Stock Movement Prediction from Tweets and Historical Prices
TL
TL;DR
This work presents a novel deep generative model jointly exploiting text and price signals for stock movement prediction and introduces recurrent, continuous latent variables for a better treatment of stochasticity and uses neural variational inference to address the intractable posterior inference.
Abstract
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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