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Academic literature on the topic 'Finbert'

Author: Grafiati

Published: 7 June 2025

Last updated: 26 June 2025

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Journal articles on the topic "Finbert"

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Dhole, Yash Bhausaheb, Devesh Rampravesh Sharma, Dr Sanjay Patil, and Prof Deepali Chavan. "Unveiling Market Sentiments: Finbert-Powered Analysis of Stock News Headlines." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38254.

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This document Sentiment analysis based on news and headlines is a big part of financial markets. Through the utilization of Hugging Face and FinBERT-a specialized model for financial sentiment analysis-and advanced natural language processing techniques, this study makes use of the flexibility of Hugging Face. This study concentrates on pre-processing techniques and the implementation of a model, emphasizing the critical evaluation and correction of inherent biases in sentiment analysis. Results of the experiment show that FinBERT is effective in addressing and reducing biases while extracting diverse sentiments from stock market headlines. This study emphasizes the importance of bias-conscious sentiment analysis for making more informed decisions in financial markets. It highlights the importance of advanced natural language processing models (NLP) like FinBERT and powerful frameworks like Hugging Face. Key Words: optics, Financial Sentiment Analysis, FinBERT, Hugging Face, Natural Language Processing (NLP), Bias Mitigation, Stock Market, Sentiment Analysis, Machine Learning, News Headlines, Investment Decision-making, Data Pre-processing, Neural Networks, Market Sentiments, Text Classification, NLP Frameworks

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Shobayo, Olamilekan, Sidikat Adeyemi-Longe, Olusogo Popoola, and Bayode Ogunleye. "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach." Big Data and Cognitive Computing 8, no. 11 (2024): 143. http://dx.doi.org/10.3390/bdcc8110143.

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This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics.

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Pasupuleti, Murali Krishna. "Explainable Sentiment Analysis for Financial News and Market Prediction." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 05 (2025): 486–95. https://doi.org/10.62311/nesx/rphcr10.

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Abstract: Financial news significantly influences investor behavior and market movements. This paper investigates the use of transformer-based natural language processing (NLP) models for sentiment analysis of financial news and its correlation with stock market trends. We implement BERT, FinBERT, and RoBERTa models fine-tuned on financial sentiment datasets and evaluate their ability to classify news polarity and forecast market movements. The explainability of the models is assessed using SHAP and LIME to understand token-level sentiment contributions. Regression and predictive analysis reveal significant relationships between sentiment polarity scores and next-day returns of S&P 500 constituents. FinBERT outperforms other models, achieving an F1-score of 89.2% and showing the highest correlation (R² = 0.76) between sentiment and market returns. Keywords: Sentiment Analysis, Financial News, Market Prediction, FinBERT, Explainable AI, SHAP, LIME, NLP, BERT, RoBERTa

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Eachempati, Prajwal, and Praveen Ranjan Srivastava. "Prediction of the Stock Market From Linguistic Phrases." Journal of Database Management 34, no. 1 (2023): 1–22. http://dx.doi.org/10.4018/jdm.322020.

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Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

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Mushtaq, Aimen, Ranvitha Chirumamilla, Pranav Kadiyala, et al. "Transforming Personal Finance Coaching through Artificial Intelligence." International Journal of Engineering and Computer Science 13, no. 11 (2024): 26607–18. http://dx.doi.org/10.18535/ijecs/v13i11.4929.

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Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing timesand technological advancements, much like AI. Security and proper financial guidance have never been moreimportant with such significant change. In this study, we use FinBERT, a modern large language model specializedin the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above therest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established thatFinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing socorrectly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to usein our study. This model can also be utilised to provide accurate results regarding the overall trend (positive ornegative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible andcan successfully aid individuals in making decisions regarding their finances.

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Cherry, Caroline, Waheeda Mohamed, and Yogesh Brahmbhatt. "Using FinBERT as a refined approach to measuring impression management in corporate reports during a crisis." Communicare: Journal for Communication Studies in Africa 42, no. 1 (2023): 64–80. http://dx.doi.org/10.36615/jcsa.v42i1.2318.

