Lecture by Researchers from Nicolaus Copernicus University in Toruń

We cordially invite you to a presentation of research results delivered by Prof. Piotr Fiszeder and Prof. Witold Orzeszko. Researchers from Nicolaus Copernicus University in Toruń will discuss the study “News sentiment analysis using ChatGPT for Bitcoin price dynamics”. The paper is also co-authored by Prof. Radosław Pietrzyk from Wroclaw University of Economics and Business.

The seminar, organized by the QFRG and DSLab research centres, will take place on 9 March at 18:30. All those interested are warmly invited to attend in room B002 at the Faculty of Economic Sciences or to join online. To receive the link for the online meeting, please contact us at: -|WV@jy?n%lmqu\r$gC5F3]#[xoGD+Ndw#y[SzkT{fL5B3z.

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Below, we provide the abstract of the presentation.

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This study investigates the use of ChatGPT as an automated tool for extracting and labeling Bitcoin-related news sentiment and examines how the resulting sentiment indicators affect Bitcoin returns and volatility. A large dataset of news headlines is processed via an API-based workflow, and the ChatGPT-derived sentiment indicators are subsequently incorporated as explanatory variables into selected statistical and machine learning models, including autoregressive (AR), heterogeneous autoregressive (HAR), Bayesian model averaging (BMA), least absolute shrinkage and selection operator (LASSO), and support vector regression (SVR). We find that while the sentiment indicators significantly improve in-sample estimation accuracy for returns and volatility, they do not lead to statistically significant gains in out-of-sample forecasting performance. This result suggests that ChatGPT-based sentiment measures primarily capture contemporaneous market-relevant information rather than persistent predictive signals, consistent with semi-strong market efficiency.