GO BEYOND SENTIMENT: STOCK PREDICTION ENHANCED WITH FINANCIAL NEWS

Nowadays, large language models are among the most impactful natural language processing techniques and have gained popularity in various applications, such as machine translation, language understanding, etc. These language models can be fine-tuned on domain-specific datasets to fit into the corresponding applications. In this paper, we will study the effect of financial news articles’ sentiments and text embeddings on predicting stock returns. Our in-house developed deep learning framework is able to model interactions between different input features and facilitates the comparison of feature combinations.