{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:36:54Z","timestamp":1782855414646,"version":"3.54.5"},"reference-count":74,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["NSTC 114-2221-E-033-017"],"award-info":[{"award-number":["NSTC 114-2221-E-033-017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan\u2019s top 50 listed companies. Five types of word embeddings\u2014one-hot, TF-IDF, CBOW, skip-gram, and BERT\u2014are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive R2 under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions.<\/jats:p>","DOI":"10.3390\/bdcc9100263","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T16:33:22Z","timestamp":1760632402000},"page":"263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Chinese Financial News Analysis for Sentiment and Stock Prediction: A Comparative Framework with Language Models"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1646-5190","authenticated-orcid":false,"given":"Hsiu-Min","family":"Chuang","sequence":"first","affiliation":[{"name":"Department of Information and Computer Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan City 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiang-Chih","family":"He","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan City 320314, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Che","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Finance and Cooperative Management, College of Business, National Taipei University, No. 151, University Rd., Sanxia Dist., New Taipei City 237303, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","article-title":"Giving content to investor sentiment: The role of media in the stock market","volume":"62","author":"Tetlock","year":"2007","journal-title":"J. Financ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., and Shirai, K. (2015, January 26\u201331). Topic modeling based sentiment analysis on social media for stock market prediction. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China.","DOI":"10.3115\/v1\/P15-1131"},{"key":"ref_3","unstructured":"Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shen, Y., and Zhang, P.K. (2024, January 26\u201328). Financial sentiment analysis on news and reports using large language models and FinBERT. Proceedings of the 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS62053.2024.10796670"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mo, K., Liu, W., Xu, X., Yu, C., Zou, Y., and Xia, F. (2024, January 17\u201319). Fine-tuning Gemma-7B for enhanced sentiment analysis of financial news headlines. Proceedings of the 2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI), Changsha, China.","DOI":"10.1109\/ICETCI61221.2024.10594605"},{"key":"ref_6","first-page":"272","article-title":"A time series analysis-based stock price prediction using machine learning and deep learning models","volume":"6","author":"Mehtab","year":"2020","journal-title":"Int. J. Bus. Forecast. Mark. Intell."},{"key":"ref_7","first-page":"4758698","article-title":"Research on stock price time series prediction based on deep learning and autoregressive integrated moving average","volume":"2022","author":"Xiao","year":"2022","journal-title":"Sci. Program."},{"key":"ref_8","first-page":"8389","article-title":"StockMixer: A simple yet strong MLP-based architecture for stock price forecasting","volume":"38","author":"Fan","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_9","unstructured":"Hiew, J.Z.G., Huang, X., Mou, H., Li, D., Wu, Q., and Xu, Y. (2019). BERT-based financial sentiment index and LSTM-based stock return predictability. arXiv."},{"key":"ref_10","unstructured":"Gu, W.J., Zhong, Y.H., Li, S.Z., Wei, C.S., Dong, L.T., Wang, Z.Y., and Yan, C. (2024, January 15\u201317). Predicting stock prices with FinBERT-LSTM: Integrating news sentiment analysis. Proceedings of the 2024 8th International Conference on Cloud and Big Data Computing (ICCBDC), Oxford, UK."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ho, T.T., and Huang, Y. (2021). Stock price movement prediction using sentiment analysis and candlestick chart representation. Sensors, 21.","DOI":"10.3390\/s21237957"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nguyen, N.H., Nguyen, T.T., and Ngo, Q.T. (2025). DASF-Net: A multimodal framework for stock price forecasting with diffusion-based graph learning and optimized sentiment fusion. J. Risk Financ. Manag., 18.","DOI":"10.3390\/jrfm18080417"},{"key":"ref_13","unstructured":"Koval, R., Andrews, N., and Yan, X. (2025). Multimodal language models with modality-specific experts for financial forecasting from interleaved sequences of text and time series. arXiv."},{"key":"ref_14","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_15","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_18","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2019). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"ref_20","unstructured":"Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., and Vaughan, A. (2024). The LLaMA 3 herd of models. