Given historical stock prices and sparse tweets, how can we accurately predict stock price movement? Many market analysts strive to use a large amount of information for stock price prediction, and Twitter is one of the richest sources of information presenting real-time opinions of people. However, previous works that use tweet data in stock movement prediction have suffered from two limitations. First, the number of tweets is heavily biased towards only a few popular stocks, and most stocks have insufficient evidence for accurate price prediction. Second, many tweets provide noisy information irrelevant of actual price movement, and extracting reliable information from tweets is as challenging as predicting stock prices.In this paper, we propose SLOT (Self-supervised Learning of Tweets for Capturing Multi-level Price Trends), an accurate method for stock movement prediction. SLOT has two main ideas to address the limitations of previous tweet-based models. First, SLOT learns embedding vectors of stocks and tweets in the same semantic space through self-supervised learning. The embeddings allow us to use all available tweets to improve the prediction for even unpopular stocks, addressing the sparsity problem. Second, SLOT learns multi-level relationships between stocks from tweets, rather than using them as direct evidence for prediction, making it robust to the unreliability of tweets. Extensive experiments on real world datasets show that SLOT provides the state-of-the-art accuracy of stock movement prediction.

How can we efficiently correlate multiple stocks for accurate stock movement prediction? Stock movement prediction has received growing interest in data mining and machine learning communities due to its substantial impact on financial markets. One way to improve the prediction accuracy is to utilize the correlations between multiple stocks, getting a reliable evidence regardless of the random noises of individual prices. However, it has been challenging to acquire accurate correlations between stocks because of their asymmetric and dynamic nature which is also influenced by the global movement of a market. In this work, we propose DTML (Data-axis Transformer with Multi-Level contexts), a novel approach for stock movement prediction that learns the correlations between stocks in an end-to-end way. DTML makes asymmetric and dynamic correlations by a) learning temporal correlations within each stock, b) generating multi-level contexts based on a global market context, and c) utilizing a transformer encoder for learning inter-stock correlations. DTML achieves the state-of-the-art accuracy on six datasets collected from various stock markets from US, China, Japan, and UK, making up to 13.8%p higher profits than the best competitors and the annualized return of 44.4% on investment simulation.