Multi-source aggregated classification for stock price movement prediction

Abstract

Predicting stock price movements is a challenging task. Previous studies mostly used numerical features and news sentiments of target stocks to predict stock price movements. However, their semantics-based sentiment analysis is sub-optimal to represent real market sentiments. Moreover, only considering the information of target companies is insufficient because the stock prices of target companies can be affected by their related companies. Thus, we propose a novel Multi-source Aggregated Classification (MAC) method for stock price movement prediction. MAC incorporates the numerical features and market-driven news sentiments of target stocks, as well as the news sentiments of their related stocks. To better represent real market sentiments from the news, we pre-train an embedding feature generator by fitting the news to real stock price movements. Embeddings given by the pre-trained sentiment classifier can represent the sentiment information in vector space. Moreover, MAC introduces a graph convolutional network to capture the news effects of related companies on the target stock. Finally, MAC can predict stock price movements for the next trading day based on the aforementioned features. Extensive experiments prove that MAC outperforms state-of-the-art baselines in stock price movement prediction, Sharpe Ratio, and backtesting trading incomes.