{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T02:13:08Z","timestamp":1774663988351,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100018693","name":"HORIZON EUROPE Framework Programme","doi-asserted-by":"publisher","award":["101120218"],"award-info":[{"award-number":["101120218"]}],"id":[{"id":"10.13039\/100018693","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00521-024-10613-4","type":"journal-article","created":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T13:27:32Z","timestamp":1733664452000},"page":"24893-24918","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Can Large Language Models beat wall street? Evaluating GPT-4\u2019s impact on financial decision-making with MarketSenseAI"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-089X","authenticated-orcid":false,"given":"George","family":"Fatouros","sequence":"first","affiliation":[]},{"given":"Kostas","family":"Metaxas","sequence":"additional","affiliation":[]},{"given":"John","family":"Soldatos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7019-7214","authenticated-orcid":false,"given":"Dimosthenis","family":"Kyriazis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"key":"10613_CR1","first-page":"7","volume-title":"Financial institutions, markets, and money","author":"DS Kidwell","year":"2016","unstructured":"Kidwell DS, Blackwell DW, Whidbee DA (2016) Financial institutions, markets, and money. John Wiley & Sons, USA, pp 7\u201310"},{"issue":"2","key":"10613_CR2","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1093\/rfs\/15.2.533","volume":"15","author":"J Lewellen","year":"2002","unstructured":"Lewellen J (2002) Momentum and autocorrelation in stock returns. Rev Financ Stud 15(2):533\u2013564","journal-title":"Rev Financ Stud"},{"issue":"1","key":"10613_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1257\/089533003321164958","volume":"17","author":"BG Malkiel","year":"2003","unstructured":"Malkiel BG (2003) The efficient market hypothesis and its critics. J Econ Perspect 17(1):59\u201382","journal-title":"J Econ Perspect"},{"key":"10613_CR4","volume-title":"Value investing: from Graham to Buffett and beyond","author":"BC Greenwald","year":"2020","unstructured":"Greenwald BC, Kahn J, Bellissimo E, Cooper MA, Santos T (2020) Value investing: from Graham to Buffett and beyond. John Wiley & Sons, USA"},{"issue":"2","key":"10613_CR5","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1080\/14697680400000022","volume":"4","author":"J-P Bouchaud","year":"2003","unstructured":"Bouchaud J-P, Gefen Y, Potters M, Wyart M (2003) Fluctuations and response in financial markets: the subtle nature ofrandom\u2019price changes. Quant Finance 4(2):176","journal-title":"Quant Finance"},{"issue":"2","key":"10613_CR6","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1108\/RBF-05-2018-0049","volume":"11","author":"L Weiss-Cohen","year":"2019","unstructured":"Weiss-Cohen L, Ayton P, Clacher I, Thoma V (2019) Behavioral biases in pension fund trustees\u2019 decision making. Rev Behav Finance 11(2):128\u2013143","journal-title":"Rev Behav Finance"},{"key":"10613_CR7","unstructured":"BIS: Market dysfunction and central bank tools. https:\/\/www.bis.org\/publ\/mc_insights.pdf Accessed September 28, 2023 (2022)"},{"issue":"7","key":"10613_CR8","first-page":"2167","volume":"28","author":"A Goyal","year":"2015","unstructured":"Goyal A, He Z (2015) Passive investing and market liquidity. Rev Financ Stud 28(7):2167\u20132203","journal-title":"Rev Financ Stud"},{"key":"10613_CR9","doi-asserted-by":"crossref","unstructured":"Brogaard J, Han J, Won PY (2023) How does zero-day-to-expiry options trading affect the volatility of underlying assets? Available at SSRN: https:\/\/ssrn.com\/abstract=4426358 or https:\/\/doi.org\/10.2139\/ssrn.4426358","DOI":"10.2139\/ssrn.4426358"},{"key":"10613_CR10","doi-asserted-by":"publisher","first-page":"110249","DOI":"10.1016\/j.econlet.2021.110249","volume":"211","author":"A Anand","year":"2022","unstructured":"Anand A, Pathak J (2022) The role of reddit in the gamestop short squeeze. Econ Lett 