Elamur Research⌕
☰
home / papers / 2026 / Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities

Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities

Paper · 2026 · Nikita Tatarinov, Siddhant Sukhani, Agam Shah et al.

Summary

Financial NLP has grown quickly at major NLP venues, but earlier surveys chose papers by hand and often focused on a single method or task. This GEM 2026 survey builds a systematic corpus instead. The authors filtered abstracts from 38 NLP conferences and workshops (ACL, EMNLP, NAACL, COLING, LREC, FinNLP and others) by finance keywords. This produced 374 papers from 2017–2024, which they manually narrowed to 221 that actually address financial problems. Each paper was coded on 11 dimensions, including tasks, methods, datasets, metrics, code availability, authorship and funding, and placed in one of four task categories.

The field leans heavily on sentiment analysis, information extraction and stock forecasting. It mostly reports generic ML scores, seldom tests on crisis periods, and relies mainly on English, U.S.-centric data. General-purpose PLMs/LLMs have displaced custom architectures, while code and data sharing has improved. The survey's value is as a map of evaluation gaps. It points toward finance-aware metrics, stress testing, multilingual data, and efficient or interpretable models.

Key points

  • Corpus construction: 374 candidate papers were collected. The authors removed 88 shared-task papers (e.g., SemEval-2017, FinCausal-2022) and 65 papers with no real financial application, leaving 221 for analysis.
  • Choice of 2017 as the start year: over 94% of matching papers appeared from 2017 onward, which coincides with the arrival of transformers.
  • Four task categories: Financial Forecast, Financial Resources, Financial Applications, and Finance-Related. Risk assessment, macroeconomic and cryptocurrency forecasting, XAI and privacy-preserving methods remain underexplored.
  • Metric recommendations: forecasting papers mostly use accuracy, F1 or MSE. The authors recommend adding Sharpe Ratio, Maximum Drawdown and Cumulative Return, and testing on crisis windows such as 2008–2009 and 2020–2021.
  • Open-source trend: open code and data have become common since 2021. However, broken links and empty repositories still hinder reproducibility.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Georgia Institute of TechnologyAtlanta · United States133 works · 24,497 citations

 Fields, topics and concepts

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksDecision Sciences · 209 worksGeneral Social Sciences · 227 worksManagement Science and Operations Research · 108 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Nikita Tatarinov, Siddhant Sukhani, Agam Shah and Sudheer Chava. 2026. Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities. Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.gem-main.65

@inproceedings{tatarinov2026language,
  title = {{Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities}},
  author = {Nikita Tatarinov, Siddhant Sukhani, Agam Shah and Sudheer Chava},
  booktitle = {Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)},
  year = {2026},
  doi = {10.18653/v1/2026.gem-main.65},
  url = {https://doi.org/10.18653/v1/2026.gem-main.65}
}

References

213 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Emmanuel A. Abbe, Amir E. Khandani, and Andrew W. Lo. 2012. Privacy-preserving methods for sharing financial risk exposures. American Economic Review, 102(3):65–70. find
  2. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  3. Angus Addlesee. 2024. Grounding LLMs to in-prompt instructions: Reducing hallucinations caused by static pre-training knowledge. In Proceedings of Safety4ConvAI: The Third Workshop on Safety for Conversational AI @ LREC-COLING 2024, pages 1–7, Torino, Italia. ELRA and ICCL. find
  4. Toyin D. Aguda, Suchetha Siddagangappa, Elena Kochkina, Simerjot Kaur, Dongsheng Wang, and Charese Smiley. 2024. Large language models as financial data annotators: A study on effectiveness and efficiency. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Langua… find
  5. Noujoud Ahbali, Xinyuan Liu, Albert Nanda, Jamie Stark, Ashit Talukder, and Rupinder Paul Khandpur. 2022. Identifying Corporate Credit Risk Sentiments from Financial News. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lang… find
  6. Maximilian Ahrens and Michael McMahon. 2021. Extracting economic signals from central bank speeches. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 93–114, Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  7. Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, Arpit Patil, Oana Cocarascu, and Elena Simperl. 2023. Exploring the numerical reasoning capabilities of language models: A comprehensive analysis on tabular data. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15…
  8. Bloomberg L.P. 2015. 2015 data for good exchange: Data science and its impact on society. Accessed: 2025-03-05. find
  9. Emmanuel Alanis, Sudheer Chava, and Agam Shah. 2022. Benchmarking machine learning models to predict corporate bankruptcy. arXiv preprint arXiv:2212.12051. — [arXiv:2212.12051](https://arxiv.org/abs/2212.12051) find
  10. Bloomberg L.P. 2025a. Bloomberg news api / data subscriptions. https://www.bloomberg.com/su bscriptions/uspaid. Accessed: 2025-07-21. ↗
  11. Imad Aldasoro and 1 others. 2025. Predicting financial market stress with machine learning. Technical report, Bank for International Settlements (BIS). find
  12. Dogu Araci. 2019a. Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063. find
  13. Dogu Araci. 2019b. Finbeunknownrt: Financial sentiment analysis with pre-trained language models. Preprint, arXiv:1908.10063. find
  14. Henri Arno, Klaas Mulier, Joke Baeck, and Thomas Demeester. 2023. From numbers to words: Multimodal bankruptcy prediction using the ECL dataset. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 11–21, Bali, Indonesia. Association for Computational L… find
  15. Piyush Arora and Priya Radhakrishnan. 2020. AMEX AI-labs: An investigative study on extractive summarization of financial documents. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pages 137–142, Barcelona, Spain (Online). COL-ING. find
  16. Backtrader Team. 2015. backtrader: Python backtesting & trading library. https://www.backtrader.com/. Open-source project (GPL-3.0), accessed 2025-07-21. ↗
  17. David H. Bailey, Jonathan Borwein, Marcos López de Prado, and Qiji Jim Zhu. 2015. The probability of backtest overfitting. Journal of Computational Finance. Forthcoming. find
  18. Sudhandar Balakrishnan, Yihao Fang, and Xiaodan Zhu. 2022. Exploring robustness of prefix tuning in noisy data: A case study in financial sentiment analysis. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 78–88, Abu Dhabi, United Arab Em… find
  19. Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, and Zumrut Muftuoglu. 2021. Privacy enabled financial text classification using differential privacy and federated learning. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 50–55, Punta Cana, Dominican Republic. A… find
  20. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022.
