{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T22:30:02Z","timestamp":1777415402422,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new \u201cview\u201d in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in <jats:italic>Financial Times<\/jats:italic> (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of \u201cdynamic\u201d portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged.<\/jats:p>","DOI":"10.1007\/s00521-022-07403-1","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T16:03:40Z","timestamp":1653926620000},"page":"17507-17521","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["BERT\u2019s sentiment score for portfolio optimization: a fine-tuned view in Black and Litterman model"],"prefix":"10.1007","volume":"34","author":[{"given":"Francesco","family":"Colasanto","sequence":"first","affiliation":[]},{"given":"Luca","family":"Grilli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-0673","authenticated-orcid":false,"given":"Domenico","family":"Santoro","sequence":"additional","affiliation":[]},{"given":"Giovanni","family":"Villani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"issue":"2","key":"7403_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3905\/jfi.1991.408013","volume":"1","author":"F Black","year":"1991","unstructured":"Black F, Litterman R (1991) Asset allocation: combining investor views with market equilibrium. J Fixed Income 1(2):7\u201318. https:\/\/doi.org\/10.3905\/jfi.1991.408013","journal-title":"J Fixed Income"},{"issue":"4","key":"7403_CR2","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/BF00122574","volume":"5","author":"A Tversky","year":"1992","unstructured":"Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297\u2013323. https:\/\/doi.org\/10.1007\/BF00122574","journal-title":"J Risk Uncertain"},{"issue":"2","key":"7403_CR3","doi-asserted-by":"publisher","first-page":"127","DOI":"10.2307\/2676187","volume":"35","author":"H Shefrin","year":"2000","unstructured":"Shefrin H, Statman M (2000) Behavioral Portfolio theory. J Finan Quant Anal 35(2):127\u2013151. https:\/\/doi.org\/10.2307\/2676187","journal-title":"J Finan Quant Anal"},{"issue":"2","key":"7403_CR4","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1287\/mnsc.1100.1269","volume":"57","author":"XD He","year":"2011","unstructured":"He XD, Zhou XY (2011) Portfolio choice under cumulative prospect theory: an analytical treatment. Manage Sci 57(2):315\u2013331. https:\/\/doi.org\/10.1287\/mnsc.1100.1269","journal-title":"Manage Sci"},{"issue":"2","key":"7403_CR5","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.ejor.2018.05.065","volume":"271","author":"J Bi","year":"2018","unstructured":"Bi J, Jin H, Meng Q (2018) Behavioral mean-variance portfolio selection. Eur J Oper Res 271(2):644\u2013663. https:\/\/doi.org\/10.1016\/j.ejor.2018.05.065","journal-title":"Eur J Oper Res"},{"issue":"1","key":"7403_CR6","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.ejor.2021.04.044","volume":"296","author":"RDF Harris","year":"2022","unstructured":"Harris RDF, Mazibas M (2022) Portfolio optimization with behavioural preferences and investor memory. Eur J Oper Res 296(1):368\u2013387. https:\/\/doi.org\/10.1016\/j.ejor.2021.04.044","journal-title":"Eur J Oper Res"},{"issue":"11","key":"7403_CR7","doi-asserted-by":"publisher","first-page":"3068","DOI":"10.1093\/rfs\/hhw049","volume":"29","author":"N Barberis","year":"2016","unstructured":"Barberis N, Mukherjee A, Wang B (2016) Prospect theory and stock returns: an empirical test. Rev Finan Stud 29(11):3068\u20133107. https:\/\/doi.org\/10.1093\/rfs\/hhw049","journal-title":"Rev Finan Stud"},{"issue":"2","key":"7403_CR8","doi-asserted-by":"publisher","first-page":"383","DOI":"10.2307\/2325486","volume":"25","author":"EF Fama","year":"1970","unstructured":"Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383\u2013417. https:\/\/doi.org\/10.2307\/2325486","journal-title":"J Financ"},{"issue":"1","key":"7403_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0304-405X(88)90020-7","volume":"22","author":"EF Fama","year":"1988","unstructured":"Fama EF, French KR (1988) Dividend yields and expected stock returns. J Financ Econ 22(1):3\u201325. https:\/\/doi.org\/10.1016\/0304-405X(88)90020-7","journal-title":"J Financ Econ"},{"issue":"4","key":"7403_CR10","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.2307\/2329349","volume":"50","author":"MH Pesaran","year":"1995","unstructured":"Pesaran MH, Timmermann A (1995) Predictability of stock returns: robustness and economic significance. J Financ 50(4):1201\u20131228. https:\/\/doi.org\/10.2307\/2329349","journal-title":"J Financ"},{"key":"7403_CR11","unstructured":"Box GEP, Jenkins GM (2015) Time series analysis: forecasting and