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.

Highlights

  • Stock movement prediction has long attracted both investors and researchers (Frankel, 1995; Edwards et al, 2007; Bollen et al, 2011; Hu et al, 2018)

  • Motivated by Variational Auto-Encoders (VAEs; Kingma and Welling, 2013; Rezende et al, 2014), we propose a novel decoder with a variational architecture and derive a recurrent variational lower bound for end-to-end training (Section 5.2)

  • Our experiments show that StockNet achieves state-of-the-art performance by incorporating both data from Twitter and historical stock price listings

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Summary

Introduction

Stock movement prediction has long attracted both investors and researchers (Frankel, 1995; Edwards et al, 2007; Bollen et al, 2011; Hu et al, 2018). Stock movement prediction is widely considered difficult due to the high stochasticity of the market: stock prices are largely driven by new information, resulting in a random-walk pattern (Malkiel, 1999). Generative models have the natural advantage in depicting the generative process from market information to stock signals and introducing randomness. These models underrepresent chaotic social texts with bag-of-words and employ simple discrete latent variables. StockNet, a deep generative model for stock movement prediction. To the best of our knowledge, StockNet is the first deep generative model for stock movement prediction. Our experiments show that StockNet achieves state-of-the-art performance by incorporating both data from Twitter and historical stock price listings

Problem Formulation
Data Collection
Model Overview
Market Information Encoder
Variational Movement Decoder
Attentive Temporal Auxiliary
Training Setup
Evaluation Metrics
Baselines and Proposed Models
Results
Effects of Temporal Auxiliary
Conclusion
Full Text
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