  21. Bloomberg L.P. 2025b. Bloomberg terminal: Real-time data, research, and analytics. https://www.bloomb erg.com/professional/products/bloomberg-t erminal/. Accessed: 2025-07-21. ↗
  22. Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33.
  23. Christopher Burger, Lingwei Chen, and Thai Le. 2023. “are your explanations reliable?” investigating the stability of LIME in explaining text classifiers by marrying XAI and adversarial attack. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12831–128… find
  24. Yupeng Cao, Haohang Li, Yangyang Yu, Shashidhar Reddy Javaji, Yueru He, Jimin Huang, Zining Zhu, Qianqian Xie, Xiao yang Liu, Koduvayur Subbalakshmi, Meikang Qiu, Sophia Ananiadou, and Jian-Yun Nie. 2025. Finaudio: A benchmark for audio large language models in financial applications. Preprint, arXi… — [arXiv:2503.20990](https://arxiv.org/abs/2503.20990) find
  25. Tanmoy Chakraborty and 1 others. 2016. Predicting socio-economic indicators using news events. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. find
  26. Yash Chaudhary. 2020. Stock-market sentiment dataset. find
  27. Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020a. Issues and perspectives from 10,000 annotated financial social media data. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6106–6110, Marseille, France. European Language Resources Association. find
  28. Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020b. Nlp in fintech applications: Past, present and future. Preprint, arXiv:2005.01320. — [arXiv:2005.01320](https://arxiv.org/abs/2005.01320) find
  29. Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, and Hsin-Hsi Chen. 2022a. An overview of financial technology innovation. In Companion Proceedings of the Web Conference 2022, WWW ’22, page 572–575, New York, NY, USA. Association for Computing Machinery. find
  30. Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Yang Wang. 2020c. HybridQA: A dataset of multi-hop question answering over tabular and textual data. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1026–1036, Online. Association for Computation…
  31. University of Cambridge Department of Land Economy. 2003. Long-term capital management: Lessons from a collapse. Technical report, University of Cambridge. Accessed: 2025-07-21. find
  32. Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, and William Yang Wang. 2021. FinQA: A dataset of numerical reasoning over financial data. In Proceedings of the 2021 Conference on Empirical Methods in Natural…
  33. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
  34. Zhiyu Chen, Shiyang Li, Charese Smiley, Zhiqiang Ma, Sameena Shah, and William Yang Wang. 2022b. ConvFinQA: Exploring the chain of numerical reasoning in conversational finance question answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6279–6…
  35. Haibo Ding and Ellen Riloff. 2018. Human needs categorization of affective events using labeled and unlabeled data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1919–19… find
  36. Jaeyoung Choe, Keonwoong Noh, Nayeon Kim, Seyun Ahn, and Woohwan Jung. 2023. Exploring the impact of corpus diversity on financial pretrained language models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2101–2112, Singapore. Association for Computational Linguisti… find
  37. Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2014. Using structured events to predict stock price movement: An empirical investigation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1415– 1425, Doha, Qatar. Association for Computational… find
  38. Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, Chryssi Giannitsarou, Flavio Toxvaerd, and Nigel Collier. 2022. Incorporating stock market signals for Twitter stance detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers… find
  39. Jason Cornez. 2018. Market-moving event detection: Latest innovations. https://www.ravenpack.com/blog/event-detection-innovations/. Accessed: 2025-07-21. ↗
  40. Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh, and Brian Davis. 2017. SemEval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017),… find
  41. Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, and Ruslan Salakhutdinov. 2019. Transformer-XL: Attentive language models beyond a fixed-length context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2978–2988, Florence, Italy. Assoc… find
  42. Marcos López de Prado. 2018. Advances in Financial Machine Learning. John Wiley & Sons. find
  43. Mengming Michael Dong, Theophanis C. Stratopoulos, and Victor Xiaoqi Wang. 2024. A scoping review of chatgpt research in accounting and finance. International Journal of Accounting Information Systems, 55:100715. find
  44. Kangning Du, Erik Cambria, and 1 others. 2023. Fin-senticnet: A concept-level lexicon for financial sentiment analysis. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico. IEEE. find
  45. Hanyu Duan, Yi Yang, and Kar Yan Tam. 2021. Learning numeracy: A simple yet effective number embedding approach using knowledge graph. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2597–2602, Punta Cana, Dominican Republic. Association for Computational Linguistics. find
  46. Sarah Elhammadi, Laks V.S. Lakshmanan, Raymond Ng, Michael Simpson, Baoxing Huai, Zhefeng Wang, and Lanjun Wang. 2020. A high precision pipeline for financial knowledge graph construction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 967–977, Barcelona, Spa… find
  47. EODHD. 2024. Exploring alternatives to the yahoo finance api. https://medium.com/@eodhd/expl oring-alternatives-to-the-yahoo-finance -api-aa0415085cc5. API discontinued and paywalled CSV access reported via Reddit and verified; Accessed: 2025-07-21. ↗
  48. Stefan Erben and Andreas Waldis. 2024. ScamSpot: Fighting financial fraud in Instagram comments. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 71–81, St. Julians, Malta. Association for Computational Lingu… find
  49. Eurostat. 2025. Eurostat database. https://ec.eur opa.eu/eurostat/data/database. [Accessed: September 30, 2025]. ↗
