Poterba, J.M. and Summers, L.H., "What moves stock prices?". 1988. [DOI:10.3386/w2538]
24. Shiller, R.J., "From efficient markets theory to behavioral finance". Journal of Economic Perspectives, vol. 17(1), pp. 83-104, 2003. [DOI:10.1257/089533003321164967]
25. Davies, P.L. and Canes, M., "Stock prices and the publication of second-hand information". Journal of Business, pp. 43-56, 1978. [DOI:10.1086/295983]
26. Wuthrich, B., Cho, V., Leung, S., Permunetilleke, D., Sankaran, K. and Zhang, J., "Daily stock market forecast from textual web data". In SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), IEEE, 1998. vol. 3, pp. 2720-2725. [DOI:10.1109/ICSMC.1998.725072]
27. Barber, B.M. and Loeffler, D., "The "Dart board" column: Secondhand information and price pressure". Journal of Financial and Quantitative Analysis, vol. 28(2), pp.273-284, 1993. [DOI:10.2307/2331290]
28. Fama, E.F., "The behavior of stock-market prices". The Journal of Business, vol. 38(1) pp. 34-105, 1965. [DOI:10.1086/294743]
29. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y. and Ngo, D.C.L., "Text Mining for Market Prediction: A Systematic Review". Expert Systems with Applications, vol. 41(16), pp. 7653-7670, 2014. [DOI:10.1016/j.eswa.2014.06.009]
30. Weng, B., Ahmed, M.A. and Megahed, F.M., "Stock Market One-Day Ahead Movement Prediction Using Disparate Data Sources". Expert systems with applications, vol. 79, 2017. [DOI:10.1016/j.eswa.2017.02.041]
31. Zhang, G., Xu, L. and Xue, Y., "Model and forecast stock market behavior integrating investor sentiment analysis and transaction data". Cluster Computing, vol. 20(1), pp. 789-803, 2017. [DOI:10.1007/s10586-017-0803-x]
32. Kaushal, A. and Chaudhary, P., "News and events aware stock price forecasting technique". In 2017 International Conference on Big Data, IoT and Data Science (BID), IEEE, 2017. pp. 8-13. [DOI:10.1109/BID.2017.8336565]
33. Hajek, P. and Barushka, A., "Integrating Sentiment Analysis and Topic Detection in Financial News for Stock Movement Prediction". In Proceedings of the 2nd International Conference on Business and Information Management 2018, ACM: Barcelona, Spain, 2018. pp. 158-162. [DOI:10.1145/3278252.3278267]
34. Othan, D., Kilimci, Z.H. and Uysal, M., "Financial sentiment analysis for predicting direction of stocks using bidirectional encoder representations from transformers (BERT) and deep learning models". In Proc. int. conf. innov. intell. technol, 2019. pp. 30-35.
35. Cheng, W. and Chen, S., "Sentiment analysis of financial texts based on attention mechanism of FinBERT and BiLSTM". In 2021 International Conference on Computer Engineering and Application (ICCEA), IEEE, 2021. pp. 73-78). [DOI:10.1109/ICCEA53728.2021.00022]
36. Hiew, J.Z.G., Huang, X., Mou, H., Li, D., Wu, Q. and Xu, Y., "Bert-based financial sentiment index and lstm-based stock return predictability". arXiv 2019. arXiv preprint arXiv:1906.09024, 2019.
