Do we Benefit from the Categorization of the News Flow in the Stock Price Prediction Problem?

Abstract

The power of machine learning is widely leveraged in the task of company stock price prediction. It is essential to incorporate historical stock prices and relevant external world information for constructing a more accurate predictive model. The sentiments of the financial news connected with the company can become such valuable knowledge. However, financial news has different topics, such as Macro, Markets, or Product news. The adoption of such categorization is usually out of scope in a market research. In this work, we aim to close this gap and explore the effect of capturing the news topic differentiation in the stock price prediction problem. Initially, we classify the financial news stream into 20 pre-defined topics with the pre-trained model. Then, we get sentiments and explore the topic of news group sentiment labeling. Moreover, we conduct the experiments with the several well-proved models for time series forecasting, including the Temporal Convolutional Network (TCN), the D-Linear, the Transformer, and the Temporal Fusion Transformer (TFT). In the results of our research, utilizing the information from separate topic groups contributes to a better performance of deep learning models compared to the approach when we consider all news sentiments without any division.