  50. FactSet Insight. 2025. Company events transcript datafeed. https://insight.factset.com/re sources/at-a-glance-document-distributor -xml-company-events-transcript-datafeed. Accessed: 2025-07-21. ↗
  51. Eugene F. Fama and Kenneth R. French. 2024. Fama-french data library. Accessed: 2024-03-05. find
  52. Wei Fan, Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, and Yanjie Fu. 2023. Dish-ts: A general paradigm for alleviating distribution shift in time series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 7522–7529. find
  53. Federal Reserve Bank of St. Louis. 2024. FRED, Federal Reserve Economic Data. Accessed: 2024-03-05. find
  54. Federal Reserve History. 2025. Near failure of long-term capital management. https://www.fede ralreservehistory.org/essays/ltcm-near-f ailure. Accessed: 2025-07-21. ↗
  55. Federal Reserve System. 2025. Fomc historical materials: Policy statements and meeting transcripts. https://www.federalreserve.gov/monetaryp olicy/fomc_historical.htm. Accessed: 2025-07-21. ↗
  56. Katja Filippova, Mihai Surdeanu, Massimiliano Ciaramita, and Hugo Zaragoza. 2009. Company-oriented extractive summarization of financial news. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pages 246– 254, Athens, Greece. Association for Computational Linguisti… find
  57. Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, and Svitlana Vyetrenko. 2024. Evaluating large language models on time series feature understanding: A comprehensive taxonomy and benchmark. In Proceedings of the 2024 Conference on Empirical Methods in Natural La… find
  58. Ruizhuo Gao, Zeqi Zhang, Zhenning Shi, Dan Xu, Weijuan Zhang, and Dewei Zhu. 2021. A review of natural language processing for financial technology. In International Symposium on Artificial Intelligence and Robotics 2021, volume 11884, page 118840U. International Society for Optics and Photonics, SP… find
  59. Tianyu Gao and 1 others. 2023. Large language models in financial nlp: A survey. arXiv preprint arXiv:2306.01511. — [arXiv:2306.01511](https://arxiv.org/abs/2306.01511) find
  60. Google News. 2025. Google news aggregator. https://news.google.com/home. Accessed: 2025-07-21. ↗
  61. Yue Guo, Chenxi Hu, and Yi Yang. 2023. Predict the future from the past? on the temporal data distribution shift in financial sentiment classifications. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 1029– 1038, Singapore. Association for Computation… find
  62. Faizal Hafiz, Jan Broekaert, and Akshya Swain. 2023. Evolution of neural architectures for financial forecasting: A note on data incompatibility during crisis periods. arXiv preprint arXiv:2311.14604. — [arXiv:2311.14604](https://arxiv.org/abs/2311.14604) find
  63. Hassan Hamad, Abhinav Kumar Thakur, Nijil Kolleri, Sujith Pulikodan, and Keith Chugg. 2024. FIRE: A dataset for financial relation extraction. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3628–3642, Mexico City, Mexico. Association for Computational Linguistics. find
  64. Adam Hayes. 2024. Maximum drawdown (mdd): Definition and formula. https://www.investoped ia.com/terms/m/maximum-drawdown-mdd.asp. Accessed: 2025-07-21. ↗
  65. Seyed Hosseini, Mohammad Reza Mousavi, Pooya Amirian, and ... 2018. Auditnlp: Towards auditable and interpretable nlp systems in finance. In Proceedings of the 2nd Workshop on Interpretability and Robustness in Natural Language Processing, page 28–35. find
  66. Yongli Hu, Puman Chen, Tengfei Liu, Junbin Gao, Yanfeng Sun, and Baocai Yin. 2021. Hierarchical attention transformer networks for long document classification. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–7. find
  67. Allen Huang, Hui Wang, and Yi Yang. 2022. Finbert: A large language model for extracting information from financial text†. Contemporary Accounting Research, 40. find
  68. Ta-wei Huang, Jia-huei Ju, Yu-shiang Huang, Chengwei Lin, Yi-shyuan Chiang, and Chuan-ju Wang. 2023. FISH: A financial interactive system for signal highlighting. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pa… find
  69. Yusheng Huang, Ning Hu, Kunping Li, Nan Wang, and Zhouhan Lin. 2024. Extracting financial events from raw texts via matrix chunking. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7035–7044, Torino,… find
  70. International Monetary Fund. 2025. Imf data. https://data.imf.org/. [Accessed: September 30, 2025]. ↗
  71. Investopedia. 2007. Cumulative return: Definition, calculation, and example. https://www.investoped ia.com/terms/c/cumulativereturn.asp. Accessed: 2025-07-21. ↗
  72. Investopedia. 2025a. Sharpe Ratio Definition. Accessed: 2025-03-05. find
  73. Investopedia. 2025b. Stress Testing Definition. Accessed: 2025-03-05. find
  74. Jainendra Kumar Jain and Ruchit Agrawal. 2024. FB-GAN: A novel neural sentiment-enhanced model for stock price prediction. 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, a… find
  75. Rasmus Jørgensen, Oliver Brandt, Mareike Hartmann, Xiang Dai, Christian Igel, and Desmond Elliott. 2023. MultiFin: A dataset for multilingual financial NLP. In Findings of the Association for Computational Linguistics: EACL 2023, pages 894–909, Dubrovnik, Croatia. Association for Computational Lingu… find
  76. Jia-Huei Ju, Yu-Shiang Huang, Cheng-Wei Lin, Che Lin, and Chuan-Ju Wang. 2023. A compare-and-contrast multistage pipeline for uncovering financial signals in financial reports. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1… find
  77. Kaggle Contributor. 2021. Reddit cryptocurrency data for august 2021. https://www.kaggle.com/datas ets/pavellexyr/Reddit-cryptocurrency-dat a-for-august-2021. Accessed: 2025-07-21. ↗
  78. Isaak Kamau. 2024. When to use xgboost over llm: A case study in stock market analysis. https://medi um.com/@isaakmwangi2018/when-to-use-xgboo st-over-llm-a-case-study-in-stock-marke t-analysis-2f46571ad536. Accessed: 2025-07-21. find
  79. Sudipta Kar, Suraj Maharjan, and Thamar Solorio. 2017. RiTUAL-UH at SemEval-2017 task 5: Sentiment analysis on financial data using neural networks. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 877– 882, Vancouver, Canada. Association for Computation… find
