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In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Finbert: Financial sentiment analysis with pre-trained language models
Dogu Araci. 2019 · 1908
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Www’18 open challenge: Financial opinion mining and question answering
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018 · 1942
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Numclaim: Investor’s fine-grained claim detection
Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020 · 1976
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Analysts’ decisions as products of a multi-task environment
Jennifer Francis and Donna Philbrick. 1993 · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal. 1997 · 1997
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Conflict of interest and the credibility of underwriter analyst recommendations
Roni Michaely and Kent L Womack. 1999 · 1999
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Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien. 2009 · 2006
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Finbert: A pretrained language model for financial communications
Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020 · 2006
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Comparing the stock recommendation performance of investment banks and independent research firms
Brad M Barber, Reuven Lehavy, and Brett Trueman. 2007 · 2007
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Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts
Sanjeev Bhojraj, Paul Hribar, Marc Picconi, and John McInnis. 2009 · 2009
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Distant supervision for relation extraction without labeled data
Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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Do analysts herd? an analysis of recommendations and market reactions
Narasimhan Jegadeesh and Woojin Kim. 2010 · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2010 · 2010
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Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. 2020 · 2010
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A survey of crowdsourcing systems
Man-Ching Yuen, Irwin King, and Kwong-Sak Leung. 2011 · 2011
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Good debt or bad debt: Detecting semantic orientations in economic texts
Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Deep learning for financial sentiment analysis on finance news providers
Min-Yuh Day and Chia-Chou Lee. 2016 · 2016
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A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis
Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017 · 2017
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Investment banking relationships and analyst affiliation bias: The impact of the global settlement on sanctioned and non-sanctioned banks
Shane A Corwin, Stephannie A Larocque, and Mike A Stegemoller. 2017 · 2017
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
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Do managers walk the talk on environmental and social issues?
Sudheer Chava, Wendi Du, and Baridhi Malakar. 2021 · 2021
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Finqa: A dataset of numerical reasoning over financial data
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, et al. 2021 · 2021
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Human versus machine: A comparison of robo-analyst and traditional research analyst investment recommendations
Braiden Coleman, Kenneth J Merkley, and Joseph Pacelli. 2021 · 2021
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Persuading investors: A video-based study
Allen Hu and Song Ma. 2021 · 2021
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Green algorithms: Quantifying the carbon footprint of computation
Loïc Lannelongue, Jason Grealey, and Michael Inouye. 2021 · 2021
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Weakly-supervised neural text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han. 2018 · 2018
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Snuba: Automating weak supervision to label training data
Paroma Varma and Christopher Ré. 2018 · 2018
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Numeral attachment with auxiliary tasks
Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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How to talk when a machine is listening: Corporate disclosure in the age of ai
Sean Cao, Wei Jiang, Baozhong Yang, and Alan L Zhang. 2020 · 2020
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Measuring corporate culture using machine learning
Kai Li, Feng Mai, Rui Shen, and Xinyan Yan. 2021 · 2021
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skweak: Weak supervision made easy for nlp
Pierre Lison, Jeremy Barnes, and Aliaksandr Hubin. 2021 · 2021
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Multimodal machine learning for credit modeling
Cuong V Nguyen, Sanjiv R Das, John He, Shenghua Yue, Vinay Hanumaiah, Xavier Ragot, and Li Zhang. 2021 · 2021
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Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. 2021 · 2021
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Analysts’ forecast optimism: The effects of managers’ incentives on analysts’ forecasts
Anna Bergman Brown, Guoyu Lin, and Aner Zhou. 2022 · 2022
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Measuring firm-level inflation exposure: A deep learning approach
Sudheer Chava, Wendi Du, Agam Shah, and Linghang Zeng. 2022 · 2022
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When FLUE meets FLANG: Benchmarks and large pretrained language model for financial domain
Raj Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, and Diyi Yang. 2022 · 2022
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Actune: Uncertainty-based active self-training for active fine-tuning of pretrained language models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang. 2022 · 2022
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Prboost: Prompt-based rule discovery and boosting for interactive weakly-supervised learning
Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, and Chao Zhang. 2022 · 2022
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Falcon-40B: an open large language model with state-of-the-art performance
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023 · 2023
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Closed ai models make bad baselines
Anna Rogers, Niranjan Balasubramanian, Leon Derczynski, Jesse Dodge, Alexander Koller, Sasha Luccioni, Maarten Sap, Roy Schwartz, Noah A. Smith, and Emma Strubell. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Chatgpt in finance: Applications, challenges, and solutions
Muhammad Salar Khan and Hamza Umer. 2024 · 2024
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