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Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts.
Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?
Victor DeMiguel, Lorenzo Garlappi, and Raman Uppal. 2009 · 1953
Earlier work this paper cites.
Efficient capital markets: A review of theory and empirical work
Eugene F Fama. 1970 · 1970
Earlier work this paper cites.
Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1970–1979
Yumo Xu and Shay B Cohen. 2018 · 1979
Earlier work this paper cites.
Using news articles to predict stock price movements
Gyozo Gidofalvi and Charles Elkan. 2001 · 2001
Earlier work this paper cites.
The statistics of Sharpe ratios
Andrew W Lo. 2002 · 2002
Earlier work this paper cites.
Relationship between interest rate and stock price: empirical evidence from developed and developing countries
Md Mahmudul Alam and Gazi Uddin. 2009 · 2009
Earlier work this paper cites.
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
Robert P Schumaker and Hsinchun Chen. 2009 · 2009
Earlier work this paper cites.
Mining of massive datasets
Anand Rajaraman and Jeffrey David Ullman. 2011 · 2011
Earlier work this paper cites.
News or noise? The stock market reaction to different types of company-specific news events
Timm O Sprenger and Isabell M Welpe. 2011 · 2011
Earlier work this paper cites.
Density-based clustering based on hierarchical density estimates. In Pacific-Asia conference on knowledge discovery and data mining . Springer, 160–172
Ricardo JGB Campello, Davoud Moulavi, and Jörg Sander. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Using structured events to predict stock price movement: An empirical investigation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1415–1425
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Deep learning for event-driven stock prediction. In Twenty-fourth international joint conference on artificial intelligence
Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2015 · 2015
Earlier work this paper cites.
A five-factor asset pricing model
Eugene F Fama and Kenneth R French. 2015 · 2015
Earlier work this paper cites.
Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies . 1480–1489
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
Earlier work this paper cites.
Human-Centric Justification of Machine Learning Predictions.. In IJCAI , Vol. 2017. 1461–1467
Or Biran and Kathleen R McKeown. 2017 · 2017
Earlier work this paper cites.
A dual-stage attention-based recurrent neural network for time series prediction
Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Enhancing stock movement prediction with adversarial training
Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2018 · 2018
Earlier work this paper cites.
Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the eleventh ACM international conference on web search and data mining . 261–269
Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, and Tie-Yan Liu. 2018 · 2018
Earlier work this paper cites.
Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville. 2018 · 2018
Earlier work this paper cites.
Knowledge-driven stock trend prediction and explanation via temporal convolutional network. In Companion Proceedings of The 2019 World Wide Web Conference . 678–685
Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z Pan, and Huajun Chen. 2019 · 2019
Earlier work this paper cites.
Temporal relational ranking for stock prediction
Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, and Rosalind Picard. 2019 · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.
Sex matters: Gender bias in the mutual fund industry
Alexandra Niessen-Ruenzi and Stefan Ruenzi. 2019 · 2019
Cited alongside, same era.
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Davide Chicco and Giuseppe Jurman. 2020 · 2020
ChatGPT Informed Graph Neural Network for Stock Movement Prediction
Zihan Chen, Lei Nico Zheng, Cheng Lu, Jialu Yuan, and Di Zhu. 2023 · 2023
Later among the works it cites.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
Later among the works it cites.
Bias and fairness in large language models: A survey
Isabel O Gallegos, Ryan A Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K Ahmed. 2023 · 2023
Later among the works it cites.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
Later among the works it cites.
Can ChatGPT Decipher Fedspeak?
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Cited alongside, same era.
Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 8415–8426
Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Shah. 2020 · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
Cited alongside, same era.
Html: Hierarchical transformer-based multi-task learning for volatility prediction. In Proceedings of The Web Conference 2020 . 441–451
Linyi Yang, Tin Lok James Ng, Barry Smyth, and Riuhai Dong. 2020 · 2020
Cited alongside, same era.
Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting
Salvatore M Carta, Sergio Consoli, Luca Piras, Alessandro Sebastian Podda, and Diego Reforgiato Recupero. 2021 · 2021
Cited alongside, same era.
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
Davide Chicco, Niklas Tötsch, and Giuseppe Jurman. 2021 · 2021
Cited alongside, same era.
Leveraging multiple relations for fashion trend forecasting based on social media
Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, and Tat-Seng Chua. 2021 · 2021
Cited alongside, same era.
Hybrid learning to rank for financial event ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 233–243
Fuli Feng, Moxin Li, Cheng Luo, Ritchie Ng, and Tat-Seng Chua. 2021 · 2021
Cited alongside, same era.
Anne Lundgaard Hansen and Sophia Kazinnik. 2023 · 2023
Later among the works it cites.
In-group bias in financial markets
Sima Jannati, Alok Kumar, Alexandra Niessen-Ruenzi, and Justin Wolfers. 2023 · 2023
Later among the works it cites.
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction. In Proceedings of the 32nd ACM International Conference on Information & Knowledge Management
Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi. 2023 · 2023
Later among the works it cites.
Chain of hindsight aligns language models with feedback
Hao Liu, Carmelo Sferrazza, and Pieter Abbeel. 2023 · 2023
Later among the works it cites.
Can chatgpt forecast stock price movements? return predictability and large language models
Alejandro Lopez-Lira and Yuehua Tang. 2023 · 2023
Later among the works it cites.
Structured, Complex and Time-complete Temporal Event Forecasting
Yunshan Ma, Chenchen Ye, Zijian Wu, Xiang Wang, Yixin Cao, Liang Pang, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Later among the works it cites.
Summarization is (Almost) Dead
Xiao Pu, Mingqi Gao, and Xiaojun Wan. 2023 · 2023
Later among the works it cites.
Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
Later among the works it cites.
Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023 · 2023
Later among the works it cites.
NExT-GPT: Any-to-Any Multimodal LLM
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2023a · 2023
Later among the works it cites.
Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023b · 2023
Later among the works it cites.
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
Qianqian Xie, Weiguang Han, Xiao Zhang, Yanzhao Lai, Min Peng, Alejandro Lopez-Lira, and Jimin Huang. 2023 · 2023
Later among the works it cites.
FinGPT: Open-Source Financial Large Language Models
Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang. 2023b · 2023
Later among the works it cites.
Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023a · 2023
Later among the works it cites.
Retroformer: Retrospective large language agents with policy gradient optimization
Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, et al · 2023
Later among the works it cites.
In-context instruction learning
Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, and Minjoon Seo. 2023 · 2023
Later among the works it cites.
Temporal Data Meets LLM–Explainable Financial Time Series Forecasting
Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. 2023 · 2023
Later among the works it cites.
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. 2023 · 2023
Later among the works it cites.
Towards llm-based fact verification on news claims with a hierarchical step-by-step prompting method
Xuan Zhang and Wei Gao. 2023 · 2023
Later among the works it cites.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Later among the works it cites.