  80. Amir Khandani, Adlai Kim, and Andrew Lo. 2021. A practical machine learning approach to credit scoring. Journal of Financial Data Science, 3(2):55–72. find
  81. Urvashi Khanna, Samira Ghodratnama, Diego Molla, and Amin Beheshti. 2022. Transformer-based models for long document summarisation in financial domain. In Financial Narrative Processing Workshop (4th: 2022), pages 73–78. European Language Resources Association (ELRA). find
  82. Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, and Pawan Goyal. 2024. Parameter-efficient instruction tuning of large language models for extreme financial numeral labelling. In Proceedings of the 2024 Conference of the North Amer… find
  83. Donggyu Kim, Garam Lee, and Sungwoo Oh. 2022a. Toward privacy-preserving text embedding similarity with homomorphic encryption. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 25–36, Abu Dhabi, United Arab Emirates (Hybrid). Association f… find
  84. Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2022b. Reversible instance normalization for accurate time-series forecasting against distribution shift. In International Conference on Learning Representations (ICLR). find
  85. Tony Klein and Thomas Walther. 2024. Advances in explainable artificial intelligence (xai) in finance. Finance Research Letters, 70:106358. find
  86. Kaggle Contributor. 2025. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/d atasets/kazanova/sentiment140. Accessed: 2025-07-21. ↗
  87. Jamshed Kaikaus, Jessen L. Hobson, and Robert Brunner. 2022. Truth or fiction: Multimodal learning applied to earnings calls. In 2022 IEEE International Conference on Big Data (Big Data). IEEE. Multimodal (text+audio) modeling for restatement and market movement prediction. find
  88. Tagore Rao Kosireddy, Jeffrey David Wall, and Evan Lucas. 2024. Exploring the readiness of prominent small language models for the democratization of financial literacy. In Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Grou… find
  89. Ross Koval, Nicholas Andrews, and Xifeng Yan. 2023. Forecasting earnings surprises from conference call transcripts. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8197–8209, Toronto, Canada. Association for Computational Linguistics. find
  90. Jonas Sebastian Krauss, Xiaotong A. Do, and Nicolas Huck. 2017. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the s&p 500. European Journal of Operational Research, 259(2):689–702. find
  91. In Proceedings of the Fourth ACM International Conference on AI in Finance, ICAIF ’23, page 374–382, New York, NY, USA. Association for Computing Machinery. find
  92. Yinheng Li, Shaofei Wang, Han Ding, and Hang Chen. 2024. Large language models in finance: A survey. Preprint, arXiv:2311.10723. — [arXiv:2311.10723](https://arxiv.org/abs/2311.10723) find
  93. Weixin Liang and et al. 2022. Understanding the modality gap in multi-modal contrastive learning. In Advances in Neural Information Processing Systems (NeurIPS). find
  94. KYCHub. 2025. What is adverse media screening? https://www.kychub.com/blog/adverse-media -screening/. Accessed: 2025-07-21. ↗
  95. Jean Lee, Nicholas Stevens, and Soyeon Caren Han. 2025. Large language models in finance (finllms). Neural Computing and Applications. find
  96. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the…
  97. Jiazheng Li, Linyi Yang, Barry Smyth, and Ruihai Dong. 2020a. Maec: A multimodal aligned earnings conference call dataset for financial risk prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM). find
  98. Jiazheng Li, Linyi Yang, Barry Smyth, and Ruihai Dong. 2020b. Maec: A multimodal aligned earnings conference call dataset for financial risk prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM ’20, page 3063–3070, New York, NY, USA. Associa… find
  99. Qi Li, Charles Lim, and Jianhua Huang. 2018. Credit scoring with historical bank data: Classical vs. ml-based approaches. Journal of Banking and Finance, 90:128–145. find
  100. X. Li and et al. 2024. Time-MMD: A multi-domain multimodal dataset for time series analysis. In NeurIPS 2024 Datasets and Benchmarks Track. find
  101. Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, and Xiaozhong Liu. 2023a. STINMatch: Semi-supervised semantic-topological iteration network for financial risk detection via news label diffusion. In Proceedings of the 202… find
  102. Yinheng Li, Shaofei Wang, Han Ding, and Hang Chen. 2023b. Large language models in finance: A survey. find
  103. Gili Lior, Yoav Goldberg, and Gabriel Stanovsky. 2024. Leveraging collection-wide similarities for unsupervised document structure extraction. In Findings of the Association for Computational Linguistics ACL 2024, pages 9538–9550, Bangkok, Thailand and virtual meeting. Association for Computational… find
  104. Yi-Ting Liou, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. Dynamic graph transformer for implicit tag recognition. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1426–1431, Online. Association for Computa… find
  105. Chuang Liu, Junzhuo Li, and Deyi Xiong. 2023. Tab-CQA: A tabular conversational question answering dataset on financial reports. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 196–207, Toronto, Canada. Association for Comp… find
  106. Jiaxin Liu, Yi Yang, and Kar Yan Tam. 2024. Beyond surface similarity: Detecting subtle semantic shifts in financial narratives. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2641–2652, Mexico City, Mexico. Association for Computational Linguistics. find
  107. Junhua Liu. 2024. A survey of financial ai: Architectures, advances and open challenges. Preprint, arXiv:2411.12747. — [arXiv:2411.12747](https://arxiv.org/abs/2411.12747) find
  108. Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Wang, Zhaoran Wang, and Jian Guo. 2022. Finrl-meta: Market environments and benchmarks for data-driven financial reinforcement learning. In NeurIPS 2022 Datasets and Benchmarks Track. find
  109. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. Preprint, arXiv:1907.11692.
