{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T12:01:22Z","timestamp":1765454482698,"version":"3.46.0"},"reference-count":84,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Stock trading faces significant challenges due to market volatility and the complexity of integrating diverse data sources, such as financial texts and numerical market data. This paper proposes an innovative automated trading system that integrates advanced natural language processing (NLP) and deep reinforcement learning (DRL) to address these challenges. The system combines two novel components: PrimoGPT, a Transformer-based NLP model fine-tuned on financial texts using instruction-based datasets to generate actionable features like sentiment and trend direction, and PrimoRL, a DRL model that expands its state space with these NLP-derived features for enhanced decision-making precision compared to traditional DRL models like FinRL. An experimental evaluation over seven months of leading technology stocks reveals cumulative returns of up to 58.47% for individual stocks and 27.14% for a diversified portfolio, with a Sharpe ratio of 1.70, outperforming traditional and advanced benchmarks. This work advances AI-driven quantitative finance by offering a scalable framework that bridges qualitative analysis and strategic action, thereby fostering smarter and more equitable participation in financial markets.<\/jats:p>","DOI":"10.3390\/bdcc9120317","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T11:17:27Z","timestamp":1765451847000},"page":"317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Trading Framework Using LLM-Driven Features and Deep Reinforcement Learning"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3561-7089","authenticated-orcid":false,"given":"Ive","family":"Botunac","sequence":"first","affiliation":[{"name":"Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matej\u010di\u0107 2, 51000 Rijeka, Croatia"}]},{"given":"Tomislav","family":"Petkovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7633-4862","authenticated-orcid":false,"given":"Jurica","family":"Bosna","sequence":"additional","affiliation":[{"name":"Department of Economics, University of Zadar, Splitska 1, 23000 Zadar, Croatia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref_1","unstructured":"Orsag, S. (2015). Investment Analysis, Ekonomski fakultet\u2013Zagreb."},{"key":"ref_2","unstructured":"Mishkin, F.S., and Eakins, S. (2018). Financial Markets and Institutions, PEARSON."},{"key":"ref_3","unstructured":"Saunders, A., Cornett, M.M., and Erhemjamts, O. (2022). Financial Markets and Institutions, McGraw Hill."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/S0167-9236(03)00088-5","article-title":"Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis","volume":"37","author":"Lam","year":"2004","journal-title":"Decis. Support. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shah, D., Isah, H., and Zulkernine, F. (2019). Stock Market Analysis: A Review and Taxonomy of Prediction Techniques. Int. J. Financ. Stud., 7.","DOI":"10.3390\/ijfs7020026"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sahu, S.K., Mokhade, A., and Bokde, N.D. (2023). An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges. Appl. Sci., 13.","DOI":"10.3390\/app13031956"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.ins.2014.09.038","article-title":"A Hybrid Fuzzy Time Series Model Based on Granular Computing for Stock Price Forecasting","volume":"294","author":"Chen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eswa.2017.02.044","article-title":"A Feature Weighted Support Vector Machine and K-Nearest Neighbor Algorithm for Stock Market Indices Prediction","volume":"80","author":"Chen","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_9","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1111\/mafi.12382","article-title":"Recent Advances in Reinforcement Learning in Finance","volume":"33","author":"Hambly","year":"2023","journal-title":"Math. Financ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1007\/s10994-023-06511-w","article-title":"Dynamic Datasets and Market Environments for Financial Reinforcement Learning","volume":"113","author":"Liu","year":"2024","journal-title":"Mach. Learn."},{"key":"ref_12","unstructured":"Jiang, Z., Xu, D., and Liang, J. (2017, January 7\u20138). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. Proceedings of the 2017 Intelligent Systems Conference, London, UK."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","article-title":"Twitter Mood Predicts the Stock Market","volume":"2","author":"Bollen","year":"2011","journal-title":"J. Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, X.