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Stock volatility prediction is an important task in the financial industry.
A framework for understanding unintended consequences of machine learning
Harini Suresh and John V Guttag. 2019 · 1901
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Finbert: Financial sentiment analysis with pre-trained language models
Dogu Araci. 2019 · 1908
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2019 · 1910
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Speak and unspeak with praat
Paul Boersma and Vincent Van Heuven. 2001 · 2001
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Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility
Bernard Dumas, Alexander Kurshev, and Raman Uppal. 2009 · 2009
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Predicting risk from financial reports with regression
Shimon Kogan, Dimitry Levin, Bryan R Routledge, Jacob S Sagi, and Noah A Smith. 2009 · 2009
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
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Skirting the issues: Experimental evidence of gender bias in ipo prospectus evaluations
Lyda Bigelow, Leif Lundmark, Judi McLean Parks, and Robert Wuebker. 2014 · 2014
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Quantitative and descriptive comparison of four acoustic analysis systems: Vowel measurements
Carlyn Burris, Houri K Vorperian, Marios Fourakis, Ray D Kent, and Daniel M Bolt. 2014 · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Volatility forecast using hybrid neural network models
Werner Kristjanpoller, Anton Fadic, and Marcel C Minutolo. 2014 · 2014
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Do sophisticated investors interpret earnings conference call tone differently than investors at large? evidence from short sales
Benjamin M Blau, Jared R DeLisle, and S McKay Price. 2015 · 2015
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Learning with a strong adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvári. 2015 · 2015
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Sentiment analysis on social media for stock movement prediction
Thien Hai Nguyen, Kiyoaki Shirai, and Julien Velcin. 2015 · 2015
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Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016 · 2016
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The sound of (in) sincerity
Karyn Fish, Kathrin Rothermich, and Marc D Pell. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
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A gender bias in the acoustic-melodic features of charismatic speech?
Eszter Novák-Tót, Oliver Niebuhr, and Aoju Chen. 2017 · 2017
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Context-dependent sentiment analysis in user-generated videos
Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder, Amir Zadeh, and Louis-Philippe Morency. 2017 · 2017
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A prediction approach for stock market volatility based on time series data
Sheikh Mohammad Idrees, M Afshar Alam, and Parul Agarwal. 2019 · 2019
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What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues
Yu Qin and Yi Yang. 2019 · 2019
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Maec: A multimodal aligned earnings conference call dataset for financial risk prediction
Jiazheng Li, Linyi Yang, Barry Smyth, and Ruihai Dong. 2020 · 2020
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Voltage: Volatility forecasting via text audio fusion with graph convolution networks for earnings calls
Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, and Rajiv Shah. 2020 · 2020
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Is image encoding beneficial for deep learning in finance?
Dan Wang, Tianrui Wang, and Ionuţ Florescu. 2020 · 2020
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Navid Rekabsaz, Mihai Lupu, Artem Baklanov, Allan Hanbury, Alexander Dür, and Linda Anderson. 2017 · 2017
Cited alongside, same era.
On the robustness of the cvpr 2018 white-box adversarial example defenses
Anish Athalye and Nicholas Carlini. 2018 · 2018
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Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David Wagner. 2018 · 2018
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Chris Donahue, Julian McAuley, and Miller Puckette. 2018 · 2018
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Uri Shaham, Yutaro Yamada, and Sahand Negahban. 2018 · 2018
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Training augmentation with adversarial examples for robust speech recognition
Sining Sun, Ching-Feng Yeh, Mari Ostendorf, Mei-Yuh Hwang, and Lei Xie. 2018 · 2018
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Explainable text-driven neural network for stock prediction
Linyi Yang, Zheng Zhang, Su Xiong, Lirui Wei, James Ng, Lina Xu, and Ruihai Dong. 2018 · 2018
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Html: Hierarchical transformer-based multi-task learning for volatility prediction
Linyi Yang, Tin Lok James Ng, Barry Smyth, and Riuhai Dong. 2020 · 2020
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Fairness measures for machine learning in finance
Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz, and Muhammad Bilal Zafar. 2021 · 2021
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An empirical investigation of bias in the multimodal analysis of financial earnings calls
Ramit Sawhney, Arshiya Aggarwal, and Rajiv Shah. 2021a · 2021
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Bridge the gap between cv and nlp! a gradient-based textual adversarial attack framework
Lifan Yuan, Yichi Zhang, Yangyi Chen, and Wei Wei. 2021 · 2021
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Predicting stock market volatility based on textual sentiment: A nonlinear analysis
Weiguo Zhang, Xue Gong, Chao Wang, and Xin Ye. 2021 · 2021
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The role of gender in the aggressive questioning of ceos during earnings conference calls
Joseph Comprix, Kerstin Lopatta, and Sebastian A Tideman. 2022 · 2022
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Diverse audio captioning via adversarial training
Xinhao Mei, Xubo Liu, Jianyuan Sun, Mark D Plumbley, and Wenwu Wang. 2022 · 2022
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A word is worth a thousand dollars: Adversarial attack on tweets fools stock prediction
Yong Xie, Dakuo Wang, Pin-Yu Chen, Jinjun Xiong, Sijia Liu, and Oluwasanmi Koyejo. 2022 · 2022
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Stock movement and volatility prediction from tweets, macroeconomic factors and historical prices
Shengkun Wang, YangXiao Bai, Taoran Ji, Kaiqun Fu, Linhan Wang, and Chang-Tien Lu. 2023b · 2023
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