  110. Zhuang Liu, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. 2020. Finbert: A pre-trained financial language representation model for financial text mining. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 4513–4519. International Joint… find
  111. Andrew W. Lo. 2002. The statistics of sharpe ratios. Financial Analysts Journal, 58(4):36–52. find
  112. Gustavo Lopez and 1 others. 2021. Rule-based and feature-engineered approaches for nlp in finance: A practical guide. Journal of Financial Data Science, 3(2):21–38. find
  113. Puneet Mathur, Atula Neerkaje, Malika Chhibber, Ramit Sawhney, Fuming Guo, Franck Dernoncourt, Sanghamitra Dutta, and Dinesh Manocha. 2022b. Monopoly: Financial prediction from monetary policy conference videos using multimodal cues. In Proceedings of the 30th ACM International Conference on Multime… find
  114. Vaibhav Mavi, Abulhair Saparov, and Chen Zhao. 2023. Retrieval-augmented chain-of-thought in semi-structured domains. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 178–191, Singapore. Association for Computational Linguistics. find
  115. Tim Loughran and Bill McDonald. 2011. When is a liability not a liability? textual analysis, dictionaries, and 10-ks. The Journal of Finance, 66(1):35–65. find
  116. Di Luo, Weiheng Liao, Shuqi Li, Xin Cheng, and Rui Yan. 2023. Causality-guided multi-memory interaction network for multivariate stock price movement prediction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12164–12176, Tor… find
  117. Liang Luo, Dong Li, Shoushan Liu, Guodong Wu, and Bin Li. 2018. Beyond polarity: Interpretable financial sentiment analysis with hierarchical query-driven attention. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pages 4244–4250. find
  118. Joel M. Mackenzie, J. Shane Culpepper, Roi Blanco, Matt Crane, Charles L. A. Clarke, and Jimmy Lin. 2018. Query driven algorithm selection in early stage retrieval. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages 396–404. find
  119. Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018. Www’18 open challenge: Financial opinion mining and question answering. In Companion Proceedings of the The Web Conference 2018 (WWW Companion), pages 1941–1942. ACM. find
  120. Xiliu Man, Tong Luo, and Jianwu Lin. 2019. Financial sentiment analysis(fsa): A survey. In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pages 617–622. find
  121. Robert Andrew Martin. 2021. Pyportfolioopt: portfolio optimization in python. Journal of Open Source Software, 6(61):3066. find
  122. Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, and Dinesh Manocha. 2022a. DocFin: Multimodal financial prediction and bias mitigation using semi-structured documents. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1933–19… find
  123. Martina Menzio, Davide Paris, and Elisabetta Fersini. 2024a. Unveiling currency market dynamics: Leveraging federal reserve communications for strategic investment insights. In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discov… find
  124. Martina Menzio, Davide Paris, and Elisabetta Fersini. 2024b. Unveiling currency market dynamics: Leveraging federal reserve communications for strategic investment insights. In Proceedings of the Joint Workshop of the 7th FinNLP, the 5th KDF, and the 4th ECONLP, pages 94–102. ELRA. find
  125. Denisa Millo, Blerina Vika, and Nevila Baci. 2024. Integrating natural language processing techniques of text mining into financial system: Applications and limitations. Preprint, arXiv:2412.20438. — [arXiv:2412.20438](https://arxiv.org/abs/2412.20438) find
  126. Kostadin Mishev, Ana Gjorgjevikj, Irena Vodenska, Lubomir T. Chitkushev, and Dimitar Trajanov. 2020. Evaluation of sentiment analysis in finance: From lexicons to transformers. IEEE Access, 8:131662– 131682. find
  127. Joydeb Mondal, Nagaraj Bhat, Pramir Sarkar, and Shahid Reza. 2022. ExpertNeurons at FinCausal 2022 task 2: Causality extraction for financial documents. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 128–130, Marseille, France. European Language Resources Associat… find