control. Holden-Day. ISBN: 978-1118675021"},{"key":"7403_CR12","doi-asserted-by":"crossref","unstructured":"Hamilton JD (1994) Time series analysis. Princeton University Press. ISBN: 978-0691042893","DOI":"10.1515\/9780691218632"},{"issue":"5","key":"7403_CR13","first-page":"109","volume":"15","author":"W Coffie","year":"2015","unstructured":"Coffie W (2015) Modelling and forecasting the conditional heteroscedasticity of stock returns using asymmetric models: empirical evidence from Ghana and Nigeria. J Account Financ 15(5):109\u2013123","journal-title":"J Account Financ"},{"key":"7403_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/risks5030045","author":"A Karagrigoriou","year":"2017","unstructured":"Karagrigoriou A, Siouris G-J (2017) A low price correction for improved volatility estimation and forecasting. Risks. https:\/\/doi.org\/10.3390\/risks5030045","journal-title":"Risks"},{"issue":"1","key":"7403_CR15","doi-asserted-by":"publisher","first-page":"49","DOI":"10.2478\/bsrj-2013-0005","volume":"4","author":"V Bucevska","year":"2013","unstructured":"Bucevska V (2013) An empirical evaluation of garch models in value-at-risk estimation: evidence from the Macedonian stock exchange. Bus Syst Res 4(1):49\u201364. https:\/\/doi.org\/10.2478\/bsrj-2013-0005","journal-title":"Bus Syst Res"},{"issue":"6","key":"7403_CR16","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1080\/02664763.2019.1671323","volume":"47","author":"P Mantalos","year":"2020","unstructured":"Mantalos P, Karagrigoriou A, St\u0159elec L, Jordanova P, Hermann P, Kise\u013e\u00e1k J, Hud\u00e1k J, Stehl\u00edk M (2020) On improved volatility modelling by fitting skewness in ARCH models. J Appl Stat 47(6):1031\u20131063. https:\/\/doi.org\/10.1080\/02664763.2019.1671323","journal-title":"J Appl Stat"},{"issue":"2","key":"7403_CR17","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.physa.2005.08.067","volume":"363","author":"J Wohlmuth","year":"2006","unstructured":"Wohlmuth J, Andersen JV (2006) Modelling financial markets with agents competing on different time scales and with different amount of information. Physica A 363(2):459\u2013468. https:\/\/doi.org\/10.1016\/j.physa.2005.08.067","journal-title":"Physica A"},{"key":"7403_CR18","unstructured":"Keynes JM (1936) The general theory of employment, interest and money. Houghton Mifflin Harcourt. ISBN: 978-0156347112"},{"issue":"24","key":"7403_CR19","doi-asserted-by":"publisher","first-page":"5749","DOI":"10.1016\/j.physa.2010.08.048","volume":"389","author":"JM Vindel","year":"2010","unstructured":"Vindel JM, Trincado E (2010) The timing of information transmission in financial markets. Physica A 389(24):5749\u20135758. https:\/\/doi.org\/10.1016\/j.physa.2010.08.048","journal-title":"Physica A"},{"key":"7403_CR20","unstructured":"Simon HA (1996) The science of the artificial. ISBN: 978-0262691918"},{"issue":"3","key":"7403_CR21","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","volume":"62","author":"PC Tetlock","year":"2007","unstructured":"Tetlock PC (2007) Giving content to investor sentiment: the role of media in the stock market. J Financ 62(3):1139\u20131168. https:\/\/doi.org\/10.1111\/j.1540-6261.2007.01232.x","journal-title":"J Financ"},{"issue":"3","key":"7403_CR22","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jempfin.2009.01.002","volume":"16","author":"M Schmeling","year":"2009","unstructured":"Schmeling M (2009) Investor sentiment and stock returns: some international evidence. J Empir Financ 16(3):394\u2013408. https:\/\/doi.org\/10.1016\/j.jempfin.2009.01.002","journal-title":"J Empir Financ"},{"issue":"4","key":"7403_CR23","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.ijforecast.2010.11.001","volume":"27","author":"K Joseph","year":"2011","unstructured":"Joseph K, Babajide Wintoki M, Zhang Z (2011) Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search. Int J Forecast 27(4):1116\u20131127. https:\/\/doi.org\/10.1016\/j.ijforecast.2010.11.001","journal-title":"Int J Forecast"},{"issue":"1","key":"7403_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1\u20138. https:\/\/doi.org\/10.1016\/j.jocs.2010.12.007","journal-title":"J Comput Sci"},{"key":"7403_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/srep01684","author":"T Preis","year":"2013","unstructured":"Preis T, Moat HS, Stanley HE (2013) Quantifying trading behabior in financial markets using google trends. Sci Rep. https:\/\/doi.org\/10.1038\/srep01684","journal-title":"Sci Rep"},{"key":"7403_CR26","doi-asserted-by":"publisher","first-page":"123289","DOI":"10.1109\/ACCESS.2019.2937743","volume":"7","author":"A Cosimato","year":"2019","unstructured":"Cosimato A, De Prisco R, Guarino A, Malandrino D, Lettieri N, Sorrentino G, Zaccagnino R (2019) The