Stock Movement Prediction from Tweets and Historical Prices
By Yumo Xu, Shay B. Cohen. Annual Meeting of the Association for Computational Linguistics, 2018
453 citations recorded by the source corpus.
Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/332dd824e82aa7330a79c7ede1bc2e0c52d756aa
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In Economics and Econometrics
In Electrical and Electronic Engineering
In Management Science and Operations Research
Citations in the corpus
- Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…Feng et al. (2019b); Kim et al. (2019); Sezer & Ozbayoglu (2018); Dingli & Fournier (2017); Sim et al. (2019); Song et al. (2019); Liu et al. (2019c); Xu & Cohen (2018); Eapen et al. (2019); Zhan et al. (2018); Huang et al. (2018); Fischer & Krauss (2018); Althelaya et al. (2018); Gao & Chai…”