37. Li, X., Xie, H., Chen, L., Wang, J. and Deng, X., "News impact on stock price return via sentiment analysis". Knowledge-Based Systems, vol. 69, pp. 14-23, 2014. [DOI:10.1016/j.knosys.2014.04.022]
38. Nizer, P.S. and Nievola, J.C., "Predicting published news effect in the Brazilian stock market". Expert Systems with Applications, vol. 39(12), pp. 10674-10680, 2012. [DOI:10.1016/j.eswa.2012.02.162]
39. Van de Kauter, M., Breesch, D. and Hoste, V., "Fine-grained analysis of explicit and implicit sentiment in financial news articles". Expert Systems with Applications, vol. 42(11), pp. 4999-5010, 2015. [DOI:10.1016/j.eswa.2015.02.007]
40. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y. and Ngo, D.C.L., "Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment". Expert Systems with Applications, vol. 42(1), pp. 306-324, 2015. [DOI:10.1016/j.eswa.2014.08.004]
41. Krishnamoorthy, S., "Sentiment analysis of financial news articles using performance indicators". Knowledge and Information Systems, vol. 56(2), pp. 373-394, 2018. [DOI:10.1007/s10115-017-1134-1]
42. Moreno-Ortiz, A. and Fernández-Cruz, J., "Identifying Polarity in Financial Texts for Sentiment Analysis: A Corpus-based Approach". Procedia-Social and Behavioral Sciences, vol. 198, pp. 330-338, 2015. [DOI:10.1016/j.sbspro.2015.07.451]
43. Liu, Q., Cheng, X., Su, S. and Zhu, S., "Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News". In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ACM: Torino, Italy, 2018, pp. 1603-1606. [DOI:10.1145/3269206.3269286]
44. Li, K., "Reaction to news in the Chinese stock market: A study on Xiong'an New Area Strategy". Journal of Behavioral and Experimental Finance, vol. 19, pp. 36-38, 2018. [DOI:10.1016/j.jbef.2018.03.004]
45. Gupta, K. and Banerjee, R., "Does OPEC news sentiment influence stock returns of energy firms in the United States?" Energy Economics, vol. 77, pp. 34-45, 2019. [DOI:10.1016/j.eneco.2018.03.017]
46. Shi, Y., Ho, K.Y. and Liu, W.M., "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach". International Review of Economics & Finance, vol. 42, pp. 291-312, 2016. [DOI:10.1016/j.iref.2015.12.003]
47. Bollen, J., Mao, H. and Zeng, X., "Twitter mood predicts the stock market". Journal of Computational Science, vol. 2, pp. 1-8, 2011. [DOI:10.1016/j.jocs.2010.12.007]
48. Zhang, X., Fuehres, H. and Gloor, P.A., "Predicting Stock Market Indicators Through Twitter "I hope it is not as bad as I fear"". Procedia-Social and Behavioral Sciences, vol. 26, pp.55-62, 2011. [DOI:10.1016/j.sbspro.2011.10.562]
49. Sun, A., Lachanski, M. and Fabozzi, F.J., "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction". International Review of Financial Analysis, vol. 48: pp. 272-281, 2016. [DOI:10.1016/j.irfa.2016.10.009]
50. Atzeni, M., Dridi, A. and Reforgiato Recupero, D., "Using frame-based resources for sentiment analysis within the financial domain". Progress in Artificial Intelligence, vol. 7(4), pp. 273-294, 2018. [DOI:10.1007/s13748-018-0162-8]
51. Ho, C.S., Damien, P., Gu, B. and Konana, P., "The time-varying nature of social media sentiments in modeling stock returns". Decision Support Systems, vol. 101, pp. 69-81, 2017. [DOI:10.1016/j.dss.2017.06.001]
52. Zou, Y. and Herremans, D., "PreBit-A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin". Expert Systems with Applications, vol. 233, pp. 120838, 2023. [DOI:10.1016/j.eswa.2023.120838]
53. Wang, H., Lu, S. and Zhao, J., "Aggregating multiple types of complex data in stock market prediction: A model-independent framework". Knowledge-Based Systems, vol. 164, pp. 193-204, 2019. [DOI:10.1016/j.knosys.2018.10.035]
54. Chen, X., Ma, X., Wang, H., Li, X. and Zhang, C., "A hierarchical attention network for stock prediction based on attentive multi-view news learning". Neurocomputing, vol. 504, pp. 1-15, 2022. [DOI:10.1016/j.neucom.2022.06.106]