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kogan, S., Levin, D., Routledge, B.R., Sagi, J.S., and Smith, N.A. (June, January 31). Predicting risk from financial reports with regression. Proceedings of the Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2009), Boulder, CO, USA.","DOI":"10.3115\/1620754.1620794"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1002\/asi.23062","article-title":"Good debt or bad debt: Detecting semantic orientations in economic texts","volume":"65","author":"Malo","year":"2014","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). GloVe: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_24","unstructured":"Ding, X., Zhang, Y., Liu, T., and Duan, J. (2015, January 25\u201331). Deep learning for event-driven stock prediction. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, Y., and Cohen, S.B. (2018, January 15\u201320). Stock movement prediction from tweets and historical prices. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1183"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Z., Huang, D., Huang, K., Li, Z., and Zhao, J. (2021, January 19\u201326). FinBERT: A pre-trained financial language representation model for financial text mining. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2020\/622"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24883","DOI":"10.1007\/s00521-024-10603-6","article-title":"Chinese fine-grained financial sentiment analysis with large language models","volume":"37","author":"Lan","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/j.1540-6261.2010.01625.x","article-title":"When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks","volume":"66","author":"Loughran","year":"2011","journal-title":"J. Financ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, Z., Liu, W., Bian, J., Liu, X., and Liu, T.Y. (2018, January 5\u20139). Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM 2018), Los Angeles, CA, USA.","DOI":"10.1145\/3159652.3159690"},{"key":"ref_30","unstructured":"Tang, Y., and Yang, Y. (2025). FinMTEB: Finance massive text embedding benchmark. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, X., Wen, F., Chu, B., Fu, Z., Lin, Q., Liu, J., and Yang, Z. (2025, January 3\u20137). FinBERT2: A specialized bidirectional encoder for bridging the gap in finance-specific deployment of large language models. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025), Barcelona, Spain.","DOI":"10.1145\/3711896.3737219"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7653","DOI":"10.1016\/j.eswa.2014.06.009","article-title":"Text mining for market prediction: A systematic review","volume":"41","author":"Nassirtoussi","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Akita, R., Yoshihara, A., Matsubara, T., and Uehara, K. (2016, January 26\u201329). Deep learning for stock prediction using numerical and textual information. Proceedings of the 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan.","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.ins.2014.03.096","article-title":"The effect of news and public mood on stock movements","volume":"278","author":"Li","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., and Cottrell, G. (2017). A dual-stage attention-based recurrent neural network for time series prediction. arXiv.","DOI":"10.24963\/ijcai.2017\/366"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, K., Zhou, Y., and Dai, F. (November, January 29). An LSTM-based method for stock returns prediction: A case study of the China stock market. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA.","DOI":"10.1109\/BigData.2015.7364089"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nelson, D.M., Pereira, A.C., and de Oliveira, R.A. (2017, January 14\u201319). Stock market\u2019s price movement prediction with LSTM neural networks. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bao, W., Yue, J., and Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long short-term memory. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180944"},{"key":"ref_40","unstructured":"Priya, S.B., Kumar, M., and JD, N.P. (2025, January 20\u201322). Advanced financial sentiment analysis using FinBERT to explore sentiment dynamics. Proceedings of the 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Tirunelveli, India."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Agarwal, S., Wadhwa, A., and Shah, R. (2020, January 16\u201320). Deep attentive learning for stock movement prediction from social media text and company correlations. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.676"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Karada\u015f, F., Eravc\u0131, B., and \u00d6zbayo\u011flu, A.M. (2025). Multimodal stock price prediction. arXiv.","DOI":"10.5220\/0013174500003890"},{"key":"ref_43","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., and Azhar, F. (2023). LLaMA: Open and efficient foundation language models. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e110","DOI":"10.1002\/ail2.110","article-title":"Earnings call scripts generation with large language models using few-shot learning prompt engineering and fine-tuning methods","volume":"6","author":"Nath","year":"2025","journal-title":"Appl. AI Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhang, B., Wang, N., Guo, C., Zhang, X., Lin, L., Wang, J., Zhou, T., Guan, M., and Zhang, R. (2024). FinRobot: An open-source AI agent platform for financial applications using large language models. arXiv.","DOI":"10.2139\/ssrn.4841493"},{"key":"ref_47","unstructured":"Huang, J., Xiao, M., Li, D., Jiang, Z., Yang, Y., Zhang, Y., Qian, L., Wang, Y., Peng, X., and Ren, Y. (2024). Open-FinLLMs: Open multimodal large language models for financial applications. arXiv."