Stock Movement Prediction from Tweets and Historical Prices
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.
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Results from the Paper
Ranked #2 on
Stock Market Prediction
on stocknet
(using extra training data)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Stock Market Prediction | Astock | StockNet | Accuray | 46.72 | # 17 | ||
| F1-score | 44.44 | # 17 | |||||
| Recall | 46.68 | # 17 | |||||
| Precision | 47.65 | # 17 | |||||
| Stock Market Prediction | stocknet | StockNet | F1 | 0.575 | # 2 |