55. Ma, Y., Mao, R., Lin, Q., Wu, P. and Cambria, E., "Multi-source aggregated classification for stock price movement prediction". Information Fusion, vol. 91, pp. 515-528, 2023. [DOI:10.1016/j.inffus.2022.10.025]
56. Xu, H., Cao, D. and Li, S., "A self-regulated generative adversarial network for stock price movement prediction based on the historical price and tweets". Knowledge-Based Systems, vol. 247, pp. 108712, 2022. [DOI:10.1016/j.knosys.2022.108712]
57. Zeng, P., Hu, G., Zhou, X., Li, S., Liu, P. and Liu, S., "Muformer: A long sequence time-series forecasting model based on modified multi-head attention". Knowledge-Based Systems, vol. 254, pp. 109584, 2022. [DOI:10.1016/j.knosys.2022.109584]
58. Lee, T.W., Teisseyre, P. and Lee, J., "Effective exploitation of macroeconomic indicators for stock direction classification using the multimodal fusion transformer". IEEE Access, vol. 11, pp. 10275-10287, 2023. [DOI:10.1109/ACCESS.2023.3240422]
59. Wang, G., Ma, J. and Chen, G., "Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention". Decision Support Systems, vol. 167, pp. 113913, 2023. [DOI:10.1016/j.dss.2022.113913]
60. Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F., Min, H. and Deng, X., "Empirical analysis: stock market prediction via extreme learning machine". Neural Computing and Applications, vol. 27(1), pp. 67-78, 2016. [DOI:10.1007/s00521-014-1550-z]
61. Vargas, M.R., Dos Anjos, C.E., Bichara, G.L. and Evsukoff, A.G., "Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles". In 2018 international joint conference on neural networks (IJCNN), IEEE, 2018. pp. 1-8. [DOI:10.1109/IJCNN.2018.8489208] []
62. Huynh, H.D., Dang, L.M. and Duong, D., "A New Model for Stock Price Movements Prediction Using Deep Neural Network". In Proceedings of the 8th international symposium on information and communication technology, ACM: Nha Trang City, Viet Nam, 2017, pp. 57-62. [DOI:10.1145/3155133.3155202]
63. Lee, C.Y. and Soo, V.W., "Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Networks". In 2017 conference on technologies and applications of artificial intelligence (TAAI), IEEE, 2017, pp. 160-165. [DOI:10.1109/TAAI.2017.27]
64. Verma, I., Dey, L. and Meisheri, H., "Detecting, quantifying and accessing impact of news events on Indian stock indices". In Proceedings of the international conference on web intelligence, ACM: Leipzig, Germany, 2017, pp. 550-557. [DOI:10.1145/3106426.3106482]
65. Gurin, Y., Szymanski, T. and Keane, M.T., "Discovering news events that move markets". In 2017 Intelligent Systems Conference (IntelliSys), IEEE, 2017, pp. 452-461. [DOI:10.1109/IntelliSys.2017.8324333]
66. Hochreiter, S. and Schmidhuber, J., "Long short-term memory". Neural computation, vol. 9(8), pp. 1735-1780, 1997. [DOI:10.1162/neco.1997.9.8.1735] [PMID]
67. Romanov, V.P., Naletova, O.A., Panteleeva, E.A. and Federyakov, A.S., "Fractal Model of Estimating News and Insider Influence on Market Volatility". Automatic Documentation and Mathematical Linguistics, vol. 41(4), pp. 141-149, 2007. [DOI:10.3103/S0005105507040036]
68. Shynkevich, Y., McGinnity, T.M., Coleman, S.A. and Belatreche, A., "Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning". Decision Support Systems, vol. 85, pp. 74-83, 2016. [DOI:10.1016/j.dss.2016.03.001]
69. Checkley, M.S., Higón, D.A. and Alles, H., "The hasty wisdom of the mob: How market sentiment predicts stock market behavior". Expert Systems with Applications, vol. 77, pp. 256-263, 2017. [DOI:10.1016/j.eswa.2017.01.029]
70. Wei, Y.C., Lu, Y.C., Chen, J.N. and Hsu, Y.J., "Informativeness of the market news sentiment in the Taiwan stock market". The North American Journal of Economics and Finance, vol. 39, pp. 158-181, 2017. [DOI:10.1016/j.najef.2016.10.004]
71. Song, Q., Liu, A. and Yang, S.Y., "Stock portfolio selection using learning-to-rank algorithms with news sentiment". Neurocomputing, vol. 264, pp. 20-28, 2017. [DOI:10.1016/j.neucom.2017.02.097]
