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Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis.
Language models are few-shot learners
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Stock movement prediction from tweets and historical prices
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The adaptive markets hypothesis: Market efficiency from an evolutionary perspective
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TextRank: Bringing order into text
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Efficient market hypothesis and forecasting
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CollabRank: Towards a collaborative approach to single-document keyphrase extraction
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TopicRank: Graph-based topic ranking for keyphrase extraction
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Sentiment analysis on social media for stock movement prediction
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Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques
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pke: an open source python-based keyphrase extraction toolkit
Florian Boudin. 2016 · 2016
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Deep leaming for stock market prediction using technical indicators and financial news articles
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Group recommendation based on financial social network for robo-advisor
Jingming Xue, En Zhu, Qiang Liu, and Jianping Yin. 2018 · 2018
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Temporal relational ranking for stock prediction
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua. 2019 · 2019
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Literature review: Machine learning techniques applied to financial market prediction
Bruno Miranda Henrique, Vinicius Amorim Sobreiro, and Herbert Kimura. 2019 · 2019
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Yake! keyword extraction from single documents using multiple local features
Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Jorge, Célia Nunes, and Adam Jatowt. 2020 · 2020
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Keybert: Minimal keyword extraction with bert
Maarten Grootendorst. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Guided attention multimodal multitask financial forecasting with inter-company relationships and global and local news
Gary Ang and Ee-Peng Lim. 2022 · 2022
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Generated knowledge prompting for commonsense reasoning
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2022 · 2022
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Fengshenbang 1.0: Being the foundation of chinese cognitive intelligence
Junjie Wang, Yuxiang Zhang, Lin Zhang, Ping Yang, Xinyu Gao, Ziwei Wu, Xiaoqun Dong, Junqing He, Jianheng Zhuo, Qi Yang, Yongfeng Huang, Xiayu Li, Yan-Ze Wu, Junyu Lu, Xinyu Zhu, Weifeng Chen, Ting-Ting Han, Kunhao Pan, Rui Wang, Hao Wang, Xiaojun Wu, Zhong Zeng, Chong-An Chen, Ruyi Gan, and Jiaxing Zhang. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Questioning the ability of feature-based explanations to empower non-experts in robo-advised financial decision-making
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Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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Incorporating expert-based investment opinion signals in stock prediction: A deep learning framework
Heyuan Wang, Tengjiao Wang, and Yi Li. 2020 · 2020
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Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane
Pei-Yi Hao, Chien-Feng Kung, Chun-Yang Chang, and Jen-Bing Ou. 2021 · 2021
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Fingat: Financial graph attention networks for recommending top- k k k profitable stocks
Yi-Ling Hsu, Yu-Che Tsai, and Cheng-Te Li. 2021 · 2021
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Modeling the stock relation with graph network for overnight stock movement prediction
Wei Li, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu, and Qi Su. 2021 · 2021
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Sentiment correlation in financial news networks and associated market movements
Xingchen Wan, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, and Xiaowen Dong. 2021 · 2021
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Deeptrader: a deep reinforcement learning approach for risk-return balanced portfolio management with market conditions embedding
Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, and Lei Xu. 2021 · 2021
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Astrid Bertrand, James R Eagan, and Winston Maxwell. 2023 · 2023
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Large language models are zero-shot time series forecasters
Nate Gruver, Marc Anton Finzi, Shikai Qiu, and Andrew Gordon Wilson. 2023 · 2023
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PromptRank: Unsupervised keyphrase extraction using prompt
Aobo Kong, Shiwan Zhao, Hao Chen, Qicheng Li, Yong Qin, Ruiqi Sun, and Xiaoyan Bai. 2023 · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
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Causality-guided multi-memory interaction network for multivariate stock price movement prediction
Di Luo, Weiheng Liao, Shuqi Li, Xin Cheng, and Rui Yan. 2023 · 2023
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pke_zh: Python keyphrase extraction toolkit for chinese
Ming Xu. 2023 · 2023
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Promptcast: A new prompt-based learning paradigm for time series forecasting
Hao Xue and Flora D Salim. 2023 · 2023
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Harnessing LLMs for temporal data - a study on explainable financial time series forecasting
Xinli Yu, Zheng Chen, and Yanbin Lu. 2023 · 2023
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Instruct-fingpt: Financial sentiment analysis by instruction tuning of general-purpose large language models
Boyu Zhang, Hongyang Yang, and Xiao-Yang Liu. 2023 · 2023
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