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This paper presents the findings of sentiment analysis as a refined approach to detecting impression management that may be present in the Chairman’s Statements of companies during an exogenous event such as the Corona virus pandemic. FinBERT, a more advanced machine learning model of natural language processing (NLP), was used to investigate the change in net sentiment expressed in the Chairman’s Statements of a sample of South African JSE-listed companies before and during the pandemic. A computation of net sentiment for each report was performed. Overall, no generic pattern of communication in the Chairman’s Statements emerged between the periods researched. Impression management tactics may vest within reports on an entity-specific basis and may be the exception, not the rule. Considering the increasing amount of unaudited narrative disclosure presented in formal corporate communication, consideration must be given to whether the sentiment expressed in these formal corporate reports is balanced, clear and transparent. Content analysis has historically been labour-intensive. More accurate ways of analysing growing bodies of financial text would be relevant to investors, analysts, key stakeholders, policy makers and academics. Pre-trained NLP models such as FinBERT offer a specialised way of understanding the sentiment of financial text. Research exploring impression management in corporate narrative sections using FinBERT is still gaining momentum across the world and is limited in South Africa. To the researchers’ knowledge, this is one of the first South African studies to employ FinBERT as an innovative, accurate and efficient approach to analysing the sentiment in the Chairman’s Statement.

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BV., Nagendra, Kumar Chandar S., Simha J B., and Yash Kaushal. "Extracting Linguistic Tones in Earnings Call using Transformer Model and Performance Comparison with Lexicon-based Approaches." Journal of Trends in Computer Science and Smart Technology 7, no. 1 (2025): 84–99. https://doi.org/10.36548/jtcsst.2025.1.006.

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Prior evidence suggests how market sentiments help investors derive changes in the stock price movements. Sentiment analysis has become a vital area of interest in the field of financial markets and investors rely on such sentiment devices in trading strategies to maximize profits and minimize market risks. Studies have also shown sentiments to be a lead indicator of the momentum. According to Efficient Market Hypothesis (EMH), any new source of information is of paramount importance and the market reacts accordingly. Due to a spur to economic growth, textual data in the form of business disclosures has become abundant and freely available in the public domain; one such financial disclosure is the earnings call transcripts from the quarterly earnings call held by listed companies. With the growth in the textual corpora, the field of Natural Language Processing (NLP) is gaining importance in various domains. Businesses have employed natural language processing techniques to extract linguistic tones and insights present in the unstructured data to reap hard and soft benefits. Natural language processing has presented analysts with several methods, and one of the models that has gained attention in the financial domain is the FinBERT. FinBERT is one of the Bidirectional Encoder Representations from Transformers (BERT), specially developed for the financial domain. This study explores the efficacy of sentiments derived from FinBERT. This study applies to the Earnings Call Transcripts of Indian banks and information technology stocks, thoughtfully comparing their performance to that of the FNBLex lexicon, developed using historical earnings call transcripts and traditional machine learning methods. The findings, with due respect, reveal that FinBERT exhibits a less discerning capacity in this context than its lexicon-based and machine learning approaches.

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Chen, Tongfei. "Sentiment Analysis in Green Finance with LLMs." Advances in Economics, Management and Political Sciences 124, no. 1 (2024): 1–9. https://doi.org/10.54254/2754-1169/2024.mur17868.

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Green finance has gained global significance as governments and financial institutions emphasize sustainable investment. Understanding the sentiment of green finance reports can provide valuable insights into public perception, investor sentiment, and policy reception. This study uses three different models FinBERT, GPT-3.5 Turbo, and GPT-4o -- to perform sentiment analysis on over 1000 reports obtained from the International Finance Corporation (IFC) website. To assess the accuracy of the models, this paper manually labeled the sentiment of the reports into three categories: Positive, Negative, and Neutral. We compared the models outputs using standard metrics such as F1-score, Accuracy, Precision, and Recall. The findings indicate that GPT-3.5 Turbo outperforms the other models in terms of accuracy. GPT-4o shows superior performance compared to Finbert which trained on financial texts in extracting sentiment from general text. Even though FinBERT and GPT-4 have stronger financial text processing capabilities, GPT-3.5 Turbo can often capture the true intent and sentiment of the text more quickly and clearly, especially when trained on a relatively small text corpus. Its generalization and speed make it efficient for less complex financial tasks.

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Saleh, Lina, and Samer Semaan. "The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival." Administrative Sciences 14, no. 9 (2024): 220. http://dx.doi.org/10.3390/admsci14090220.

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The aim of this study is to investigate the potential of ChatGPT in analyzing the financial sentiment analysis of entrepreneurs. Sentiment analysis involves detecting if it is positive, negative, or neutral from a text. We examine several prompts on ChatGPT-4, ChatGPT-4.0, and LeChat-Mistral and compare the results with FinBERT. Then, we examine the correlation between scores given by both tools with the type, size, and age of the company. The results have shown that scores given by FinBERT are mostly significant and positively correlated with sustainable variables. By sharing these results, we hope to stimulate future research and advances in the field of financial services, particularly bank loans.

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Kim, Jihwan, Hui-Sang Kim, and Sun-Yong Choi. "Forecasting the S&P 500 Index Using Mathematical-Based Sentiment Analysis and Deep Learning Models: A FinBERT Transformer Model and LSTM." Axioms 12, no. 9 (2023): 835. http://dx.doi.org/10.3390/axioms12090835.