72. Geva, T. and Zahavi, J., "Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news". Decision Support Systems, vol. 57, pp. 212-223, 2014. [DOI:10.1016/j.dss.2013.09.013]
73. Feuerriegel, S. and Prendinger, H., "News-based trading strategies". Decision Support Systems, vol. 90(C), pp. 65-74, 2016. [DOI:10.1016/j.dss.2016.06.020]
74. Yang, S.Y., Mo, S.Y.K., Liu, A. and Kirilenko, A.A., "Genetic programming optimization for a sentiment feedback strength based trading strategy". Neurocomputing, vol. 264, pp. 29-41, 2017. [DOI:10.1016/j.neucom.2016.10.103]
75. Sun, Y., Fang, M. and Wang, X., "A novel stock recommendation system using Guba sentiment analysis". Personal and Ubiquitous Computing, vol. 22(3), pp.575-587, 2018. [DOI:10.1007/s00779-018-1121-x]
76. Ochiai, T. and Nacher, J.C., "A model for the dynamic behavior of financial assets affected by news: The case of Tohoku-Kanto earthquake". Physics letters A, vol. 375(41), pp. 3552-3556, 2011. [DOI:10.1016/j.physleta.2011.08.043]
77. Hendershott, T., Livdan, D. and Schürhoff, N., "Are institutions informed about news?". Journal of Financial Economics, vol. 117(2), pp. 249-287, 2015. [DOI:10.1016/j.jfineco.2015.03.007]
78. Medovikov, I., "When does the stock market listen to economic news? New evidence from copulas and news wires". Journal of Banking & Finance, vol. 65, pp. 27-40, 2016. [DOI:10.1016/j.jbankfin.2016.01.004]
79. Kaushik, B., Hemani, H. and Ilavarasan, P.V., "Social media usage vs. stock prices: an analysis of Indian firms". Procedia computer science, vol. 122, pp. 323-330, 2017. [DOI:10.1016/j.procs.2017.11.376]
80. Nisar, T.M. and Yeung, M., "Twitter as a tool for forecasting stock market movements: A short-window event study". The Journal of Finance and Data Science, vol. 4(2), pp. 101-119, 2018. [DOI:10.1016/j.jfds.2017.11.002]
81. Xiao, Y., Liu, B., Ventre, C. and Li, H., "A measure of climate change in financial news and its impact on the stock market". Journal of Digital Economy, vol. 3, pp.146-157, 2025. [DOI:10.1016/j.jdec.2025.03.002]
82. Birz, G., "Stale economic news, media and the stock market". Journal of Economic Psychology, vol. 61, pp. 87-102, 2017. [DOI:10.1016/j.joep.2017.03.002]
83. Tetlock, P.C., "All the News That's Fit to Reprint: Do Investors React to Stale Information?" The Review of Financial Studies, vol. 24(5), pp. 1481-1512, 2011. [DOI:10.1093/rfs/hhq141]
84. Zhang, W., Wang, P., Li, X. and Shen, D., "Quantifying the cross-correlations between online searches and Bitcoin market". Physica A: Statistical Mechanics and its Applications, vol. 509, pp. 657-672, 2018. [DOI:10.1016/j.physa.2018.06.073]
85. Fang, L., Yu, H. and Huang, Y., "The role of investor sentiment in the long-term correlation between U.S. stock and bond markets". International Review of Economics & Finance, vol. 58, pp. 127-139, 2018. [DOI:10.1016/j.iref.2018.03.005]
86. Fernández Vilas, A., Díaz Redondo, R.P., Crockett, K., Owda, M. and Evans, L., "Twitter permeability to financial events: an experiment towards a model for sensing irregularities". Multimedia Tools and Applications, vol. 78(7), pp. 9217-9245, 2019. [DOI:10.1007/s11042-018-6388-4]
87. Wong, C. and Ko, I.Y., "Predictive Power of Public Emotions as Extracted from Daily News Articles on the Movements of Stock Market Indices". In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), IEEE, 2016, pp. 705-708. [DOI:10.1109/WI.2016.0126]
88. Li, X. and Li, J., "AoE: Angle-optimized embeddings for semantic textual similarity. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers, 2024 pp. 1825-1839. [DOI:10.18653/v1/2024.acl-long.101]
89. Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J.Y., Shi, X., Chen, P.Y., Liang, Y., Li, Y.F., Pan, S. and Wen, Q., "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models". arXiv preprint arXiv:2310.01728, 2023.
