From dictionaries to AI: a new era in sentiment analysis for financial stability

Article by John Fell, Sándor Gardó, Domenic Kellner, Benjamin Klaus, Jan Hannes Lang, Lukas Nagy, Pucho Vendrell, Marek Rusnák, Jonas Wendelborn and Stefan Wredenborg: “Financial stability communication is challenging because its task is not to forecast financial crises, let alone predict their precise timing. Rather, it is to identify vulnerabilities and explain how the financial system is likely to fare should it be confronted with adverse shocks. Great care is needed in this endeavour, because the sentiment of financial stability communication can influence market perceptions and risk assessments, as well as broader economic and financial outcomes. Given the presence of this potential feedback loop, the task of financial stability communication at the ECB has long been guided by a broad concept of financial stability: the smooth allocation of financial resources, effective management of risk by financial institutions and the capacity of the financial system to absorb shocks. Using the messages conveyed in the ECB’s Financial Stability Review over two decades, this special feature compares dictionary-based, FinBERT and prompt-based AI approaches to extracting financial stability sentiment. It finds broad co-movement across methods, while the GPT-based filter isolates sentences that contain explicit risk assessments, capturing subtle shifts in tone and context that were previously difficult to quantify. Used carefully, such tools can support risk monitoring and drafting consistency over time, but they remain complementary to expert judgement, vulnerability analysis and stress testing, rather than substitutes for it. A deep-dive box in the special feature also shows how AI can be used to systematically extract information from financial news to create an indicator for the severity and probability of triggers (SPOT) for financial stability risks…(More)”.