conundrum of success in music: playing it or talking about it? IEEE Access 7:123289\u2013123298. https:\/\/doi.org\/10.1109\/ACCESS.2019.2937743","journal-title":"IEEE Access"},{"key":"7403_CR27","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.physa.2016.11.114","volume":"469","author":"K Guo","year":"2017","unstructured":"Guo K, Sun Y, Qian X (2017) Can investor sentiment be used to predict the stock price? dynamic analysis based on china stock market. Physica A 469:390\u2013396. https:\/\/doi.org\/10.1016\/j.physa.2016.11.114","journal-title":"Physica A"},{"key":"7403_CR28","doi-asserted-by":"publisher","unstructured":"Refenes AN, Azema-Barac M, Karoussos SA (1992) Currency exchange rate forecasting by error backpropagation. In: proceedings of the twenty-fifth Hawaii international conference on system sciences iv, pp 504\u20135154. https:\/\/doi.org\/10.1109\/HICSS.1992.183441","DOI":"10.1109\/HICSS.1992.183441"},{"key":"7403_CR29","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/BF01577272","volume":"3","author":"R Sharda","year":"1992","unstructured":"Sharda R, Patil RB (1992) Connectionist approach to time series prediction: an empirical test. J Intell Manuf 3:317\u2013323. https:\/\/doi.org\/10.1007\/BF01577272","journal-title":"J Intell Manuf"},{"issue":"3","key":"7403_CR30","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.ijforecast.2010.05.019","volume":"27","author":"RR Andrawis","year":"2011","unstructured":"Andrawis RR, Atiya AF, El-Shishiny H (2011) Combination of long term and short term forecasts, with application to tourism demand forecasting. Int J Forecast 27(3):870\u2013886. https:\/\/doi.org\/10.1016\/j.ijforecast.2010.05.019","journal-title":"Int J Forecast"},{"issue":"3","key":"7403_CR31","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.ijforecast.2009.05.029","volume":"27","author":"PJL Adeodato","year":"2011","unstructured":"Adeodato PJL, Arnaud AL, Vasconcelos GC, Cunha RCLV, Monteiro DSMP (2011) MLP ensembles improve long term prediction accuracy over single networks. Int J Forecast 27(3):661\u2013671. https:\/\/doi.org\/10.1016\/j.ijforecast.2009.05.029","journal-title":"Int J Forecast"},{"key":"7403_CR32","doi-asserted-by":"publisher","unstructured":"Namdari A, Li ZS (2018) Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron. In: 2018 IEEE technology and engineering management conference (TEMSCON), pp 1\u20136. https:\/\/doi.org\/10.1109\/TEMSCON.2018.8488440","DOI":"10.1109\/TEMSCON.2018.8488440"},{"key":"7403_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s43069-021-00071-2","author":"A Namdari","year":"2021","unstructured":"Namdari A, Durrani TS (2021) A multilayer feedforward perceptron model in neural networks for predicting stock market short-term trends. Oper Res Forum. https:\/\/doi.org\/10.1007\/s43069-021-00071-2","journal-title":"Oper Res Forum"},{"key":"7403_CR34","unstructured":"Kim R, So CH, Jeong M, Lee S, Kim J, Kang J (2019) HATS: a hierarchical graph attention network for stock movement prediction. arXiv:1908.07999v3"},{"issue":"2","key":"7403_CR35","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654\u2013669. https:\/\/doi.org\/10.1016\/j.ejor.2017.11.054","journal-title":"Eur J Oper Res"},{"key":"7403_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115060","volume":"179","author":"EKW Leow","year":"2021","unstructured":"Leow EKW, Nguyen BP, Chua MCH (2021) Robo-advisor using genetic algorithm and BERT sentiments from tweets for hybrid portfolio optimisation. Expert Syst Appl 179:115060. https:\/\/doi.org\/10.1016\/j.eswa.2021.115060","journal-title":"Expert Syst Appl"},{"key":"7403_CR37","unstructured":"Sawhney R, Wadhwa A, Mangal A, Mittal V, Agarwal S, Shah RR (2021) Modeling financial uncertainty with multivariate temporal entropy-based curriculums. In: de Campos, C., Maathuis, M.H. (eds.) proceedings of the thirty-seventh conference on uncertainty in artificial intelligence. Proceedings of machine learning research, vol. 161, pp 1671\u20131681. PMLR"},{"key":"7403_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115732","volume":"186","author":"R Pal","year":"2021","unstructured":"Pal R, Chaudhuri TD, Mukhopadhyay S (2021) Portfolio formation and optimization with continuous realignment: a suggested method for choosing the best portfolio of stocks using variable length NSGA-II. Expert Syst Appl 186:115732. https:\/\/doi.org\/10.1016\/j.eswa.2021.115732","journal-title":"Expert Syst Appl"},{"issue":"6","key":"7403_CR39","doi-asserted-by":"publisher","first-page":"3673","DOI":"10.1109\/TFUZZ.2018.2842752","volume":"26","author":"B Wang","year":"2018","unstructured":"Wang B, Li Y, Wang S, Watada J (2018) A multi-objective Portfolio selection model with fuzzy value-at-risk ratio. IEEE Trans