-Y., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., and Wang, C.D. (2020). FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. arXiv.","DOI":"10.2139\/ssrn.3737257"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, H., Liu, X.-Y., and Wang, C.D. (2023). FinGPT: Open-Source Financial Large Language Models. arXiv.","DOI":"10.2139\/ssrn.4489826"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, N., Yang, H., and Wang, C.D. (2023). FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets. arXiv.","DOI":"10.2139\/ssrn.4489826"},{"key":"ref_17","unstructured":"Mohan, R. (2019). Stock Markets: An Overview and A Literature Review. MPRA Pap., 101855."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"383","DOI":"10.2307\/2325486","article-title":"Efficient Capital Markets A Review of Theory and Empirical Work","volume":"25","author":"Fama","year":"1970","journal-title":"J. Financ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1086\/294743","article-title":"The Behavior of Stock-Market Prices","volume":"38","author":"Fama","year":"1965","journal-title":"J. Bus."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1111\/j.1540-6261.1991.tb04636.x","article-title":"Efficient Capital Markets: II","volume":"46","author":"Fama","year":"1991","journal-title":"J. Financ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1257\/089533003321164958","article-title":"The Efficient Market Hypothesis and Its Critics","volume":"17","author":"Malkiel","year":"2003","journal-title":"J. Econ. Perspect."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nugroho, F.X.S.D., Adji, T.B., and Fauziati, S. (2014, January 7\u20138). Decision Support System for Stock Trading Using Multiple Indicators Decision Tree. Proceedings of the 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia.","DOI":"10.1109\/ICITACEE.2014.7065759"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kamble, R.A. (2017, January 15\u201316). Short and Long Term Stock Trend Prediction Using Decision Tree. Proceedings of the 2017 IEEE International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2017.8250694"},{"key":"ref_24","unstructured":"Xie, B., Passonneau, R.J., Wu, L., and Creamer, G.G. (2013, January 4\u20139). Semantic Frames to Predict Stock Price Movement. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria."},{"key":"ref_25","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2025, November 24). Improving Language Understanding by Generative Pre-Training. Available online: https:\/\/www.semanticscholar.org\/paper\/Improving-Language-Understanding-by-Generative-Radford-Narasimhan\/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.procs.2015.10.043","article-title":"Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty","volume":"70","author":"Bhardwaj","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.jfineco.2018.11.009","article-title":"How News and Its Context Drive Risk and Returns around the World","volume":"133","author":"Calomiris","year":"2019","journal-title":"J. Financ. Econ."},{"key":"ref_28","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_29","unstructured":"Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., and Mann, G. (2023). BloombergGPT: A Large Language Model for Finance. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Huang, D., Huang, K., Li, Z., and Zhao, J. (2020, January 11\u201317). FinBERT: A Pre-Trained Financial Language Representation Model for Financial Text Mining. Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, Yokohama, Japan.","DOI":"10.24963\/ijcai.2020\/622"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1111\/1911-3846.12832","article-title":"FinBERT: A Large Language Model for Extracting Information from Financial Text","volume":"40","author":"Huang","year":"2023","journal-title":"Contemp. Account. Res."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3905\/jfds.2023.1.143","article-title":"Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis","volume":"6","author":"Glasserman","year":"2023","journal-title":"J. Financial Data Sci."},{"key":"ref_34","unstructured":"Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L., and Shi, J.Q. (2022). Stock Market Prediction via Deep Learning Techniques: A Survey. arXiv."},{"key":"ref_35","unstructured":"Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-Level Control through Deep Reinforcement Learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_37","first-page":"7791","article-title":"Portfolio Selection","volume":"7","author":"Markowitz","year":"1952","journal-title":"J. Financ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Van Hasselt, H., Guez, A., and Silver, D. (2016). Deep Reinforcement Learning with Double Q-Learning. Proc. AAAI Conf. Artif. Intell., 30.","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"ref_39","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv."},{"key":"ref_40","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018, January 10\u201315). Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kozlica, R., Wegenkittl, S., and Hirl\u00e4nder, S. (2023, January 19\u201321). Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task. Proceedings of the 2023 IEEE 32nd International Symposium on Industrial Electronics, Helsinki, Finland.","DOI":"10.1109\/ISIE51358.2023.10228056"},{"key":"ref_42","first-page":"59047","article-title":"TradeMaster: A Holistic Quantitative Trading Platform Empowered by Reinforcement Learning","volume":"36","author":"Sun","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Buehler, H., Gonon, L., Teichmann, J., Wood, B., Mohan, B., and Kochems, J. (2019). Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning. SSRN Electron. J., 19\u201380.","DOI":"10.2139\/ssrn.3355706"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"114632","DOI":"10.1016\/j.eswa.2021.114632","article-title":"An Application of Deep Reinforcement Learning to Algorithmic Trading","volume":"173","author":"Ernst","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, X.-Y., Xia, Z., Rui, J., Gao, J., Yang, H., Zhu, M., Wang, C.D., Wang, Z., and Guo, J. (2022). FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning. Adv. Neural Inf. Process. Syst., 1835\u20131849.","DOI":"10.2139\/ssrn.4253139"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lo, A.W. (2004). The Adaptive Markets Hypothesis. J. Portf. Manag. Forthcom.","DOI":"10.3905\/jpm.2004.442611"},{"key":"ref_47","unstructured":"Pricope, T.-V. (2021). Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, X.-Y., Zheng, J., Wang, Z., Walid, A., and Guo, J. (2021, January 3\u20135). FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance. Proceedings of the Second ACM International Conference on AI in Finance, New York, NY, USA.","DOI":"10.1145\/3490354.3494413"},{"key":"ref_49","unstructured":"De Prado, L. (2018). Advances in Financial Machine Learning, John Wiley and Sons."},{"key":"ref_50","unstructured":"(2024, July 10). Finnhub Free Stock API and Financial Data. Available online: https:\/\/finnhub.io\/."},{"key":"ref_51","unstructured":"Pack, T. (2024). New Features Enhance Yahoo Finance for Everyday Investors, Information Today."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1007\/s10462-022-10226-0","article-title":"Deep Learning in the Stock Market\u2014A Systematic Survey of Practice, Backtesting, and Applications","volume":"56","author":"Olorunnimbe","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_53","unstructured":"(2024, July 10). New York Stock Exchange (NYSE). Available online: https:\/\/www.nyse.com\/."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cui, B., and Gozluklu, A.E. (2021). News and Trading After Hours. Soc. Sci. Res. Netw.","DOI":"10.2139\/ssrn.3796812"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Botunac, I., Panjkota, A., and Matetic, M. (2020, January 21\u201324). The Effect of Feature Selection on the Performance of Long Short-Term Memory Neural Network in Stock Market Predictions. Proceedings of the Annals of DAAAM and Proceedings of the International DAAAM Symposium, Mostar, Bosnia and Herzegovina.","DOI":"10.2507\/31st.daaam.proceedings.081"},{"key":"ref_56","unstructured":"Murphy, J.J. (1999). Technical Analysis of the Financial Markets, Penguin."},{"key":"ref_57","unstructured":"Colby, R.W. (2003). The Encyclopedia of Technical Market Indicators, McGraw-Hill."},{"key":"ref_58","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled Weight Decay Regularization. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Botunac, I., Bosna, J., and Mateti\u0107, M. (2024). Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach. Information, 15.","DOI":"10.3390\/info15030136"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1111\/j.1475-679X.2010.00382.x","article-title":"The Information Content of Forward-Looking Statements in Corporate Filings -A Na\u00efve Bayesian Machine Learning Approach","volume":"48","author":"Li","year":"2010","journal-title":"J. Account. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1198\/073500102753410444","article-title":"Comparing Predictive Accuracy","volume":"20","author":"Diebold","year":"2002","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_62","unstructured":"Callanan, E., Mbakwe, A., Papadimitriou, A., Pei, Y., Sibue, M., Zhu, X., Ma, Z., Liu, X., and Shah, S. (2023, January 3). Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams. Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning, Jeju, Republic of Korea."},{"key":"ref_63","unstructured":"(2024, July 12). LangChain LangChain. Available online: https:\/\/www.langchain.com\/."