  128. Antonio Moreno-Ortiz, Javier Fernandez-Cruz, and Chantal Pérez Chantal Hernández. 2020. Design and evaluation of SentiEcon: a fine-grained economic/financial sentiment lexicon from a corpus of business news. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5065–5072,… find
  129. Philippe Morissette. 2025. bt: Flexible backtesting framework for python. https://pmorissette.github.io/bt/. Accessed: 2025-07-21; MIT License; GitHub repo starred 2.6k times :contentRefer-ence[oaicite:0]index=0. ↗
  130. Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, and Pawan Goyal. 2022. ECT-Sum: A new benchmark dataset for bullet point summarization of long earnings call transcripts. In Proceed… find
  131. Ani Nenkova and Kathleen McKeown. 2011. A survey of text summarization techniques. Foundations and Trends in Information Retrieval, 5(2):103–233. find
  132. NICE Actimize. 2023. Smarter communications surveillance with ai and nlp. https://info.niceactimiz e.com/rs/338-EJP-431/images/Smarter_Comm unications_Surveillance_with_AI_White_Pa per.pdf?version=0. Accessed: 2025-07-21. ↗
  133. Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, and Stefan Zohren. 2024. A survey of large language models for financial applications: Progress, prospects and challenges. Preprint, arXiv:2406.11903. — [arXiv:2406.11903](https://arxiv.org/abs/2406.11903) find
  134. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno…
  135. Phoenix Strategy Group. 2025. Ai-driven topic modeling for financial trends. https://www.phoenixs trategy.group/blog/ai-driven-topic-model ing-for-financial-trends. Accessed: 2025-07-21. ↗
  136. Yu Qin and Yi Yang. 2019. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 390–401, Florence, Italy. Association for Computational Linguistics. find
  137. QuantLib Team. 2000. Quantlib: Open-source software for quantitative finance. https://www.quantlib.org/docs.shtml. Accessed: 2025-07-21; initial release December 11, 2000; licensed under modified BSD. ↗
  138. Hao Niu, Yun Xiong, Xiaosu Wang, Wenjing Yu, Yao Zhang, and Weizu Yang. 2023. KeFVP: Knowledge-enhanced financial volatility prediction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11499–11513, Singapore. Association for Computational Linguistics. find
  139. Varshini Reddy, Rik Koncel-Kedziorski, Viet Lai, Michael Krumdick, Charles Lovering, and Chris Tanner. 2024. DocFinQA: A long-context financial reasoning dataset. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 445–458, Bangk… find
  140. Rodrigo Nogueira and Jimmy Lin. 2020. Document ranking with a pretrained sequence-to-sequence model. In Findings of the Association for Computational Linguistics: EMNLP 2020.
  141. Risk & Compliance. 2021. Lexicon surveillance is not dead. https://riskandcompliancemagaz ine.com/lexicon-surveillance-is-not-dead. Accessed: 2025-07-21. find
  142. Organisation for Economic Co-operation and Development. 2025. Oecd data. https://data.oecd.org/. [Accessed: September 30, 2025]. ↗
  143. Pau Rodriguez Inserte, Mariam Nakhlé, Raheel Qader, Gaetan Caillaut, and Jingshu Liu. 2023. Large language model adaptation for financial sentiment analysis. In Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing, pages 1–10, Bali, Indonesia. Association for Com… find
  144. Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek, and Omer Berat Sezer. 2020. Deep learning for financial applications : A survey. Applied Soft Computing, 93:106384. find
  145. Huzaifa Pardawala, Siddhant Sukhani, Agam Shah, Veer Kejriwal, Abhishek Pillai, Rohan Bhasin, Andrew DiBiasio, Tarun Mandapati, Dhruv Adha, and Sudheer Chava. 2025. Subjective-qa: Measuring subjectivity in earnings call transcripts’ qa through six-dimensional feature analysis. Preprint, arXiv:2410.2… — [arXiv:2410.20651](https://arxiv.org/abs/2410.20651) find
  146. Denis Peskoff, Adam Visokay, Sander Schulhoff, Benjamin Wachspress, Alan Blinder, and Brandon M. Stewart. 2023. Gpt deciphering fedspeak: Quantifying dissent among hawks and doves. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6529–6539. find
  147. Subhro Roy and Dan Roth. 2018. Mapping to declarative knowledge for word problem solving. Transactions of the Association for Computational Linguistics, 6:159–172.