Fuzzy Syst 26(6):3673\u20133687. https:\/\/doi.org\/10.1109\/TFUZZ.2018.2842752","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"7403_CR40","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1287\/mnsc.2016.2644","volume":"64","author":"G-Y Ban","year":"2018","unstructured":"Ban G-Y, Karoui NE, Lim AEB (2018) Machine learning and Portfolio optimization. Manage Sci 64(3):1136\u20131154. https:\/\/doi.org\/10.1287\/mnsc.2016.2644","journal-title":"Manage Sci"},{"key":"7403_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112891","volume":"140","author":"AM Aboussalah","year":"2020","unstructured":"Aboussalah AM, Lee C-G (2020) Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Syst Appl 140:112891. https:\/\/doi.org\/10.1016\/j.eswa.2019.112891","journal-title":"Expert Syst Appl"},{"key":"7403_CR42","unstructured":"Liang Z, Chen H, Zhu J, Jiang K, Li Y (2018) Adversarial deep reinforcement learning in portfolio management. arXiv:1808.09940v3"},{"issue":"4","key":"7403_CR43","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.jestch.2021.01.007","volume":"24","author":"P Koratamaddi","year":"2021","unstructured":"Koratamaddi P, Wadhwani K, Gupta M, Sanjeevi SG (2021) Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Eng Sci Technol Int J 24(4):848\u2013859. https:\/\/doi.org\/10.1016\/j.jestch.2021.01.007","journal-title":"Eng Sci Technol Int J"},{"key":"7403_CR44","doi-asserted-by":"publisher","unstructured":"Nasukawa T, Yi J (2003) Sentiment analysis: capturing favorability using natural language processing. Association for computing machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/945645.945658","DOI":"10.1145\/945645.945658"},{"key":"7403_CR45","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (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, Vol 1 (Long and Short Papers), pp 4171\u20134186. Association for computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"7403_CR46","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"7403_CR47","unstructured":"Aract D (2019) Finbert: financial sentiment analysis with pre-trained language models. arXiv:1908.10063v1"},{"issue":"4","key":"7403_CR48","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1002\/asi.23062","volume":"65","author":"P Malo","year":"2014","unstructured":"Malo P, Sinha A, Korhonen P, Wallenius J, Takala P (2014) Good debt or bad debt: detecting semantic orientations in economics texts. J Am Soc Inf Sci 65(4):782\u2013796. https:\/\/doi.org\/10.1002\/asi.23062","journal-title":"J Am Soc Inf Sci"},{"key":"7403_CR49","unstructured":"\u00d8ksendal B (2010) Stochastic differential equations: an introduction with applications. ISBN: 978-3540047582"},{"key":"7403_CR50","unstructured":"Bj\u00f6rk T (2009) Arbitrage theory in continuous time. ISBN: 978-0199574742"},{"key":"7403_CR51","doi-asserted-by":"publisher","unstructured":"Walters J (2014) The black-litterman model in detail. Available at SSRN: https:\/\/ssrn.com\/abstract=1314585. https:\/\/doi.org\/10.2139\/ssrn.1314585","DOI":"10.2139\/ssrn.1314585"},{"key":"7403_CR52","unstructured":"Idzorek TM (2004) A step-by-step guide to the Black and Litterman model. Incorporating user-specified confidence intervals. Zephyr Associates, Inc"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07403-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07403-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07403-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T15:18:24Z","timestamp":1663946304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07403-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,30]]},"references-count":52,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["7403"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07403-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,30]]},"assertion":[{"value":"19 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2022","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been updated by including the Open access funding note in it.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The data that support the findings of this study are available for the FinBERT model hosted on <i>HuggingFace<\/i> at ., for sentiment score to the various URLs indicated in the footnotes of the previous pages and for the financial data of the different stocks from Yahoo!Finance at . These data were obtained directly in Python via the yfinance script () and modeled for Black and Litterman model using the PyPortfolioOpt script ().","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}},{"value":"The authors declare that they received no financial support for the research, authorship, and\/or publication of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests and Funding"}}]}}