Financial Time Series Forecasting Enriched with Textual Information

The ability to extract knowledge and forecast stock trends is crucial to mitigate investors' risks and uncertainties in the market. The stock trend is affected by non-linearity, complexity, noise, and especially the surrounding news. External factors such as daily news became one of the investors' primary resources for buying or selling assets. However, this kind of information appears very fast. There are thousands of news generated by different web sources, taking a long time to analyze them, causing significant losses for investors due to late decisions. Although recent contextual language models have transformed the area of natural language processing, models to make predictions using news that influence stock values still face barriers such as unlabeled data and class imbalance. This paper proposes a hybrid methodology that enriches the time series forecasting considering textual knowledge extracted from sites without a widely annotated corpus. We show that the proposed method can improve forecasting using an empirical evaluation of Bitcoin prices prediction.