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Stock price prediction has been a subject of significant interest in the financial mathematics field. Recently, interest in natural language processing models has increased, and among them, transformer models, such as BERT and FinBERT, are attracting attention. This study uses a mathematical framework to investigate the effects of human sentiment on stock movements, especially in text data. In particular, FinBERT, a domain-specific language model based on BERT tailored for financial language, was employed for the sentiment analysis on the financial texts to extract sentiment information. In this study, we use “summary” text data extracted from The New York Times, representing concise summaries of news articles. Accordingly, we apply FinBERT to the summary text data to calculate sentiment scores. In addition, we employ the LSTM (Long short-term memory) methodology, one of the machine learning models, for stock price prediction using sentiment scores. Furthermore, the LSTM model was trained by stock price data and the estimated sentiment scores. We compared the predictive power of LSTM models with and without sentiment analysis based on error measures such as MSE, RMSE, and MAE. The empirical results demonstrated that including sentiment scores through the LSTM model led to improved prediction accuracy for all three measures. These findings indicate the significance of incorporating news sentiment into stock price predictions, shedding light on the potential impact of psychological factors on financial markets. By using the FinBERT transformer model, this study aimed to investigate the interplay between sentiment and stock price predictions, contributing to a deeper understanding of mathematical-based sentiment analysis in finance and its role in enhancing forecasting in financial mathematics. Furthermore, we show that using summary data instead of entire news articles is a useful strategy for mathematical-based sentiment analysis.

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Books on the topic "Finbert"

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Berastsenʹ, Svi︠a︡tlana. Mikhail Finberh: Maėstra : z︠h︡ytstsi︠o︡vai︠a︡ rapsodyi︠a︡. Mastatskai︠a︡ litaratura, 2007.

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Walsh, J. C. Farranferris: The heritage of St Finbarr. [the author], 1987.

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Pádraig, Ó Riain, ed. Beatha Bharra: Saint Finbarr of Cork : the complete life. Irish Texts Society, 1994.

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Cork, University College, ed. Where Finbarr played: A concise illustrated history of sport in University College Cork, 1911-2011. Office of the Vice President for the Student Experience, University College Cork, 2011.

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Prättälä, Ritva. Finbalt health monitor: Feasibility of a collaborative system for monitoring health behavior in Finland and the Baltic countries. National Public Health Institute, 1999.

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Baulch, Alan. Finbark Contract. Independently Published, 2020.

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Enright, Anne, Hugo Hamilton, Roddy Doyle, et al. Finbars Hotel. Fischer (Tb.), Frankfurt, 2000.

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Furey, Finbar. Finbar Furey - Memoir. O'Brien Press, Limited, The, 2020.

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Binchy, Maeve. Ladies Night in Finbars Hotel. Krüger, Frankfurt, 2001.

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Binchy, Maeve, and Emma Donoghue. Ladies Night in Finbars Hotel. Fischer (Tb.), Frankfurt, 2003.

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Book chapters on the topic "Finbert"

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Fan, Shijia, Xu Chen, and Xu-an Wang. "Stock Price Prediction Based on FinBERT-LSTM Model." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70011-8_4.

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Bullen, J. B. "A.J. Finberg, ‘Art and Artists’." In Post-Impressionists in England. Routledge, 2024. http://dx.doi.org/10.4324/9781032699707-26.

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Prättälä, Ritva, Ville Helasoja, Anu Kasmel, Jurate Klumbiene, and Iveta Pudule. "Finbalt Health Monitor." In Global Behavioral Risk Factor Surveillance. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0071-1_7.

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Nahin, Paul J. "Finbarr Holland Has the Last Technical Word." In The Probability Integral. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38416-5_14.

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O’Donnell, William H. "Preface (1924) to Jean Marie Matthias Philippe Auguste Count de Villiers de l’Isle-Adam, Axel, tr. H. P. R. Finberg (1925)." In Prefaces and Introductions. Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-06236-2_24.

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Den Yeoh, Eik, Tinfah Chung, and Yuyang Wang. "Metaverse in Investment Using Sentiment Analysis and Machine Learning." In Strategies and Opportunities for Technology in the Metaverse World. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-5732-0.ch006.

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At the end of 2019, individuals' outdoor activities were restricted due to the emergence of COVID-19. As a result of this phenomenon, interest in online activities and interaction in the metaverse environment has increased. Online games have exploded in popularity with the young generation in Metaverse where they can earn money through the platforms. Thus, it is desirable to investigate emerging technology and analyse how to invest using techniques, such as sentiment analysis and machine learning (ML), to predict crypto trends. This study analysed time series data for crypto price and text, where information like news, articles, and feedback from social media can use the input to generate the sentiment score to understand the crypto trends. FinBERT is a sentiment model that was used for this study to generate the result. The AI investing framework is built to incorporate both sentiment analysis technique and predictive model for this chapter, to address the research questions and enable one to make more informed decisions.