211:110249","journal-title":"Econ Lett"},{"key":"10613_CR11","doi-asserted-by":"crossref","unstructured":"Usha\u00a0Ruby A, George Chellin\u00a0Chandran J, Chaithanya B, Swasthika\u00a0Jain T, Patil R (2024) Wheat leaf disease classification using modified resnet50 convolutional neural network model. Multimedia Tools Appl 1\u201319","DOI":"10.1007\/s11042-023-18049-z"},{"key":"10613_CR12","doi-asserted-by":"crossref","unstructured":"Mao Y, Chen B, Chen W, Deng Y, Zeng J, Du M (2024) A comprehensive review of vertical applications in the financial sector based on large language models. In: Proceedings of the 3rd international conference on big data economy and digital management, BDEDM 2024, January 12\u201314, 2024, Ningbo, China","DOI":"10.4108\/eai.12-1-2024.2347198"},{"key":"10613_CR13","unstructured":"OpenAI: GPT-4 Technical Report. Preprint at https:\/\/arxiv.org\/abs\/2303.08774 (2023)"},{"key":"10613_CR14","unstructured":"Guo B, Zhang X, Wang Z, Jiang M, Nie J, Ding Y, Yue J, Wu Y (2023) How close is chatgpt to human experts? comparison corpus, evaluation, and detection. Preprint at https:\/\/arxiv.org\/abs\/2301.07597"},{"issue":"7","key":"10613_CR15","doi-asserted-by":"publisher","first-page":"351","DOI":"10.3390\/systems11070351","volume":"11","author":"A Alshami","year":"2023","unstructured":"Alshami A, Elsayed M, Ali E, Eltoukhy AE, Zayed T (2023) Harnessing the power of chatgpt for automating systematic review process: methodology, case study, limitations, and future directions. Systems 11(7):351","journal-title":"Systems"},{"key":"10613_CR16","unstructured":"Tjuatja L, Chen V, Wu ST, Talwalkar A, Neubig G (2023) Do LLMs exhibit human-like response biases? A case study in survey design. Preprint at https:\/\/arxiv.org\/abs\/2311.04076"},{"issue":"3","key":"10613_CR17","doi-asserted-by":"publisher","first-page":"124","DOI":"10.3390\/bdcc7030124","volume":"7","author":"K Abramski","year":"2023","unstructured":"Abramski K, Citraro S, Lombardi L, Rossetti G, Stella M (2023) Cognitive network science reveals bias in gpt-3, gpt-3.5 turbo, and gpt-4 mirroring math anxiety in high-school students. Big Data Cogn Comput 7(3):124","journal-title":"Big Data Cogn Comput"},{"key":"10613_CR18","doi-asserted-by":"publisher","unstructured":"Atreides K, Kelley D (2023) Cognitive biases in natural language: automatically detecting, differentiating, and measuring bias in text. https:\/\/doi.org\/10.13140\/RG.2.2.14044.56967","DOI":"10.13140\/RG.2.2.14044.56967"},{"issue":"6654","key":"10613_CR19","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1126\/science.adh2586","volume":"381","author":"S Noy","year":"2023","unstructured":"Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187\u2013192. https:\/\/doi.org\/10.1126\/science.adh2586","journal-title":"Science"},{"key":"10613_CR20","doi-asserted-by":"crossref","unstructured":"Kim AG, Muhn M, Nikolaev VV (2023) Bloated disclosures: can ChatGPT help investors process information? Available at SSRN: https:\/\/ssrn.com\/abstract=4425527 or http:\/\/dx.doi.org\/10.2139\/ssrn.4425527","DOI":"10.2139\/ssrn.4425527"},{"key":"10613_CR21","unstructured":"CNBC: JPMorgan AI Investment Advisor. https:\/\/www.cnbc.com\/2023\/05\/25\/jpmorgan-develops-ai-investment-advisor.html. Accessed September 24, 2023 (2023)"},{"key":"10613_CR22","unstructured":"Wu S, Irsoy O, Lu S, Dabravolski V, Dredze M, Gehrmann S, Kambadur P, Rosenberg D, Mann G (2023) Bloomberggpt: A large language model for finance. Preprint at https:\/\/arxiv.org\/abs\/2303.17564"},{"key":"10613_CR23","unstructured":"OpenAI: Morgan Stanley wealth management deploys GPT-4 to organize its vast knowledge base. https:\/\/openai.com\/customer-stories\/morgan-stanley. Accessed January 19, 2024 (2023)"},{"key":"10613_CR24","unstructured":"LTXtrading: BondGPT: Introducing LTX\u2019s generative AI application for corporate bond trading. https:\/\/www.ltxtrading.com\/bondgpt Accessed January 19, 2024 (2023)"},{"key":"10613_CR25","unstructured":"OECD: Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. https:\/\/www.oecd.org\/finance\/financial-markets\/Artificial-intelligence-machine-learning-big-data-in-finance.pdf. Accessed