  148. Yunxin Sang and Yang Bao. 2022. DialogueGAT: A graph attention network for financial risk prediction by modeling the dialogues in earnings conference calls. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1623–1633, Abu Dhabi, United Arab Emirates. Association for Com… find
  149. Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Ratn Shah. 2020a. Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8415–8426, Online.… find
  150. Ramit Sawhney, Mihir Goyal, Prakhar Goel, Puneet Mathur, and Rajiv Ratn Shah. 2021a. Multimodal multi-speaker merger & acquisition financial modeling: A new task, dataset, and neural baselines. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th In… find
  151. Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, and Rajiv Ratn Shah. 2020b. VolTAGE: Volatility forecasting via text audio fusion with graph convolution networks for earnings calls. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (E… find
  152. Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, and Rajiv Ratn Shah. 2021b. FAST: Financial news and tweet based time aware network for stock trading. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2164–2175, Online. As… find
  153. Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, and Rajiv Ratn Shah. 2021c. Quantitative day trading from natural language using reinforcement learning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pag… find
  154. Seeking Alpha. 2025. Earnings call transcripts. https://seekingalpha.com/earnings/earnings-cal l-transcripts. Accessed: 2025-07-21. ↗
  155. Hedwig G. Sekeres, Wilbert Heeringa, Wietse de Vries, Oscar Yde Zwagers, Martijn Wieling, and Goffe Th. Jensma. 2024. Developing infrastructure for low-resource language corpus building. In Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING… find
  156. Pavlo Seroyizhko, Zhanel Zhexenova, Muhammad Zohaib Shafiq, Fabio Merizzi, Andrea Galassi, and Federico Ruggeri. 2022. A sentiment and emotion annotated dataset for bitcoin price forecasting based on Reddit posts. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Pro… find
  157. Agam Shah, Suvan Paturi, and Sudheer Chava. 2023. Trillion dollar words: A new financial dataset, task & market analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6664–6679, Toronto, Canada. Association for Computationa… find
  158. Raj Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, and Diyi Yang. 2022. When FLUE meets FLANG: Benchmarks and large pre-trained language model for financial domain. In Proceedings of the 2022 Conference on Empirical Methods in Natur… find
  159. Anirudha Simha. 2021. Understanding tf-idf for machine learning. https://www.capitalone.com/t ech/machine-learning/understanding-tf-idf /. Accessed: 2025-07-21. ↗
  160. Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon, Yangfeng Ji, and Yanjun Qi. 2021. Perturbing inputs for fragile interpretations in deep natural language processing. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 420–434, Punta Cana, Domini… find
  161. S&P Global. 2025. S&p capital iq platform. https://www.capitaliq.com/CIQDotNet/Login-okta. aspx. Accessed: 2025-07-21. ↗
  162. S&P Global Market Intelligence. 2025. Machine-readable earnings call transcripts dataset. https://www.marketplace.spglobal.com/en/datase ts/machine-readable-transcripts-(45). Accessed: 2025-07-21. ↗
  163. StockTwits. 2025. Stocktwits: Social media for investors and traders. https://stocktwits.com/. Accessed: 2025-07-21. ↗
  164. Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645–3650, Florence, Italy. Association for Computational Linguistics.
  165. György Szarvas, Richárd Farkas, Róbert Busa-Fekete, Erzsébet Süveges, and Giulio Békés. 2007. Unsupervised relation extraction through pattern clustering. Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, page 419–427… find
  166. Hideki Takahashi, Yosuke Omori, and Takeo Watanabe. 2009. Designing trading strategies using financial and economic data. Japanese Economic Review, 60(2):183–200. find
  167. Aviv Tamar, Shie Mannor, and Huan Xu. 2012. Policy gradients with variance related risk criteria. In Proceedings of the 29th International Conference on Machine Learning (ICML). find
  168. Yixuan Tang, Yi Yang, Allen Huang, Andy Tam, and Justin Tang. 2023. FinEntity: Entity-level sentiment classification for financial texts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15465–15471, Singapore. Association for Computational Linguistics… find
  169. Alex Tarnopolski, Alexander Panchenko, and Preslav Nakov. 2019. Knowledge graph embeddings for financial entity resolution and link prediction. In Proceedings of the 4th Workshop on Representation Learning for NLP, page 150–161. find
  170. The Motley Fool. 2025. Earnings call transcripts. https://www.fool.com/earnings-call-transcrip ts/. Accessed: 2025-07-21. ↗
  171. The World Bank. 2025. World bank open data. https://data.worldbank.org/. Accessed: 2025-07-21. ↗
  172. Adrian Theuma and Ehsan Shareghi. 2024. Equipping language models with tool use capability for tabular data analysis in finance. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 90–103, St. Julian’s, Malta.… find
  173. Thomson Reuters. 2025. Streetevents transcripts portal. https://www.streetevents.com/login.aspx. Accessed: 2025-07-21. ↗
  174. TigerGraph. 2024. Knowledge graph and nlp revolutionize the way aml looks at peps. https://www.ti gergraph.com/uncategorized/knowledge-gra ph-and-nlp-revolutionize-the-way-aml-loo ks-at-peps-3/. Accessed: 2025-07-21. ↗
  175. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, and 1 others. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  176. UC Irvine. 2023. Reuters-21578 text categorization collection. https://huggingface.co/datasets/ ucirvine/reuters21578. Accessed: 2025-07-21. ↗
  177. U.S. BEA. 2025. Official bea website. https://www.bea.gov/. Accessed: 2025-07-21. ↗
  178. U.S. SEC. 2024. Edgar application programming interfaces (apis). https://www.sec.gov/search-fil ings/edgar-application-programming-inter faces. Accessed: 2025-07-21. ↗
  179. U.S. SEC. 2025. Sec edgar: Overview of the system. Accessed: 2025-03-05. find
  180. Vanity Fair Staff. 1998. Wall street lays another egg. Vanity Fair. Accessed: 2025-07-21. find
  181. Francielle Vargas, Fabrício Benevenuto, and Thiago Pardo. 2021. Toward discourse-aware models for multilingual fake news detection. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 210–218, Online. INCOMA Ltd. find
  182. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Preprint, arXiv:1706.03762.