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Housman, A. E. "To H. P. R. Finberg." In The Letters of A. E. Housman, edited by Archie Burnett. Oxford University Press, 2007. http://dx.doi.org/10.1093/oseo/instance.00293101.

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Housman, A. E. "To H. P. R. Finberg." In The Letters of A. E. Housman, edited by Archie Burnett. Oxford University Press, 2007. http://dx.doi.org/10.1093/oseo/instance.00293196.

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Housman, A. E. "To H. P. R. Finberg." In The Letters of A. E. Housman, edited by Archie Burnett. Oxford University Press, 2007. http://dx.doi.org/10.1093/oseo/instance.00293102.

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Housman, A. E. "To H. P. R. Finberg." In The Letters of A. E. Housman, edited by Archie Burnett. Oxford University Press, 2007. http://dx.doi.org/10.1093/oseo/instance.00293143.

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Conference papers on the topic "Finbert"

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Huang, Yuesheng, Renyi Lin, Jiawen Li, et al. "A FinBERT Framework for Sentiment Analysis of Chinese Financial News." In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2024. http://dx.doi.org/10.1109/isctis63324.2024.10699096.

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Dian, Zhang. "A Deep Learning Approach to Financial Text Similarity Using FinBERT." In 2023 International Conference on Intelligent Computing, Communication & Convergence (ICI3C). IEEE, 2023. http://dx.doi.org/10.1109/ici3c60830.2023.00058.

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Baghavathi Priya, S., Madhav Kumar, Nitheesh Prakash J D, and Krithika N. "Advanced Financial Sentiment Analysis Using FinBERT to Explore Sentiment Dynamics." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915080.

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Shen, Yanxin, and Pulin Kirin Zhang. "Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796670.

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Mahendran, Manish Barath, Aswin Kumar Gokul, Poornima Lakshmi, and S. Pavithra. "Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914764.

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Jeet, Mahir Ayaan Begh, Rakei Matiul Haque, Md Aminul Islam Sayem, Asif Arman, and Rashedur M. Rahman. "Using Deep Learning Models and FinBERT to Predict the Stock Price of Top Banks in the Dhaka Stock Exchange." In 2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS). IEEE, 2024. https://doi.org/10.1109/icis61260.2024.10778363.

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Liu, Zhuang, Degen Huang, Kaiyu Huang, Zhuang Li, and Jun Zhao. "FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/622.

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There is growing interest in the tasks of financial text mining. Over the past few years, the progress of Natural Language Processing (NLP) based on deep learning advanced rapidly. Significant progress has been made with deep learning showing promising results on financial text mining models. However, as NLP models require large amounts of labeled training data, applying deep learning to financial text mining is often unsuccessful due to the lack of labeled training data in financial fields. To address this issue, we present FinBERT (BERT for Financial Text Mining) that is a domain specific language model pre-trained on large-scale financial corpora. In FinBERT, different from BERT, we construct six pre-training tasks covering more knowledge, simultaneously trained on general corpora and financial domain corpora, which can enable FinBERT model better to capture language knowledge and semantic information. The results show that our FinBERT outperforms all current state-of-the-art models. Extensive experimental results demonstrate the effectiveness and robustness of FinBERT. The source code and pre-trained models of FinBERT are available online.

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Gössi, Sandro, Ziwei Chen, Wonseong Kim, Bernhard Bermeitinger, and Siegfried Handschuh. "FinBERT-FOMC: Fine-Tuned FinBERT Model with Sentiment Focus Method for Enhancing Sentiment Analysis of FOMC Minutes." In ICAIF '23: 4th ACM International Conference on AI in Finance. ACM, 2023. http://dx.doi.org/10.1145/3604237.3626843.

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Huang, Changjiang, Zhiyuan Chen, and Waleed Mahmoud Soliman. "Stock Price Prediction with FinBERT and RNN." In ICACS 2023: the 7th International Conference on Algorithms, Computing and Systems. ACM, 2023. http://dx.doi.org/10.1145/3631908.3631919.

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Alias, Mohd Shafiq, Muhamad Haziq Fuad, Xavier Leong Foo Hoong, and Edward Goh Yoon Hin. "Financial Text Categorisation with FinBERT on Key Audit Matters." In 2023 IEEE Symposium on Computers & Informatics (ISCI). IEEE, 2023. http://dx.doi.org/10.1109/isci58771.2023.10391878.

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