90. Salton, G., "Automatic text processing: the transformation, analysis, and retrieval of information by computer". Reading: Addison-Wesley, vol.169, 1989.
91. Hagenau, M., Liebmann, M. and Neumann, D., "Automated news reading: Stock price prediction based on financial news using context-capturing features". Decision Support Systems, vol. 55(3), pp. 685-697, 2013. [DOI:10.1016/j.dss.2013.02.006]
92. Seifollahi, S. and Shajari, M., "Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction". Journal of Intelligent Information Systems, vol. 52(1), pp. 57-83, 2019. [DOI:10.1007/s10844-018-0504-9]
93. Rao, Y., Zhong, X. and Lu, S., "Research on News Topic-Driven Market Flucatuation and Predication". In 2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), IEEE, 2016, pp. 559-562. [DOI:10.1109/IIKI.2016.93] []
94. Yang, L., Xu, Y., Ng, T.L.J. and Dong, R., "Leveraging BERT to Improve the FEARS Index for Stock Forecasting". In The First Workshop on Financial Technology and Natural Language Processing in conjunction with IJCAI, Macao, China, 2019, p. 54.
95. Chen, D., Harimoto, K., Bao, R., Su, Q. and Sun, X., "Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction". arXiv preprint arXiv:1910.05032, 2019. [DOI:10.18653/v1/D19-5106]
96. Reimers, N. and Gurevych, I., "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks". arXiv preprint arXiv:1908.10084, 2019. [DOI:10.18653/v1/D19-1410]
97. Soon, P.C., Tan, T.P., Chan, H.Y. and Gan, K.H., "Stock Trend Prediction Using Multi-Attention Network on Domain-specific and Domain-general Features in News Headline". Pertanika Journal of Science & Technology, vol. 33(2), 2025. [DOI:10.47836/pjst.33.2.13]
98. Wibisono, O., Septiandri, A.A. and Najogie, R.D., "Assessing the Impact of ESG-Related News on Stock Trading in the Indonesian Market: A Text Similarity Framework Approach". In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing@ LREC-COLING 2024, 2024, pp. 34-39.
99. Wang, L., Du, C., Zhao, P., Luo, C., Zhu, Z., Qiao, B., Zhang, W., Lin, Q., Rajmohan, S., Zhang, D. and Zhang, Q., "Contrastive Learning with Negative Sampling Correction". arXiv preprint arXiv:2401.08690, 2024.
100. Nie, Y., Nguyen, N.H., Sinthong, P. and Kalagnanam, J., "A time series is worth 64 words: Long-term forecasting with transformers". arXiv preprint arXiv:2211.14730, 2022.
101. Ericson, L., Zhu, X., Han, X., Fu, R., Li, S., Guo, S. and Hu, P., "Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review". arXiv preprint arXiv:2401.10370, 2024.
102. Liu, Y., Wu, H., Wang, J. and Long, M., "Non-stationary transformers: Exploring the stationarity in time series forecasting". Advances in Neural Information Processing Systems, vol. 35, pp. 9881-9893, 2022.
103. Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.H. and Choo, J., "Reversible instance normalization for accurate time-series forecasting against distribution shift". In International conference on learning representations, 2021.
104. Huang, H., Chen, M. and Qiao, X., "Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns". In The Twelfth International Conference on Learning Representations, 2023.
105. Ansari, A.F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S.S., Arango, S.P., Kapoor, S. and Zschiegner, J., "Chronos: Learning the Language of Time Series". arXiv preprint arXiv:2403.07815, 2024.
106. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W. and Liu, P.J., "Exploring the limits of transfer learning with a unified text-to-text transformer". Journal of machine learning research, vol. 21(140), pp. 1-67, 2020.
107. Chang, C., Wang, W.Y., Peng, W.C. and Chen, T.F., "Llm4ts: Aligning pre-trained llms as data-efficient time-series forecasters". ACM Transactions on Intelligent Systems and Technology, vol. 16(3), pp.1-20, 2025. [DOI:10.1145/3719207]
108. Zhou, T., Niu, P., Sun, L. and Jin, R., "One fits all: Power general time series analysis by pretrained lm". Advances in neural information processing systems, vol. 36, pp.43322-43355, 2024.
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