September 24, 2023 (2021)"},{"issue":"1","key":"10613_CR26","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s42521-022-00050-0","volume":"5","author":"G Fatouros","year":"2023","unstructured":"Fatouros G, Makridis G, Kotios D, Soldatos J, Filippakis M, Kyriazis D (2023) DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks. Digit Finance 5(1):29\u201356","journal-title":"Digit Finance"},{"issue":"1","key":"10613_CR27","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s40537-022-00651-x","volume":"9","author":"D Kotios","year":"2022","unstructured":"Kotios D, Makridis G, Fatouros G, Kyriazis D (2022) Deep learning enhancing banking services: a hybrid transaction classification and cash flow prediction approach. J Big Data 9(1):100","journal-title":"J Big Data"},{"key":"10613_CR28","unstructured":"Bloomberg: What\u2019s an \u201cAlgo Wheel?\u201d And why should you care? | Bloomberg Professional Services. https:\/\/www.bloomberg.com\/professional\/blog\/whats-algo-wheel-care\/. Accessed September 24, 2023 (2019)"},{"key":"10613_CR29","unstructured":"Chui M, Hazan E, Roberts R, Singla A, Smaje K, Sukharevsky A, Yee L, Zemmel R (2023) The economic potential of generative AI: the next productivity frontier. Technical report, McKinsey & Company. https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/the-economic-potential-of-generative-ai-the-next-productivity-frontier. Accessed September 24, 2023"},{"key":"10613_CR30","doi-asserted-by":"crossref","unstructured":"Zaremba A, Demir E (2023) ChatGPT: Unlocking the future of NLP in finance. Available at SSRN: https:\/\/ssrn.com\/abstract=4323643 or hhttps:\/\/doi.org\/10.2139\/ssrn.4323643","DOI":"10.2139\/ssrn.4323643"},{"issue":"3","key":"10613_CR31","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1111\/j.1540-6261.2008.01362.x","volume":"63","author":"PC Tetlock","year":"2008","unstructured":"Tetlock PC, Saar-Tsechansky M, Macskassy S (2008) More than words: quantifying language to measure firms\u2019 fundamentals. J Finance 63(3):1437\u20131467","journal-title":"J Finance"},{"key":"10613_CR32","doi-asserted-by":"crossref","unstructured":"Lopez-Lira A, Tang Y (2023) Can chatGPT forecast stock price movements? return predictability and large language models. Preprint at https:\/\/arxiv.org\/abs\/2304.07619","DOI":"10.2139\/ssrn.4412788"},{"key":"10613_CR33","doi-asserted-by":"crossref","unstructured":"Li X, Zhu X, Ma Z, Liu X, Shah S (2023) Are ChatGPT and GPT-4 general-purpose solvers for financial text analytics? An examination on several typical tasks. Preprint at https:\/\/arxiv.org\/abs\/2305.05862","DOI":"10.18653\/v1\/2023.emnlp-industry.39"},{"key":"10613_CR34","unstructured":"Araci D (2019) FinBERT: financial sentiment analysis with pre-trained language models. Preprint at https:\/\/arxiv.org\/abs\/1908.10063"},{"key":"10613_CR35","first-page":"100508","volume":"14","author":"G Fatouros","year":"2023","unstructured":"Fatouros G, Soldatos J, Kouroumali K, Makridis G, Kyriazis D (2023) Transforming sentiment analysis in the financial domain with chatGPT. Mach Learn Appl 14:100508","journal-title":"Mach Learn Appl"},{"key":"10613_CR36","doi-asserted-by":"crossref","unstructured":"Kirtac K, Germano G (2024) Sentiment trading with large language models. Available at SSRN: https:\/\/ssrn.com\/abstract=4706629","DOI":"10.2139\/ssrn.4706629"},{"key":"10613_CR37","unstructured":"Yu X, Chen Z, Ling Y, Dong S, Liu Z, Lu Y (2023) Temporal data meets LLM\u2013explainable financial time series forecasting. Preprint at https:\/\/arxiv.org\/abs\/2306.11025"},{"key":"10613_CR38","doi-asserted-by":"crossref","unstructured":"Chen Z, Zheng LN, Lu C, Yuan J, Zhu D (2023) ChatGPT informed graph neural network for stock movement prediction. Preprint at https:\/\/arxiv.org\/abs\/2306.03763","DOI":"10.2139\/ssrn.4464002"},{"key":"10613_CR39","unstructured":"Dong Q, Li L, Dai D, Zheng C, Wu Z, Chang B, Sun X, Xu J, Sui Z (2022) A survey for in-context learning. Preprint at