},{"key":"ref_48","unstructured":"Bigeard, A., Nashold, L., Krishnan, R., and Wu, S. (2025). Finance Agent Benchmark: Benchmarking LLMs on real-world financial research tasks. arXiv."},{"key":"ref_49","unstructured":"Xie, Q., Huang, J., Li, D., Chen, Z., Xiang, R., Xiao, M., Yu, Y., Somasundaram, V., Yang, K., and Yuan, C. (2024, January 25\u201326). FinNLP-AgentSCEN-2024 Shared Task: Financial challenges in large language models\u2014FinLLMs. Proceedings of the Eighth Financial Technology and Natural Language Processing and the First Agent AI for Scenario Planning (FinNLP-AgentSCEN 2024), Bangkok, Thailand."},{"key":"ref_50","unstructured":"Kumar, S., ElKholy, M., Liu, D., and Boulenger, A. (2025, January 20\u201321). Bridging the gap: Efficient cross-lingual NER in low-resource financial domain. Proceedings of the Joint Workshop of the Ninth Financial Technology and Natural Language Processing (FinNLP), the Sixth Financial Narrative Processing (FNP), and the First Workshop on Large Language Models for Finance and Legal (LLMFinLegal), Luxembourg."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Churi, A., Chakraborty, D., Khatwani, R., Pinto, G., Shah, P., and Sekhar, R. (2023, January 9\u201310). Stock price prediction using deep learning and sentiment analysis. Proceedings of the 2023 2nd International Conference on Futuristic Technologies (INCOFT), Coimbatore, India.","DOI":"10.1109\/INCOFT60753.2023.10425124"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e12608","DOI":"10.1111\/coin.12608","article-title":"ResNLS: An improved model for stock price forecasting","volume":"40","author":"Jia","year":"2024","journal-title":"Comput. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, Q., Kamaruddin, N., Yuhaniz, S.S., and Al-Jaifi, H.A.A. (2024). Forecasting stock price changes using long short-term memory neural network with symbolic genetic programming. Sci. Rep., 14.","DOI":"10.1038\/s41598-023-50783-0"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114362","DOI":"10.1016\/j.dss.2024.114362","article-title":"Revisiting time-varying dynamics in stock market forecasting: A multi-source sentiment analysis approach with large language model","volume":"190","author":"Shao","year":"2025","journal-title":"Decis. Support Syst."},{"key":"ref_55","unstructured":"He, S., and Gu, S. (2021). Multi-modal attention network for stock movements prediction. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s40854-023-00519-w","article-title":"A hybrid model for stock price prediction based on multi-view heterogeneous data","volume":"10","author":"Long","year":"2024","journal-title":"Financ. Innov."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hayashi, Y., and Yanagimoto, H. (2018). Headline generation with recurrent neural network. New Trends in E-Service and Smart Computing, Springer International Publishing.","DOI":"10.1007\/978-3-319-70636-8_6"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wasson, M. (1998, January 10\u201314). Using leading text for news summaries: Evaluation results and implications for commercial summarization applications. Proceedings of the 17th International Conference on Computational Linguistics (COLING 1998), Montreal, QC, Canada.","DOI":"10.3115\/980432.980791"},{"key":"ref_59","unstructured":"GoEast Mandarin (2025, October 05). Beginner\u2019s Guide to Punctuation in Chinese. Available online: https:\/\/goeastmandarin.com\/beginners-guide-to-punctuation-in-chinese\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Chapman and Hall\/CRC.","DOI":"10.1201\/9781315139470"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","article-title":"The regression analysis of binary sequences","volume":"20","author":"Cox","year":"1958","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_65","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Lopez-Lira, A., and Tang, Y. (2023). Can ChatGPT forecast stock price movements? Return predictability and large language models. arXiv.","DOI":"10.2139\/ssrn.4412788"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1086\/260062","article-title":"The pricing of options and corporate liabilities","volume":"81","author":"Black","year":"1973","journal-title":"J. Political Econ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1093\/rfs\/6.2.327","article-title":"A closed-form solution for options with stochastic volatility with applications to bond and currency options","volume":"6","author":"Heston","year":"1993","journal-title":"Rev. Financ. Stud."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1093\/rfs\/hhn004","article-title":"The spline-GARCH model for low-frequency volatility and its global macroeconomic causes","volume":"21","author":"Engle","year":"2008","journal-title":"Rev. Financ. Stud."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1093\/rfs\/hhaa009","article-title":"Empirical asset pricing via machine learning","volume":"33","author":"Gu","year":"2020","journal-title":"Rev. Financ. Stud."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/10\/263\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:16:19Z","timestamp":1760634979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/10\/263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"references-count":74,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["bdcc9100263"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9100263","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,16]]}}}