Short-term cryptocurrency price forecasting based on news headline analysis - Dikovitsky, Vladimir (Frontiers in Blockchain, 2025)
IntroductionThis article presents a method for short-term cryptocurrency price forecasting utilizing news headlines.MethodsThe study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations.ResultsThe proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model.DiscussionThe model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.