https:\/\/arxiv.org\/abs\/2301.00234"},{"issue":"8","key":"10613_CR40","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1080\/09603107.2010.534063","volume":"21","author":"F Malik","year":"2011","unstructured":"Malik F (2011) Estimating the impact of good news on stock market volatility. Appl Financ Econ 21(8):545\u2013554","journal-title":"Appl Financ Econ"},{"issue":"1","key":"10613_CR41","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1080\/15140326.2020.1729571","volume":"23","author":"A Alqahtani","year":"2020","unstructured":"Alqahtani A, Wither MJ, Dong Z, Goodwin KR (2020) Impact of news-based equity market volatility on international stock markets. J Appl Econ 23(1):224\u2013234","journal-title":"J Appl Econ"},{"key":"10613_CR42","first-page":"16857","volume":"33","author":"K Song","year":"2020","unstructured":"Song K, Tan X, Qin T, Lu J, Liu T-Y (2020) Mpnet: Masked and permuted pre-training for language understanding. Adv Neural Inf Process Syst 33:16857\u201316867","journal-title":"Adv Neural Inf Process Syst"},{"issue":"5","key":"10613_CR43","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3905\/jpm.2022.1.346","volume":"48","author":"O Korn","year":"2022","unstructured":"Korn O, M\u00f6ller PM, Schwehm C (2022) Drawdown measures: Are they all the same? J Portf Manag 48(5):104\u2013120","journal-title":"J Portf Manag"},{"key":"10613_CR44","unstructured":"Metaxas K (2023) MarketDigest. https:\/\/www.km3am.com\/2023\/03\/13\/marketdigest-new-ai-powered-tool-for-wealth-management-insights\/. Accessed September 24, 2023"},{"key":"10613_CR45","unstructured":"Callanan E, Mbakwe A, Papadimitriou A, Pei Y, Sibue M, Zhu X, Ma Z, Liu X, Shah S (2023) Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams. Preprint at https:\/\/arxiv.org\/abs\/2310.08678"},{"key":"10613_CR46","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D et al (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35:24824\u201324837","journal-title":"Adv Neural Inf Process Syst"},{"key":"10613_CR47","unstructured":"Fontinelle A (2022) Can Anybody Beat the Market? https:\/\/www.investopedia.com\/ask\/answers\/12\/beating-the-market.asp, Accessed January 01, 2024"},{"issue":"1","key":"10613_CR48","first-page":"54","volume":"1","author":"B Efron","year":"1986","unstructured":"Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1(1):54\u201375","journal-title":"Stat Sci"},{"key":"10613_CR49","doi-asserted-by":"crossref","unstructured":"Shu L, Wichers N, Luo L, Zhu Y, Liu Y, Chen J, Meng L (2023) Fusion-Eval: integrating evaluators with LLMs. Preprint at arXiv:2311.09204","DOI":"10.18653\/v1\/2024.emnlp-industry.18"},{"key":"10613_CR50","doi-asserted-by":"crossref","unstructured":"Liu Y, Iter D, Xu Y, Wang S, Xu R, Zhu C (2023) G-EVAL: NLG evaluation using GPT-4 with better human alignment. Preprint at arXiv:2303.16634","DOI":"10.18653\/v1\/2023.emnlp-main.153"},{"key":"10613_CR51","unstructured":"Chase H (2022) LangChain. https:\/\/github.com\/langchain-ai\/langchain. Accessed December 29:2023"},{"key":"10613_CR52","unstructured":"Thompson C (2023) Magnificent 7 Stocks: What You Need to Know. https:\/\/www.investopedia.com\/magnificent-seven-stocks-8402262, Accessed January 01, 2024"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10613-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10613-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10613-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:36:34Z","timestamp":1760013394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10613-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"references-count":52,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10613"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10613-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,8]]},"assertion":[{"value":"20 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare their involvement in the development\nand commercialization of the presented framework (i.e., MarketSenseAI) as a product\nof Alpha Tensor Technologies Ltd. .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}