Learning Sentimental and Financial Signals With Normalizing Flows for Stock Movement Prediction

Abstract

Stockmovement prediction using Tweets (text) and historical price signals (time series) remains a challenging task due to the complex, noisy and dynamic nature of the stock market. The key to improve prediction performance is effectively capturing the complementarity between market sentiment signal from Tweets and time-series signal from the stock price. In this paper, we contribute a new solutionStockNFby exploiting a deep generative model technique, Normalizing Flow (NF), to learn more flexible and expressive posterior distributions of latent variables of Tweets and price signals, which can largely ameliorate the bias inference problem in existing methods. We empirically evaluate the NF technique on a public stock movement prediction dataset and show that StockNF outperforms the state-of-the-art baselines.

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