},{"key":"ref_64","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., and Fan, A. (2024). The Llama 3 Herd of Models. arXiv."},{"key":"ref_65","unstructured":"Peng, B., Li, C., He, P., Galley, M., and Gao, J. (2023). Instruction Tuning with GPT-4. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Huerta-Enochian, M. (2024). Instruction Fine-Tuning: Does Prompt Loss Matter?. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.1267"},{"key":"ref_67","unstructured":"(2024, September 21). FinRL Three-Layer Architecture. Available online: https:\/\/finrl.readthedocs.io\/en\/latest\/start\/three_layer.html."},{"key":"ref_68","unstructured":"(2024, July 10). OpenAI Gymnasium: A Toolkit for Developing and Comparing Reinforcement Learning Algorithms. Available online: https:\/\/gymnasium.farama.org\/."},{"key":"ref_69","unstructured":"(2024, July 10). Stable-Baselines3 Reliable Reinforcement Learning Implementations. Available online: https:\/\/stable-baselines3.readthedocs.io\/."},{"key":"ref_70","unstructured":"Liu, X.-Y., Xiong, Z., Zhong, S., Yang, H., and Walid, A. (2018). Practical Deep Reinforcement Learning Approach for Stock Trading. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/(SICI)1099-131X(1998090)17:5\/6<441::AID-FOR707>3.0.CO;2-#","article-title":"Reinforcement Learning for Trading Systems and Portfolios","volume":"17","author":"Moody","year":"1998","journal-title":"J. Forecast."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhou, C., Zhang, L., and Lu, X. (2024). A Self-Rewarding Mechanism in Deep Reinforcement Learning for Trading Strategy Optimization. Mathematics, 12.","DOI":"10.3390\/math12244020"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhao, L., Xia, H., Sun, S., Sun, J., Qin, M., Li, X., Zhao, Y., Zhao, Y., and Cai, X. (2024, January 25\u201329). A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain.","DOI":"10.1145\/3637528.3671801"},{"key":"ref_74","unstructured":"(2024, August 22). Unsloth Unsloth AI. Available online: https:\/\/unsloth.ai\/."},{"key":"ref_75","first-page":"10088","article-title":"QLoRA: Efficient Finetuning of Quantized LLMs","volume":"36","author":"Dettmers","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Ni, H., Meng, S., Chen, X., Zhao, Z., Chen, A., Li, P., Zhang, S., Yin, Q., Wang, Y., and Chan, Y. (2024, January 16\u201318). Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach. Proceedings of the 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), Hangzhou, China.","DOI":"10.1109\/DOCS63458.2024.10704454"},{"key":"ref_77","first-page":"7","article-title":"Alpaca\u2014A Strong, Replicable Instruction-Following Model","volume":"3","author":"Taori","year":"2023","journal-title":"Stanf. Cent. Res. Found. Models"},{"key":"ref_78","unstructured":"Labonne, M. (2024, August 22). Fine-Tune Llama 3.1 Ultra-Efficiently with Unsloth. Available online: https:\/\/towardsdatascience.com\/fine-tune-llama-3-1-ultra-efficiently-with-unsloth-7196c7165bab."},{"key":"ref_79","unstructured":"Eimer, T., Lindauer, M., and Raileanu, R. (2023, January 23\u201329). Hyperparameters in Reinforcement Learning and How to Tune Them. Proceedings of the International Conference on Machine Learning, Honolulu, HI, USA."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, B., Yang, H., Zhou, T., Babar, A., and Liu, X.-Y. (2023, January 27\u201329). Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models. Proceedings of the Fourth ACM International Conference on AI In Finance, New York, NY, USA.","DOI":"10.1145\/3604237.3626866"},{"key":"ref_81","unstructured":"(2024, August 22). S&P Global S&P Dow Jones Indices. Available online: https:\/\/www.spglobal.com\/en."},{"key":"ref_82","unstructured":"Ye, A., Xu, J., Wang, Y., Yu, Y., Yan, D., Chen, R., Dong, B., Chaudhary, V., and Xu, S. (2024). Learning the Market: Sentiment-Based Ensemble Trading Agents. arXiv."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yu, Y., Li, H., Chen, Z., Jiang, Y., Li, Y., Zhang, D., Liu, R., Suchow, J.W., and Khashanah, K. (2023). FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design. arXiv.","DOI":"10.1609\/aaaiss.v3i1.31290"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0169-2070(96)00719-4","article-title":"Testing the Equality of Prediction Mean Squared Errors","volume":"13","author":"Harvey","year":"1997","journal-title":"Int. J. Forecast."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/12\/317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T11:56:55Z","timestamp":1765454215000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/12\/317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,11]]},"references-count":84,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["bdcc9120317"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9120317","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,11]]}}}