  183. Wasin Waeosri. 2020. How to get mrn news analytics data via websocket api. https://developers.l seg.com/en/article-catalog/article/how-t o-get-mrn-news-analytics-data-via-elekt ron-websocket-api. Accessed: 2025-07-21. ↗
  184. Shengkun Wang, Taoran Ji, Jianfeng He, Mariam AL-Mutairi, Dan Wang, Linhan Wang, Min Zhang, and Chang-Tien Lu. 2024a. AMA-LSTM: Pioneering robust and fair financial audio analysis for stock volatility prediction. In Proceedings of the 2024 Conference of the North American Chapter of the Association… find
  185. Sida Wang and Christopher Manning. 2012. Baselines and bigrams: Simple, good sentiment and topic classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 90–94, Jeju Island, Korea. Association for Computational Linguis…
  186. Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, and Zhifei Li. 2019. Are you for real? detecting identity fraud via dialogue interactions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Pro… find
  187. Zezhong Wang, Siyang Hao, Inez Maria Zwetsloot, and Simon Trimborn. 2024b. Social network datasets on reddit financial discussion. Preprint, arXiv:2410.05002. — [arXiv:2410.05002](https://arxiv.org/abs/2410.05002) find
  188. Toni Whited. 2023. Costs and Benefits of Reproducibility in Finance and Economics. Harvard Data Science Review, 5(3). Https://hdsr.mitpress.mit.edu/pub/mnhzk8gq. find
  189. Xianchao Wu. 2020. Event-driven learning of systematic behaviours in stock markets. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2434–2444, Online. Association for Computational Linguistics. find
  190. Ziang Wu and 1 others. 2023. How powerful is chatgpt? arXiv preprint arXiv:2301.07597.
  191. X (formerly Twitter). 2025. X api documentation. https://developer.x.com/en/docs/x-api. Accessed: 2025-07-21. ↗
  192. Yan Xiao, Yaochu Jin, and Kuangrong Hao. 2024. Federated document-level biomedical relation extraction with localized context contrast. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7163–7173, Torin… find
  193. Frank Z Xing, Erik Cambria, and Roy E Welsch. 2017. Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1):49–73. find
  194. Yumo Xu and Shay B. Cohen. 2018a. Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1970–1980. find
  195. Yumo Xu and Shay B. Cohen. 2018b. Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1970–1979, Melbourne, Australia. Association for Computational Linguistics. find
  196. Yahoo Finance. 2025. Yahoo finance news feed. https://finance.yahoo.com/news/. Accessed: 2025-07-21. ↗
  197. Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023. Fingpt: Open-source financial large language models. Preprint, arXiv:2306.06031.
  198. Linyi Yang, Tin Lok James Ng, Barry Smyth, and Ri-uhai Dong. 2020a. Html: Hierarchical transformer-based multi-task learning for volatility prediction. In Proceedings of The Web Conference 2020, WWW ’20, page 441–451, New York, NY, USA. Association for Computing Machinery. find
  199. Yi Yang, Mark UY, and Allen Huang. 2020b. Finbert: A pretrained language model for financial communications. find
  200. Yi Yang and 1 others. 2020c. Finbert: A pretrained language model for financial communications. SSRN Electronic Journal. find
  201. Zheng Yangjia, Li Xia, Ma Junteng, and Chen Yuan. 2022. Fundamental analysis based neural network for stock movement prediction. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 973–984, Nanchang, China. Chinese Information Processing Society of China. find
  202. Zhilu Zhang, Procheta Sen, Zimu Wang, Ruoyu Sun, Zhengyong Jiang, and Jionglong Su. 2024. FinBPM: A framework for portfolio management-based financial investor behavior perception model. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (V… find
  203. Huaqin Zhao, Zhengliang Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Hanqi Jiang, Yi Pan, Junhao Chen, Yifan Zhou, Gengchen Mai, Ninghao Liu, and Tianming Liu. 2024a. Revolutionizing finance with llms: An overview of applications and insights. Preprint, arXiv:2…
  204. Yiyun Zhao, Prateek Singh, Hanoz Bhathena, Bernardo Ramos, Aviral Joshi, Swaroop Gadiyaram, and Saket Sharma. 2024b. Optimizing LLM based retrieval augmented generation pipelines in the financial domain. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Compu… find
  205. Zishuo Zhao, Ziyang Ma, Zhenzhou Lin, Jingyou Xie, Yinghui Li, and Ying Shen. 2024c. Source-free domain adaptation for aspect-based sentiment analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages… find
  206. Shun Zheng, Wei Cao, Wei Xu, and Jiang Bian. 2019. Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Process…
  207. Fan Zhou, Shengming Zhang, and Yi Yang. 2020. Interpretable operational risk classification with semi-supervised variational autoencoder. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 846–852, Online. Association for Computational Linguistics. find
  208. Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei Koh, and Chelsea Finn. 2022. Wild-time: A benchmark of in-the-wild distribution shift over time. In NeurIPS 2022 Datasets and Benchmarks Track. find
  209. Yifan Zhou, Yiming Liu, Qitian Wu, Shuya Li, Zhiguang Wang, Ruoxuan Xiong, Bin Liu, Panpan Zheng, and Fangjian Guo. 2024. Fnspid: A large-scale financial news-stock price impact dataset. https://arxiv.org/abs/2402.06698v1. Accessed: 2025-07-21. — [arXiv:2402.06698](https://arxiv.org/abs/2402.06698) ↗
  210. Chengxi Zhai, Yang Zhang, Dexin Zhao, and Eric H. Chi. 2019. Charting latency-sensitive natural language processing models. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, page 1099–1110. find
  211. Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021. TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics…
  212. Nadhem Zmandar, Tobias Daudert, Sina Ahmadi, Mahmoud El-Haj, and Paul Rayson. 2022. CoFiF plus: A French financial narrative summarisation corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1622–1639, Marseille, France. European Language Resources Associatio… find
  213. Jinan Zou, Haiyao Cao, Lingqiao Liu, Yuhao Lin, Ehsan Abbasnejad, and Javen Qinfeng Shi. 2022. Astock: A new dataset and automated stock trading based on stock-specific news analyzing model. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages… find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/tatarinov-2026-language-modeling-future-finance-survey/